Authors: Robert P. Narcessian and Janet M. Leet
Robert P. Narcessian, EdM
St. Joseph’s Health and Regional Medical Center
Department of Orthopedics
703 Main Street
Paterson, NJ 07503
Robert P.Narcessian is a faculty member and research consultant in the Department of Orthopedics, and the primary investigator of the study
Janet M. Leet, President
508 S. Evanston Avenue
Arlington, IL 60004
Janet M. Leet is a coach and the co-investigator of the study at St. Joseph’s Regional Medical Center
Scientific Epistemology for Physical Education Fundamental Movement Skills Prerequisites
A scientific epistemology, using a systems thinking qualitative methodology for translating practice into theory, integrates mathematical and dynamical systems concepts with belief systems that are presented in this original research of unique prerequisites for fundamental movement skills (FMS) in physical education as illustrated with running. FMS prerequisites demonstrate that FMS are neither fundamental nor reliable screentests conducted on individuals by physical education teachers, coaches, and healthcare practitioners for performance readiness evaluations or injury risk assessments. FMS prerequisites identify and assess eliminating the hypothetical set of worst first moves, assess the integrity of their respective coordinative structures, and assess performers’ beliefs (i.e., preferred behaviors) with the objective to provide a new direction for researching injury risk and performance readiness. The researchers illustrate this new method with participants for FMS prerequisites in running and squatting to provide insight for the observer-performer interaction. A new observer-performer classification and non-epistemic modeling show what is known with self-discovery strategies that detect hidden skills at the observable level using four independent tasks. There were 297 participants in kindergarten through high school (213 females and 84 males; mean 14.5 years; range 5 to 17 years) and 21 participants from the community at large (15 females and 6 males; mean 31.4 years, range 12 to 94 years). A variety of running strategies of different degrees of configured complexity from which to run were self-selected and observed as preferred with and without practice or intervention. An idealized 2-joint planar multi-joint mechanism (MJM) was used to assess individual skill with respect to adding and removing constraints. Findings are presented for strategies, trends, and transitions of preferred behavior including observables that reveal hidden skills including a visual search of a hidden skill with world record Olympian sprint performances. FMS prerequisites are theorized for future study with an inverted U-model and a leading MJM hypothesis; and they provide the rudiments for injury risk assessments and performance readiness evaluations approaching optimal health biomechanically in the very early detection of flawed gross motor skill development before manifesting into the signs and symptoms of injury or poor performance.
Key words: dynamical systems, belief systems, fundamental movement skills, classification, running, physical education
The human body is comprised of about 50 trillion cells organized as complex biological systems (23) and their many subsystems that act to perform a vast variety of voluntary and involuntary functions with the fundamental purpose being to sustain the quality of healthy life. A major goal to achieve this basic objective is dependent upon fundamental movement skills (FMS). An essential problem to be solved is that children’s FMS development is a nonlinear evolutionary process that neither guarantees optimizing FMS nor reveals hidden skills underlying that process. For example, an ability to run does not prevent faulty running mechanics, which could lead to misuse, overuse, or abuse of the body reducing the quality of life physically, emotionally, and economically. In 2013, moving toward optimal health, Faust (8) argued “…we don’t necessarily need to know a cause in order to have a preventive effect” (p.553); and Khushf (18) said “… epistemology is worth the pursuit” (p.483) for such a state of wellbeing.
This paper translates practice into theory with a new scientific epistemology as an alternative to scientific inquiry. It presents a different approach to acquire knowledge (43) with FMS prerequisites that uses a qualitative methodology when lacking quantification (4). This includes systems thinking in physical education to reveal (i.e., know) hidden skills towards optimal health and enhanced performance through observation of the body and what it senses and learns (22). These FMS prerequisites, which are based on 2-joint systems called multi-joint mechanisms (MJM), transition from MJM into complex dynamical Fundamental Movement Skills (dFMS) that promote learning through the body’s senses as argued by the constructivism viewpoint (22), as well as, a sense of wellbeing as pursued for a better quality of life (8,18). MJM is a simple model that provides a scientific basis to investigate what the researchers know using dFMS about the value judgment of the observer (e.g.; physical education teachers, coaches, athletic trainers, physical therapists, sports medicine doctors) and the perception, motor action, and bodily senses of the performer (e.g., students, athletes, and the community at large). A novel Observer—Performer Classification (OPC) by the researchers is explained in Appendix A to classify groups and assign interventions for the observer–performer interaction value judgments. An epistemological view of decision-making relationship between beliefs and behavior (14) was personalized with the Skill Learning Chain (13) of sports coaching that organizes structured thinking and rationalized decisions by a coach with competing theories; namely, information processing and dynamical systems (1,13). A qualitative modeling strategy for systems thinking in biology education to attain systems learning entails the crucial step from empirically observable phenomena to a systems theoretical conceptualization of such phenomena (42).
Connecting epistemology and research methods
In this paper, the researchers construct FMS prerequisites as a new epistemic framework for a qualitative methodology (see Appendix A: Observer—Performer Classification) to classify, assess, teach, and research FMS with mathematical concepts (see Appendix A: The dynamical systems perspective). This methodology is illustrated with running or crouching down (i.e., bending down or squatting) to demonstrate these observed insights with highly skilled performances in Olympic sprinting (see Appendix A: The Visual Search) to foster wellbeing (e.g.; optimal health biomechanically) and enhanced performance (e.g., optimize momentum). In this epistemic framework, the researchers neither support nor reject any one specific motor theory but rather question that FMS are neither fundamental (2) nor a reliable and valid screening tool (30,44). The researchers’ observations that will be discussed later (see Tasks) were not done to support a theory but instead to challenge systems thinking in physical education. These observations are used for a different qualitative methodology (4) to generate hypotheses (see the inverted U-model and leading MJM hypothesis discussed later).
Connecting epistemology and practice
Ab initium (from the beginning) is a fundamental question concerning seeking the most favorable initial configuration for a skilled motion from which to move optimally to a target with an ultimate goal to predict performance readiness evaluations and injury risk assessments using FMS prerequisites to identify the worst first move. The initial insight of a step-back strategy (19), which was used by 95% of the runners executing a sprint-start from a stance-posture, was thought to be the worst first move. Stepping back for the sprint-start produced an effective horizontal force by repositioning the center of mass of the body in front of the feet (11). In the initial study, the researchers did not observe a step-back strategy for the stance-posture of the run-start with performers in grades 7-12. Rather, the majority used step-forward and very few used the 2-point crouch-down starting from a simple stance-posture, lowering slightly, within its narrow support base of 2 feet together before running instead of a complex 3-point crouch-down start (36,39). The researchers suspected that step-back (i.e., a motor action) involves hidden skills that are self-discovered perceptively derived from bodily senses among elementary school children. Apparently, young children are not well developed with the 2-point crouch-down strategy from a stance-posture. Lacking quantification (e.g., perception, bodily senses), then a qualitative methodology (4) makes possible a deeper understanding to develop the what questions for these hidden skills and to answer why and how knowledge can be created, acquired, and communicated with an epistemological perspective both practically and theoretically (4,43).
Participants. From a sample of 318 participants, a series of observational qualitative studies involving these four tasks were approved by the institutional review board of the St. Joseph’s Regional Medical Center. Adult and parental written consents, child written assents (ages 7-17), and child verbal assents (ages 6 or younger) were obtained prior to their participation with the right to refuse involvement at any time. For the purpose of this paper, the participating volunteers are referred to as “performers”. These performers in the study consisted of an Illinois elementary school with grades K-5 (38 females and 35 males; mean 8.2 years; range 5 to 10 years), an Illinois girls running camp of 144 females and two New Jersey high schools of 31 females and 49 males comprising grades 7-12 (175 females and 49 males; mean 15.0 years; range 12 to 17 years), and a Chicago area community-at-large X-group (15 females and 6 males; mean 31.4 years, range 12 to 94 years).
Procedures. All performers (N=318) were shown a simple run-start configuration of standing at attention with 2 feet together and from this stance-posture, they were asked to run (not sprint). Three movement outcomes (i.e., movement functions that each performer seeks an initial configuration from which to run) were seen in the sagittal plane and recorded as: 1) step-forward, 2) 2-point crouch-down (i.e., lowering slightly in a narrow support base of 2 feet together before running), or 3) step-back. An observer-performer agreement was used for outcomes like a step-forward or step-back. Performers 12 years and older were able to acknowledge their preferred behavior was in agreement with the strategy for the run-start that the observer saw within a few attempts without practice or intervention. Performers in grades K-5, observations using the game Simon Says & Shows were made by one author and independently confirmed by the other author to observe self-selected strategies as their preferred behavior and to show configurations in a playful setting among a set of irrelevant movement tasks, which disguised those tasks of interest. Task observations were recorded as a simple tally. Proportions were calculated to quantify behavioral preferences and fifth order polynomials were generated in Microsoft Excel (2003) charts to illustrate trends across age groups. All relevant data are contained in this manuscript; and preferred behaviors can be obtained as percentages of the sample populations in the paper.
Run-start strategies (Figure 1) were assessed as OPC-I (see Appendix A: The Observer—Performer Classification) for a 2-point crouch-down because it was assumed this strategy required a higher level of skill as a prerequisite for a run (i.e., 1-point crouch-down at footstrike); but, step-forward and step-back were assigned OPC-II and assumed not to optimize performance. Grades K-5 (N=73) performer preferences were step-forward, step-back, and 2-point crouch-down at 63%, 35.6%, and 1.4%, respectively; and where 2-point crouch-down preferences averaged 1.4% for grades K-5 (N=73), 5.8% for grades 7-12 (N=224), and 4.8% for the X-group (N=21). Performers (N=245) in grades 7-12 and the X-group preferred step-forward at 94.3% and 2-point crouch-down at 5.7%. All performers (N=318) averaged 87.1%, 8.2%, and 4.7% for step-forward, step-back, and 2-point crouch-down, respectively.
Figure 1. For kindergarteners and second graders, step-forward (triangle) was seen preferred with a decline during third grade. An upward trend dominated thereafter. A transition was seen from step forward to step-back (black square) with first graders. A reversal from step-back to step-forward occurred in second grade, which was equally preferred by third graders. The step-back strategy followed a decreasing trend from third grade that flowed into another transition of a preferred 2-point crouch-down (circle) strategy with very few in grades 7-12 and the X-group.
Step-back was seen with run-start among elementary school children. Apparently, elementary school children, especially first graders, solve an initial value problem (IVP: see Appendix A: The dynamical systems perspective) through self-discovery steady states with step-back, which increase the base of support and rely on the initial conditions of motor system estimates for the preplanned inverse kinematics, including the predicted forward dynamics (45) in order for the inverse dynamics to produce their preferred behavior in the step-back strategy for run-start. From a dynamical systems perspective, an initial configuration from which to run occurs when the initial conditions of an unsteady state with a narrow support base of an existing behavior (e.g., stance-posture) are replaced by those of a more preferable and reliably stable behavior, which increases the base of support (e.g., step-forward or step-back) and can be trusted by the performer. Yet, observation alone neither explains the initial conditions for these run-start strategies nor the transitions that occur over time where so few 2-point crouch-down strategies were seen. The researchers are left with a dilemma as to which of these strategies is best.
Running can assume many different starting configurations as force plate studies have revealed that among the standing sprint-starts, step-back is better than step-forward by repositioning the center of mass of the body in front of the feet to generate a horizontal force (11); and a 3-point crouch-down is better than step-back by generating the greatest increase in a backward shift of the center of pressure to produce the most horizontal acceleration of the center of mass of the body (39). Combining force plate with kinematic analysis (36) showed that a wide base of support in a 3-point crouch-down resulted in the shortest duration of start time of various positions influencing sprint performance. Once again, observation fails to explain why so few performers self-discover the 2-point crouch-down strategy (4.7%) compared with those performers who preferred step-forward (87.1%) for the run-start.
These observations of falling within or without the initial support base depict an essential question to be solved with FMS prerequisites during child development, which is a nonlinear evolutionary process that does not guarantee reliable FMS nor reveal acquired hidden skills. This ability to run with different strategies implies low complexity and great difficulty to predict the multifactorial movement patterns that are involved with a vast number of degrees of freedom compensating for error and risking injury. Moreover, the paucity to self-discover the 2-point crouch-down strategy suggests a hidden skill where IVP solutions for dynamic systems theory lack sufficient input information of initial conditions to perform an act like 2-point crouch-down with a narrow base from a stance-posture when more stable 3-D bases exist (e.g., a wide support base of the 3-point crouch-down start, step-forward, or step-back). This situation might support information processing theory (1) where creative wording and appropriate feedback provides the performer the required information (46).
From the dynamical systems perspective, the run-start configuration is modeled as an idealized 1-D inverted pendulum, which is a straight line of self-constrained joints and segments of the body that starts as a single-joint system with a potential energy and a narrow base of support in a state of stability that can be researched as an IVP to assess the motion about the ankle joint when the run-start transitions from an idealized 1-D single-joint system into the 3-D multi-joint system of a run. Each performer attempts to solve an IVP for a single-joint system without knowing the initial conditions of the segmental velocities and the skill of temporary assemblages utilizing more segments and joints that transition from the single-joint system (i.e., removing constraints and increasing degrees of freedom) into a multi-joint system to find a steady state among a set of targeted initial configurations that can be trusted solutions to run well.
From an epistemological perspective, Task 1 lets us know that very few performers, as illustrated in Figure 1, preferred the 2-point crouch down (i.e., utilize gravity) as an OPC-I to find an initial configuration within the base of support from which to run. Why this occurs may be explained where a lack of vital sensory input has yet to exist for a meaningful perceptual understanding that could change the belief system of the performer. Furthermore, the researchers know that falling to an initial configuration within a support base to start to run requires the skill of a multi-joint system different from those configuration strategies outside the support base, which seeks to answer the question how this needs to be done in a chaotic state of too many degrees of freedom. It appears that potential learning can occur by investigating the 2-point crouch-down with a narrow base of support, which requires removing constraints and adding degrees of freedom, that are not easily self-discovered compared to falling outside the support base as seen with either step-forward or step-back. Task 2 provides insight about falling within a narrow support base.
Participants. Grades K-5 (N=57) and the X-group (N=21) were the only performers experiencing this task for the first time. K-5 students (N=16) were absent. The 144 female runners and 80 NJ students (31 females and 49 males) were excluded due to previous experience.
Procedures. Although starting in the stance-posture of Task 1, the X-group performers were only asked to match the elbows-to-knees boundary configuration (Figure 2A) and the hands-to-knees target configuration (Figure 2B). For performers in grades K-5, observations using the game Simon Says & Shows were made by one author and independently confirmed by the other author. Results were recorded as OPC-I only on their ability to match the boundary configuration or a target configuration; yet, the BMM helps assess the motion without a measurement device.
Figure 2. MJM configurations: (A) boundary and (B) target; (C) environmental constraint, (D) BMM and fulcrum, and (E) geometric model of the planar 2-joint system. Adult, parental, and child written consents were obtained for permission to use these images.
Performers were provided the boundary value problem (BVP: see Appendix A: The dynamical systems perspective) boundary conditions for the robotic MJM with show & tell instructions to keep feet and knees together, keep a straight back with arms kept bent for the boundary configuration and straight for the target configuration, and sustain lower leg immobility while balancing and crouching down. Constraints were applied to eliminate the worst first moves. For example, verbal constraints to keep knees together or some environmental constraints like a chair (Figure 2C) were used to see if the performer would behave more like the inanimate robot and to prevent a suspected worst first move (e.g., ankle-dorsi-flexion). The BBM of whole foot ground contact and lower leg perpendicularity to the ground, with its fulcrum point (Figure 2D) ideally located at the anterior aspect of the calcaneus through the talus bone, adjusts with minimal functional variability to maintain the moment arm (r1) of the femur mass center (m1) and the moment arm (r2) of the segmental quasi-rigid mass center (m2) of head-spine-pelvis-upper extremities where m1r1 = m2r2 approximates the linear trajectory of a point particle system’s center of mass representing the idealized 2-joint planar system. This FMS prerequisite assesses squatting and balance capability to reconfigure body segments in the 2-point crouch-down strategy optimally guided by the line of gravity from stance-posture to the boundary configuration as illustrated with a geometric model in equilibrium (Figure 2E).
MJM constrained was OPC-I at 28.1% and 33.3% at boundary, and 100% and 95.2% at target, for K-5 students (N=57) and the X-group (N=21), respectively. Constrained data were attained with knowledgeable application of the constraints. MJM unconstrained for X-group was OPC-I at 14.3% boundary and 81% target. K-5 students failed MJM unconstrained using the Simon Says & Shows game. Some X-group boundary errors (OPC-II) are seen in Figure 3.
Figure 3. Sample errors: (A) trunk alignment, (B) pelvic tilt, (C) ankle dorsi-flexion (D) hip-knee lacks one-to-one correspondence. Performers thought that they executed correctly (OPC-II). Adult, parental, and child written consents were obtained for permission to use these images.
From the dynamical systems perspective, starting with stance-posture of Task-1, MJM is modeled as an idealized 2-joint, 2-D system that represents the most basic skill for the hip and knee to crouch-down and to maintain equilibrium as a predictable motor action where retaining fixed segments (i.e., adding constraints and reducing degrees of freedom), the movement of femur segment to a new position requires the fixed segments of the quasi-rigid head-spine-pelvis-upper extremities to solve a BVP of a known boundary configuration via highly predictable positions of low complexity, which are reliable scientifically and valid mathematically.
The stance-posture of Task-I is an unskilled configuration of a single-joint system; whereas, MJM represents the most basic skilled configuration of a 2-joint system. Both systems balance about the ankle joint, where the BMM was used with MJM to construct what to observe as the worst first move (i.e., ankle motion) with and without constraints. MJM constrained was helpful for performers to assume the target configuration; however, observation alone doesn’t explain why most performers had difficulty applying constraints and failing to acknowledge (i.e., OPC-II) an inability to match the boundary configuration.
MJM offers a means to investigate not only control with sensory equilibrium with properly applied constraints, but also the 2-point crouch-down coordinative role of 2-joint systems of the hip-knee linkage during planar motions with and without task constraints unlike those of single-joint motions, which do not evaluate coordination or equilibrium. Performers who are able to self-impose the boundary conditions must reconfigure 2-point crouch-down to utilize the force of gravity (i.e., a conservative force to guide the reconfigured body to unload and fall within the base of support to a specified target configuration). With the hidden skill of unloading (a prerequisite skill for squatting), 2-point crouch-down behaves like a highly predictable robotic MJM 2-joint planar system of low complexity may attain stabilization as described in 1972 by Greene (15) where: “The feedforward had only to bring the state of the system “into the right ballpark”— that is, into some broad class of states, within which feedback could automatically bring the system the rest of the way to the exact state required” (p.310). However, often postural constraints are overlooked perceptually (i.e., a hidden skill) with respect to even simple configurations. This suggests that balance may be achieved with compensatory configurations leading to risk of injury or poorer performance. Failure to apply constraints of a simple 2-joint planar system presupposes identifying a worst first move or revealing an underdeveloped hidden skill in which practicing an inferior stable pattern reinforces a belief system that could argue support of schema theory (34). Herein, there exists a paradox; namely, an inferior stable pattern is preferred based upon perception. Such beliefs are suspected to lead to misuse, overuse, or abuse that ignores microtraumatic warning signs of faulty mechanics that potentially risks injury or at least results in poorer performance.
From an epistemological perspective, MJM has the epistemic value of simplicity to assess the coordination of the hip and knee in a planar 2-joint system that avoids chaos by reducing the degrees of freedom; however, this model also offers the non-epistemic value of wellbeing with its output that provides an evaluative judgment (6) about a state of a most basic skill to crouch-down applying constraints to match either a target or the boundary configuration. Why this occurs may be explained where a hidden skill controlling constraints has yet to be discovered how to utilize gravity; as well as, a lack of vital sensory input to enable a perception that is difficult to discern, often resulting as an OPC-II, and fails to change a belief system.
Sensory information and perception (belief systems) that influence preferred behavior possess elements of uncertainty, which lacks both reliability and predictability. An application of the Le Chatelier-Braun principle attempts to measure uncertainty of sensory equilibrium ranging from a state of complete certainty to sensory disequilibrium (28). When an unfamiliar stimulus increases uncertainty, a state of sensory disequilibrium exists until a restoring influence provides a new level of sensory equilibrium. Sensory equilibrium approaches a state of complete certainty as the preferred behavior when movement tasks are perceived as acceptance by the performer (i.e., OPC-I or OPC-II) compared to a state of sensory disequilibrium when movement tasks are assessed as rejection by the performer (i.e., OPC-III or OPC-IV). This presents the performer with a sensory dilemma of discrimination with an observer-performer interaction that may undergo a sensory order described in 1976 by Hayek (17) as a process of classification and reclassification in the brain “to create altogether new sensory qualities which have never been experienced before… can be greatly developed by practice” (p.152), and the observer with a wording or feedback predicament where Wulf (46) in 2013 argued “subtle differences in the wording of instruction or feedback can have significantly different effects on performance and learning” (p.99) as with optimal configurations and movement strategies.
Participants. The performers from Task 1 that were available for Task 3 were students in grades K-5 (N=72) except for one absentee, grades 7-12 comprised of the female runners (N=144), and the X-group (N=21). Task 3 was added to the study after the NJ students (N=80).
Figure 4. RRStart configurations with BMM: (A) boundary and (B) target; and (C) functional BMM variability is hypothesized to be resolved with self-discovery learning provided MJM is set close to the rear foot. Adult, parental, and child written consents, as well as child verbal assent under age 7 were obtained for permission to use these images.
Procedures. The performers were asked to run from a dFMS, called RRStart, which was configured and shown as a 4-point crouch-down for the feet, hand, and elbow in a tandem stance with their feet about a foot’s length apart, the hand of the rear foot placed on their ribs, and the contralateral elbow placed at the knee of their rear leg for the RRStart boundary configuration (Figure 4A) with a BMM set with bodyweight primarily on the heel of the rear foot. The hand-elbow positions help attain thoracic rotation of the trunk. Only the X-group was told how to set the BMM with whole foot ground contact weighted primarily on the perpendicular rear leg. The runners and elementary students were not told about perpendicularity. Simon-Says & Shows was used with K-5 students; and their hand instead of the elbow was placed at the rear leg knee for the RRStart target configuration (Figure 4B) because they failed the MJM unconstrained in Task 2.
Three predictable outcomes were observed and recorded as: 1) no-step, which increases the base of support by transferring weight onto front foot before stepping forward with the rear foot, 2) momentum-step, where the front foot steps forward spontaneously due to an assumed rear foot propulsive force that produces the momentum-step from the idealized narrow support base approximating the anterior calcaneus point of the rear foot, or 3) step-back, which increases the base of support prior to running. Task observations were recorded as a simple tally. Proportions were calculated to quantify behavioral preferences. All relevant data regarding preferred behaviors can be obtained as percentages of the samples in the paper and Figure 5.
RRStart strategies (Figure 5) were assessed as OPC-I for the momentum-step because it was assumed this strategy required a higher level of skill as a prerequisite for a run; but, no-step and step-back were assigned OPC-II and assumed not to optimize performance. For the RRStart (Figure 5), K-5 and the X-Group were seen with BMM intervention and without practice. K-5 (N=72) averaged OPC-II at 19.4% for no-step. K-2 (N=33) performers averaged OPC-I at 72.7% for momentum-step. Grades 3-5 (N=39) averaged OPC-II at 79.5% for step-back. The X-group (N=21) averaged OPC-I at 81% and OPC-II at 19% for momentum-step and no-step, respectively. With practice and without BMM intervention, the preferred behavior for the performers in grades 8-12 (N=130) averaged OPC-I at 12.3% and OPC-II at 87.7% for momentum-step and no-step, respectively. Grade 7 (N=14) were preferred similarly; then diverge.
Figure 5. RRStart preferred behaviors with BMM intervention and without practice (white legend keys) and preferred behaviors with practice and without BMM intervention (black legend keys). The X-group and grades K-2, especially grade 1, preferred a momentum step (white square). Grades 3-5 preferred the step-back (white circle). Grades 8-12 preferred no-step (black triangle) and very few preferred the momentum-step (black square). The momentum-step (black square) and the forward-step (black triangle) were closer in preference for students in grade 7.
RRStart was observed with and without BMM intervention or practice of the 2-joint system to assess the IVP 4-point crouch-down strategy to start a run for a complex 3-D system with moderately predictable outcomes of each performer’s preferred behavior without knowing the initial conditions to seek a steady state among a set of possible initial configurations that can be trusted as preferred solutions from which to run and reveal hidden skills. Again, this situation might support information processing theory (1) where creative wording and appropriate feedback provides the performer the required information (46). The perpendicularity of the BMM intervention may be the critical information given to the performer by the observer.
From the dynamical systems perspective, RRStart theoretically models IVP solutions for the hidden skill that applies an effective propulsive force that optimizes momentum for a 4-point crouch-down strategy to produce a momentum-step from an idealized narrow support base to start a run. The researchers hypothesize that the momentum is directed optimally forward and up with a momentum-step, which increases step length; and that the step-back or no-step strategies increase their support base before running where momentum is directed backward initially or downward, respectively. RRStart offers a performance readiness evaluation of momentum-step self-discovery with MJM using the BMM, which appears to be important information for the initial condition of position that wasn’t presented using drills during the initial study. Future study is needed to resolve the contribution of MJM with RRStart as a key configuration leading to develop a momentum-step (see a leading MJM hypothesis).
From an epistemological perspective, Task 3 lets us know that very few performers in grades 7-12, as illustrated in Figure 5, preferred the momentum-step without BMM intervention even with practice compared to applying BMM intervention where a majority of the performers in grades K-2 and the X-Group preferred the momentum-step without practice. It is not clear why grades 3-5 preferred step-back. RRStart has the epistemic value of simplicity to study a dFMS; however, RRStart also offers the non-epistemic value that represents a complex process to describe and explain wellbeing (6) with its output (i.e., the momentum step) to provide a sensory value judgment about a most basic skill to exploit momentum with a properly directed quality thrust that results as a smooth transition to start a run (OPC-I) or not. This illustrates a case of a non-epistemic value that the RRStart models a process where pragmatic limitations of a sense exists and epistemic values do not determine the significance of an effortless perception (6). Why this occurs is explained with the fact that the brain does not sense momentum itself; yet, this elucidates how a FMS prerequisite addresses this issue and offers a means to create, acquire, and communicate knowledge to the performer to discover the skill of exploiting momentum.
Participants. Only X-group performers (N=20) with one opting out attempted another dFMS, called the RRRun. K-5 performers (N=73) did not participate because they required intervention drills and practice, which was beyond the scope of this paper. RRRun was added to the study after the participation of the female camp runners (N=144) and the NJ students (N=80).
Procedures. The X-group performers were asked to attempt the RRRun as a simultaneous 2-point crouch-down of the hand and foot, which involves touching their contralateral knee with their hand at the same time their foot strikes the ground while running at a preferred speed within a distance less than 20 meters. Their bilateral ability was observed when the performers were asked to repeat the task on the opposite side. The three possible outcomes observed with hand-knee contact were recorded as: 1) foot-grounded, 2) foot-airborne, or 3) no-contact. To avoid the common occurrence of foot-airborne, the researchers emphasized instruction to touch the knee when the foot hits the ground for a pragmatic perceptive assessment of the RRRun at footstrike.
Figure 6. RRRun (A) left foot contact reported perceptively as a good thrust during the run; (B) right foot contact reported perceptively as awkward, which would likely make the performer think something is wrong and the observer believe the performer’s perception of a flinging right arm to compensate a loss of balance is an error rather than a self-discovery, evolutionary process with functional variability supported with BMM that aids learning and adaptation; and (C) right foot contact reported perceptively a good thrust during the run after 2 weeks of self-discovery practice. Parental and child written consents were obtained for permission to use these images.
For the X-group executing a few RRRun trials, unilateral outcomes for no-contact and foot-grounded were seen OPC-II or OPC-IV at 65% and OPC-I at 35%, respectively. All non-contact outcomes were seen and acknowledged by the observer and performer, respectively. All unilateral foot-grounded outcomes were seen in the ballpark; and each performer reported feeling a quality thrust on one side; yet, no thrust on the other. Of the two performers (10%) who were observed successful bilaterally, a 12-year old female had been videoed during the RRRun, which revealed screenshots that her reported quality thrust did hit the simultaneous 2-point crouch-down BMM target configuration (Figure 6A) on her left foot; and that she reported feeling awkward when touching her knee at right foot contact with compensatory right arm to balance at the simultaneous 2-point crouch-down BMM target configuration (Figure 6B).
RRRun is a non-epistemic process that models (6) a sensory experience of a quality thrust transitioning smoothly throughout the phases of the RRRun where pragmatic limitations of a feeling exist and epistemic values do not determine the significance of a quality perception. This experience results in an increased wellbeing that is essential for the performer to trust feeling an applied force that can be aided by the observer with the BMM screenshot. The objective is a value judgment to assess the hidden skill of applying an effective force that avoids the misuse of an improperly directed force. Reliance on such a perception of force is questionable (28); however, clinically this perception aids to develop and learn the hypothesized hidden skill of the effective force to exploit momentum in a run. Also, unilateral outcomes provide motivation to self-discover the feeling of a quality thrust bilaterally with a smooth transition into the run. RRRun provides the performer what to feel, which cannot be described like the taste of an apple. Rather, it can only be known through feel. What the researchers know here is that the performer feels whether or not they experience a thrust; as well as, whether or not there is a sense of awkwardness or smoothness performing the task. Why this occurs may be explained similarly to Task 1 where a lack of vital sensory input has yet to exist for a meaningful perceptual understanding that could change the belief system of the performer. Caution is advised attempting RRRun at faster speeds. Also, cautions with footstrike have been expressed (16,26). Future study is needed for the contribution of MJM using BMM with RRRun as a key configuration to develop the momentum-step (see a leading MJM hypothesis).
The researchers used these 4 independent tasks to introduce a qualitative methodology for both practitioners and researchers to identify a set of worst first moves, reveal hidden skills, and provide perceptive value-judgments with respect to what needs to be known to group the preferred behaviors of performers as witnessed by the observer. This was done using a scientific epistemology involving dynamical systems and the concept of idealization for the different dimensions of these four tasks; namely, a single-joint 1-D system, a 2-joint 2-D system, and two dFMS 3-D systems (i.e., RRStart and RRRun) as FMS prerequisites for running.
The researchers observed different running strategies with which performers seek a more stable configuration from which to run (e.g., step-back, step-forward, crouch-down, momentum-step, foot-grounded, no-step, and no-contact). However, dFMS like RRStart, yielding momentum-step with self-discovery, and RRRun, sensing an asymmetry with respect to force or an awkwardness, solve an IVP where the performer must spontaneously rely on not only the initial conditions of position and velocity, but also the somatosensory inputs from the pressure on the sole of the rear foot, and the proprioceptive signals about the status of the joints, participating muscles, and configurations. Inverse dynamics of the motor system involves muscle torque activation that ideally moves the limbs of the body as desired (45). Inverse kinematics is the required motor control for reaching actions whereupon before planning those acts were described in 2002 by Wise & Shadmehr (45) as “… the motor system must estimate both current hand position and the direction and magnitude of the movement needed to reach the target” (p.11). Analogous to hand position is the run-start, RRStart, RRRun configurations requiring increased sensory input to solve the initial conditions of their IVP. Furthermore, the forward dynamics of the motor system’s applied force estimates needed to produce the desired motion relies on this computation with the ability to predict the sensory consequences of motor commands (45).
The Biomechanical Problem
Seeking an optimal initial configuration is a problem in nonlinear dynamics for systems of greater complexity than the expected MJM as their trajectories are unpredictable (chaotic). This is further complicated by the fact that biomechanical models and their studies are incapable of quantifying the sensory and perceptual contributions of skill development. In addition, inverse dynamics attempts to analyze more suitable trajectories for more complex systems of three joints or more, but is limited even when compared with forward dynamics. Forward dynamics was claimed as the golden standard (29) from which gait analysis laboratories using inverse dynamics vary greatly to match forward dynamics data. Forward dynamics relies on computing double integration of the ground reaction forces of existing configurations to calculate center of mass of the body’s position that represents locomotion throughout its trajectory. Conversely, inverse dynamics uses kinematics (i.e., the geometry of the system) in approximating the body’s position with double derivation that risks error in acceleration calculations when compared with forward dynamics data. Moreover, neither forward dynamics nor inverse dynamics is able to find new configurations that have yet to exist or assess sensory inputs that have perceptive epistemic value, which cannot be quantified. For example, a forward dynamics analysis of the straddle high jump technique before the existence of the Fosbury Flop technique would fail to discover the Fosbury Flop in spite of any inverse dynamics analysis of the straddle high jump that closely approximates its forward dynamics data. Likewise, these forward dynamics studies could only compare known crouch-down initial configurations and their motions; and they could neither discover an unknown optimal crouch-down initial configuration nor original dFMS like the RRStart and the RRRun that provide value-judgments for what can be known through feel and a method to train and learn with the explicit knowledge of MJM’s geometry and the tacit knowledge (31) acquired from decades of hands-on clinical experience regarding human movement skill associated with both enhanced athletic performance and functional rehabilitation moving toward optimal health (8,18).
The Value of the BioMechanical Marker
A geometrical interpretation at an observable level of FMS prerequisites affords economy and insight in monitoring the outcomes to investigate novel MJM solutions. Geometrically, the center of mass of the body represents its location either stationary or moving. The center of mass of the body can be assumed to travel a one-dimensional linear trajectory in the hip-knee planar motion of the MJM 2-joint system, which is bounded anatomically by a terminal configuration at the start of its linear path and by a boundary configuration at its end range of motion to maintain equilibrium as seen with the BMM. According to Glazier and Robins (12) in 2012, qualitative analytical techniques are not the traditional “… observation and subjective evaluation of movement sequences … but rather the study of geometric properties of movement” (p.121). Robotic MJM gives both a visual image and a mathematically defined geometric coordinative movement pattern for a technique analysis of whether or not a performer successfully applies self-imposed constraints while attempting to perform the process-oriented, self-discovery approach of pragmatically solving a two-point BVP for the motion of an idealized, 2-joint planar system. At its boundary or target configuration, MJM has a high predictability to function reliably as a mechanism, which identifies the performer’s sensibility and knowledge about the worst first move, and provides a means to compare the ability of the performer to control and coordinate the simple 2-joint system to match a boundary or a target configuration of low complexity. In addition, MJM is observed at a key BMM moment, which helps the observer clinically assess the RRStart in a state of static equilibrium and RRRun using a screenshot for dynamic equilibrium.
Translating Practice into Theory
Complex systems research for the emergent patterns of human movement has been argued to start directly at the observable level of behavior (25). An epistemic framework is needed to bridge the gap between practice and theory. Evidence of this gap in research was held in 2016, where after almost 2 decades of research, Latash (20) wrote, “While the recent progress in biomechanics and motor control has been impressive, we are still far from being able to make recommendations for practitioners, such as physical therapists, coaches and physical education teachers. The current established knowledge is meager and the intuition of a good clinician or a good coach typically beats recommendations that can be made by a researcher” (pp.17-18).
Figure 7. RRRun (A) an IVP of unknown initial conditions prior to toe-off; (B) whole-foot contact characterizing the first BVP point starting with a known BMM 2-point simultaneous crouch-down target configuration; (C) the second BVP point ending a phase transition of whole-foot maintenance prior to heel-lift; (D) a third point depicting one of many possible forward lean configurations prior to toe-off; (E) the known whole-foot configuration as seen with BMM, representing the hypothetical IVP initial configuration of running periodicity; and (F) an IVP solution of a known position and uncertain velocities to hit another forward lean configuration prior to toe-off. Parental and child written consents were obtained for permission to use images.
For the good clinician or good coach, the researchers present systems thinking with a qualitative methodology for how what they know to explain and predict natural phenomena that cannot be observed empirically; rather, that which is known through the senses as a non-epistemic value of wellbeing in modeling the process of dFMS. For example, consider the follow-up screenshots of a video with a Samsung Galaxy (S7) are used to illustrate what the researchers know about the self-discovery experienced using RRRun by a 12-year-old female from the X-group. After two weeks of having played independently with RRRun to self-discover bilateral symmetry, she was seen as a follow-up and reported a quality thrust on the right foot as seen with the simultaneous 2 -point crouch-down BMM target configuration (Figure 6C). One week later, her video screenshots showed a forward lean configuration on the left foot prior to toe-off (Figure 7A), her left hand touching her right knee in the simultaneous 2-point crouch-down BMM target configuration of right whole-foot strike (Figure 7B), maintaining whole-foot contact prior to heel-lift at another MJM (beyond the scope of this paper) where she reported experiencing a good thrust (propulsive force) (Figure 7C), a forward lean configuration prior to toe-off (Figure 7D), the left whole-foot strike seen with the BMM at a theoretical initial configuration of the run or sprint (Figure 7E), and ending the left foot stride at another forward lean configuration prior to toe-off (Figure 7F). An epistemology is needed to close this gap leading to wellbeing, and it is worth the pursuit (18).
Epistemological Insights and Theoretical Concepts
From the perspective of an observer-performer interaction, the researchers connect observation to theory with the epistemological systems concept (42) that involves distinguishing the empirical and the theoretical concepts in order to grasp systems concepts by bridging the usage of their languages describing observations. Clinically, pictures are the language of a thousand words and observations are the first step in the scientific process to answer the question: What do the researchers see and what does the runner feel? Video screenshots, the language of observable configurations, offer the perspectives of both the observer and the performer. The researchers opine that her reported feeling a good thrust with the RRRun provided her with a justified belief to continue independently to self-discover bilateral success. Her forward lean (Figure 7A) theoretically required an IVP solution for momentum to be directed optimally forward and up; rather than leaning too far forward redirecting momentum forward and down. It is the opinion of the researchers that she possessed the intrinsic dynamics to trust that her toe-off IVP solution would hit the known BMM simultaneous 2-point crouch-down target configuration (Figure 7B) without her explicit knowledge of the IVP initial conditions of many possible toe-off configurations. From an epistemological perspective, the belief of the researchers is supported observing the whole-foot BMM and hearing about her justified belief claiming feeling a quality thrust without any awkwardness. Theoretically, her simultaneous 2-point crouch-down target has established the first of two points for solving a 2-point BVP within this piecewise phase of interest that a hidden skill may be revealed with other points between the two footstrike BVP points. In theory, there are infinite numbers of transitional configurations of which heel contact (Figure 7C) remains debatable as a justified belief in sprinting. From perspective of the observer, the representational momentum that is directed from the forward lean (Figure 7D) can be supported by Nakamoto, et al (27) in 2015 who claim such a perspective “… depends not only on raw visual information, but also on internal representations that include the expectation for a future object location, perhaps based on prior knowledge” (p.970); that is BMM. The researchers’ belief is that the expected future location is the theoretical initial configuration of the run or sprint between steps as seen with the BMM configuration (Figure 7E) that the observer would predict to see at the completion of the step from right whole-foot to left whole-foot, which is a known position. From her perspective, she feels a change in momentum at impact (i.e., an impulsive force) and the muscular forces stabilizing segmental rotary motion as an effortless and smooth transition into the run. Her brain cannot feel momentum itself, and is subjected to its consequences of linearly directing the center of mass of the body and angularly segmental spin about bodily axes. However, this created another IVP for her to solve the unknown initial segmental velocities to complete the stride between left footstrikes at another forward lead configuration prior to toe-off (Figure 7F), which depends on her producing and feeling a quality thrust (i.e., a propulsive force). This example using RRRun, a dFMS prerequisite, reclassifies from an OPC-III to an OPC-I with only her self-discovery playing with the RRRun. The information provided to this performer was the feeling of awkwardness and no thrust (Figure 6B) that she rejected the task; yet, seen achieving the BMM, she was classified an OPC-III. However, the comparison with a quality thrust during the BMM of the left foot (Figure 6A) was assessed as an OPC-I, which helps to create, acquire, and communicate knowledge to the performer to discover the skill bi-laterally as an OPC-I (Figure 6C). The knowledge of what the researchers know and how the researchers know it with respect to what the performer senses and then perceives requires future study for translating practice into theory using FMS prerequisites with a BMM of an MJM 2-joint system.
Olympic Sprinting: Is There a Hidden Skill?
Performers who successfully hit the simultaneous 2-point crouch-down target configuration starting from many possible unknown initial conditions at the toe-off configuration represent the hidden skill in which they self-discover a forward dynamic solution for the IVP to hit the known configured target with moderate predictability and functional variability from which to learn (5,24). This involves a low tolerance of error in the temporary assemblage of a coordinative structure required in order to establish the first point for the 2-point BVP, which is necessary and sufficient to have at least one more point to hit a BMM target configuration at the next footstrike. However, theoretically critical information is likely missed by not identifying other configured targets as critical points between these 2 BVP points, which generate piecewise intervals of an inverse dynamic solution. Mathematically, human gait was modeled in 3-D space with 34 degrees of freedom as a piecewise function, and with a future goal to model running (9).
Clinically, RRRun provides a dynamic visual model of a piecewise function of intervals between steps. For example, rather than an interval between steps, heel-lift from whole-foot and toe-off are other points of interest theoretically on the same foot before the next footstrike, especially because whole-foot contact is considered a sprinting error; and therefore, heel-lift remains a debatable hidden skill. This debate (21) had already entered the distance running world showing that the minimally shod Tarahumara Indians during running used strikes at mid-foot (40%), forefoot (30%), and rear-foot (30%). The researchers’ contention is that MJM at a key BMM moment of whole-foot contact as seen with Olympic sprint champions (Figure 8) approximates an optimal initial configuration of periodicity from which to sprint with a momentum-step directed optimally forward and up at the toe-off lean compared to sprinters who are likely leaning too far forward with their momentum directed forward and down at toe-off; and thus, the heel doesn’t make contact with the ground (see Appendix A: The Visual Search).
Figure 8. Dynamic MJM whole-foot contacts with the track are seen at 100 meters for (A) Owens in 1936 and (B) Bolt in 2009; at 400 meters for (C) Van Niekerk in 2016; and at 300 meters for (D) Van Niekerk in 2017 at the 56th Ostrava Golden Spike; (E) whole-foot contact is maintained. These screenshots are used in accordance with the Fair Use Act only for educational and research purposes that add new information. They are not a substitute for their original use; and they are different from their intended use and the nature of their copyrighted work.
The Inverted U-Model
The concept of MJM, as whole sub-systems contained within the larger body of FMS systems, reveals hidden skills to be assessed and trained with creative, empirically observable dFMS like RRStart and RRRun that offer theoretical conceptualization of hidden skill. The dFMS is a tactic that provides a comparison to other strategies that give meaning for the performer with respect to momentum in an optimal state of variability to organize chaotic behavior in a more predictable manner over time. A hypothesis that involved human movement variability was explained in 2013 by Stergiou et al. (38) with an inverted U-model “… based on the idea that mature motor skills and healthy states are associated with optimal movement variability that reflects the adaptability of the underlying control system” (p.96), which depicts chaotic systems in an inverted U-shaped relationship. In 2004, Shalizi et al (33) defined “… complexity as the amount of information for optimal statistical prediction” (p.4). In 2015, Stephens (37) argued that complexity for complex systems is not a measure of degrees of freedom, non-linear interactions, or a balance between order and disorder, but rather meaning and fitness for languages and biological systems, respectively as distinguishing properties of complex systems.
Figure 9. The inverted U-model of gross motor skills show a relationship of low complexity for FMS and MJM compared to a greater complexity for dFMS; and predictability is least for FMS and most for MJM with dFMS somewhat likely.
The researchers submit FMS, dFMS, and MJM as another inverted U-shaped relationship (Figure 9) that exhibits gross motor skill predictability with respect to constraints applied to the degrees of freedom of a system; such that FMS possess the least constraints, dFMS deal with some, and MJM have the most. Complexity is highest for 3-D dFMS, which are reconfigured systems involving competing strategies and sensory input that adopt a new update rule within a given environment greater than the less complex old rules (37) of the 3-D FMS and the sub-level of the 2-D MJM configured system. The researchers propose that these competing strategies update their rules during an observable motion in a certain dynamic state towards a particular goal with something that the brain cannot feel – momentum – such that, there is a sense of smoothness, effortlessness, proficiency, grace, or power. The CNS experiences changes in momentum (i.e., a force). This inability to feel momentum is a measure of complexity that the brain must self-discover meaning of this phenomenon in the complex state of dFMS reconfigured strategies as momentum evolves better and better with updated rules that aid the brain in choosing a newly preferred running strategy in a state that did not exist originally, which is compared and understood with competing strategies of lesser complexity leading to a deeper understanding through perceptive learning with bodily sensory inputs (17,22) and wellbeing (8,18).
A Leading MJM Hypothesis
A leading joint hypothesis (LJH) was offered as an alternative theory (7) that permitted transparency for the control of human movements to address the significant limitations of the major human movement control theories that involve multiple joints in spite of their invaluable contributions. LJH is theorized on the notion that the central nervous system exploits interaction torque involving the linkages of several segments. Future research could be discovered in other anatomical units as a leading structure greater than the single joint as seen with the leading role of the hip-knee linkage in cycle pedaling (32), which may also serve as leading structures beyond the LJH of a single joint. Furthermore, MJM is suspected to signify a predictable occurrence of low complexity and minimal variability of hip-knee coupling seen in running (9). The researchers propose the hip-knee linkage in MJM might also function as a prospective leading MJM hypothesis. Extending the scope of the LJH beyond a single joint, a leading MJM hypothesis with MJM and dFMS may provide answers to know how to ask questions to identify hidden skills and identify the set of worst first moves. For example, does using the BMM of MJM for the key BVP points seen in the RRRun (Figure 7) provide a leading MJM hypothesis for IVP solutions theoretically; as well as for the BMM to assess performance clinically? The MJM and dFMS are clinical assessments for translating practice into theory where future research is required to determine reliability and validity. This would establish performance readiness evaluation procedures and injury risk assessment algorithms using early MJM tests and dFMS interventions to prevent ignoring the signs or symptoms of microtraumatic events leading to misdirected forces (misuse), repetitive joint stressors (overuse), and misguided beliefs of “no pain, no gain” (abuse) not only in the athlete but also for everyone.
FMS prerequisites, which were introduced and assessed epistemologically and algorithmically in OPC, offer a scientific epistemology as a different scientific inquiry at the observable level of a pragmatic qualitative methodology that uses systems thinking for translating practice into theory by integrating mathematical and dynamical systems concepts with belief systems in physical education as illustrated with running. By addressing the what is known question that leads to ask why and how, this approach moves toward optimal health biomechanically and a wellbeing sense with the epistemic and non-epistemic values that support the OPC value-judgments of the FMS prerequisites to promote learning for physical educators and healthcare professionals; as well as, to challenge the belief systems of students, athletes, or community-at-large involving their perception, motor actions, and bodily senses.
A major limitation of this study is that it relies on observable data without using biomechanical equipment; however, these MJM and dFMS observations represent novel outcomes that should stimulate theoreticians to contemplate and mathematicians to model the inverted U-model and the leading MJM hypothesis of the unobservables derived from MJM and dFMS that have not yet been well expressed.
FMS prerequisites will help to 1) prevent undesirable chaotic behavior by pragmatically solving IVP for an optimal, self-organizing task like the crouch-down running strategies that exploits the momentum-step to optimize motion (e.g., step-length and direction), 2) discover newly preferred steady states of the system, 3) assess the trust of the system, including the belief system of the performer, to rely on its initial conditions, 4) encourage other researchers and practitioners to design other MJM and dFMS as FMS prerequisites to provide very early detection of flawed gross motor skill development before manifesting into the signs and symptoms of injury or poor performance, which are the rudimentary configurations for injury risk assessments and performance readiness evaluations, and 5) provide new leadership with key stakeholders to pursue a new direction for physical education which has failed the children with respect to pedagogical strategies to optimize the development, learning, and testing of fundamental movement skills as a foundation for sport, as well as a sense of wellbeing and moving towards optimal heath biomechanically across a lifespan.
APPLICATIONS IN SPORT
This original research methodology of sorting out observer-performer interactions acquires what the observer (physical education teacher, coach, health professional, parent) needs to know and learn about the aptitude of a performer’s fundamental movement skills (FMS) as illustrated with running, and what performers (athletes, students, community-at-large) need to feel and trust about their belief system to promote sensory and perceptive learning where novel FMS prerequisites discover hidden skills leading to optimal FMS development. The ultimate goal is to translate practice into theory that leads to a paradigm shift for prevention of sports injuries and flawed performances by means of moving towards optimal health biomechanically and wellbeing.
The first author wishes to thank Professor H. Michael Lacker, MD, PhD for many years of friendship and collaboration. The researchers are grateful to Gladys Davis, Head of School, for her support with access to K-5 students. Screenshots in Figure 8 showing whole-foot contact for Jesse Owens winning the 1936 Olympics, Usain Bolt in Berlin at the 2009 World Athletics Championships breaking the 100 meter world record, and Wayde van Niekerk’s 400 meter world record at the 2016 Olympic Games and his 300 meter world record at the 56th Ostrava Golden Spike, IAAF World Challenge on June 28, 2017 are used in accordance with the Fair Use Act only for educational and research purposes that add new information, and not a substitute for original use. They are different from their intended use, the nature of their copyrighted work; and they are provided on the internet at no expense.
CONFLICT OF INTEREST
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
- Anson, G., Elliott, D., & Davids, K. (2005). Information processing and constraint-based views of skill acquisition: Divergent or complimentary? Motor Control, 9, 217-241. Retrieved from https://pdfs.semanticscholar.org/48ec/8414b9804b3d23574cc9693fc45c0295e59e.pdf
- Barnett, L. M., Stodden, D., Cohen, K. E., Smith, J. J., Lubans, D. R., Lenoir, M., Iivonen, S., Miller, A. D., Laukkanen, A., Dudley, D., Lander, N. J., Brown, H., & Morgan, P. J. (2016). Fundamental movement skills: An important focus. Journal of Teaching in Physical Education, 35(3), 219-225. doi.org/10.1123/jtpe.2014-0209
- Bernstein, N. A. (1967). The co-ordination and regulation of movements. Oxford: Pergamon Press.
- Darlaston-Jones, D. (2007). Making connections: The relationship between epistemology and research methods. The Australian Community Psychologist, 19(1), 19-27. Retrieved from https://groups.psychology.org.au/Assets/Files/Darlaston-Jones_19(1).pdf
- Davids, K., Glazier, P., Araújo, D., & Bartlett, R. (2003). Movement systems as dynamical systems: The functional role of variability and its implications for sports medicine. Sports Medicine, 33(4), 245-260. doi:10.2165/00007256-200333040-00001
- Dickmann, S., & Peterson, M. (2013). The role of non-epistemic values in engineering models. Science and Engineering Ethics, 19, 207-218. Retrieved from https://link.springer.com/content/pdf/10.1007%2Fs11948-011-9300-4.pdf
- Dounskaia, N. (2010). Control of human limb movements: The leading joint hypothesis and its practical applications. Exercise Sport Sciences Reviews, 38(4), 201-208. doi:10.1097/JES.0b013e3181f45194
- Faust, H. S. (2013). A cause without an effect? Primary prevention and causation. Journal of Medicine and Philosophy, 38, 539-558. doi:10.1093/jmp/jht039
- Felis, M. L., & Mombaur, K. (2016). “Synthesis of full-body 3-d human gait using optimal control methods,” in 2016 IEEE International Conference on Robotics and Automation (ICRA) (Stockholm: IEEE), 1560–1566
- Floría, P., Sánchez-Sixto, A., Ferber, R., & Harrison, A. J. (2018). Effects of running experience on coordination and its variability in runners. Journal of Sports Sciences, 36(3), 272-278. doi:10.1080/02640414.2017.1300314
- Frost, D. M., & Cronin, J. B. (2011). Stepping back to improve sprint performance: A kinetic analysis of the first step forwards. Journal of Strength and Conditioning Research, 25(10), 2721-2728. doi:10.1519/JSC.0b013e31820d9ff6
- Glazier, P. S., & Robins, M. T. (2012). Comment on “Use of deterministic models in sports and exercise biomechanics research” by Chow and Knudson (2011). Sports Biomechanics 11(1), 120-122. doi:10.1080/14763141.2011.650189
- Grecic, D. (2017). Making sense of skill – a personal narrative of becoming more skilled at skill. Journal of Qualitative Research in Sports Studies, 11(1), 33-48. Retrieved from https://works.bepress.com/clive_palmer/147/
- Grecic, D., & Collins, D. (2013). The epistemological chain: Practical applications in sports, Quest, 65(2), 151-168. doi: 10.1080/00336297.2013.773525
- Greene, P. H. (1972). Problems of organization of motor systems. In R. Rosen & F. Snell (Eds.), Progress in Theoretical Biology, Vol. 2. (pp 303–338). Academic Press, New York
- Hamill, J., & Gruber, A. H. (2017). Is changing footstrike pattern beneficial to runners? Journal of Sport and Health Science, 6(2), 146-153. doi.org/10.1016/j.jshs.2017.02.004
- Hayek, F. A. (1976). The Sensory Order (paperback edition.). The University of Chicago Press, Chicago, Illinois
- Khushf, G. (2013). A framework for understanding medical epistemologies. Journal of Medicine and Philosophy, 38, 461-486. doi:10.1093/jmp/jht044
- Kraan, G. A., van Veen, J., Snijders, C. J., & Storm, J. (2001). Starting from standing: why step backwards? Journal of Biomechanics, 34(2), 211-215. doi: 10.1016/s0021-9290(00)00178-0
- Latash, M. (2016). Biomechanics as a window into the neural control of movement. Journal of Human Kinetics, 52, 7-20. doi: 10.1515/hukin-2015-0190
- Lieberman, D. E. (2014). Strike type variation among Tarahumara Indians in minimal sandals versus conventional running shoes. Journal of Sport and Health Science 3(2), 86-94. doi.org/10.1016/j.jshs.2014.03.009
- Light, R. (2008). Complex learning theory – Its epistemology and its assumptions about learning: Implications for physical education. Journal of Teaching in Physical Education, 27, 21-37. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.457.9365&rep=rep1&type=pdf
- Ma’ayan A. (2017). Complex systems biology. Journal of the Royal Society Interface 14: 20170391, 1-9. doi.org/10.1098/rsif.2017.0391
- Manoel, Ede. J., & Connolly, K. J. (1995). Variability and the development of skilled actions. International Journal of Psychophysiology, 19(2), 129-147. doi.org/10.1016/0167-8760(94)00078-S
- Mayer-Kress, G., Liu, Yeou-Teh, & Newell, K. M. (2006). Complex systems and human movement. Complexity, 12(2), 40-51. doi.org/10.1002/cplx.20151
- Moore, I. S. (2016). Is there an economical running technique? A review of modifiable biomechanical factors affecting running economy. Sports Medicine, 46(6), 793-807. doi:10.1007/s40279-016-0474-4
- Nakamoto, H., Mori, S., Ikudome, S., Unenaka, S., & Imanaka, K. (2015). Effects of sports expertise on representational momentum during timing control. Attention, Perception, & Psychophysics, 77, 961-971. doi:10.3758/s13414-014-0818-9
- Norwich, K. H. (2010). Le Chatelier’s principle in sensation and perception: fractal-like enfolding at different scales. Frontier in Physiology, 28, 1-7. doi.org/10.3389/fphys.2010.00017
- Pavei, G., Seminati, E., Cazzola, D., & Minetti, A. E. (2017). On the estimation accuracy of the 3d body center of mass trajectory during human locomotion: Inverse vs. forward dynamics. Frontiers in Physiology, 8, 1-13. doi: 10.3389/fphys.2017.00129
- Philp, F., Blana, D., Chadwick, E. K., Stewart, C., Stapleton, C., Major, K., & Pandyan, A. D. (2018). Study of the measurement and predictive validity of the functional movement screen. BMJ Open Sport & Exercise Medicine, 4, 1-7. doi:10.1136/bmjsem-2018-000357
- Polanyi, M. (1974). Personal knowledge: Towards a post-critical philosophy. (paperback edition). The University of Chicago Press, Chicago, Illinois
- Raasch, C. C., & Zajac, F. E. (1999). Locomotor strategy for pedaling: muscle groups and biomechanical functions. Journal of Neurophysiology, 82(2), 515-525. doi:10.1152/jn.19188.8.131.525
- Shalizi, C. R., Shalizi, K. L., & Haslinger, R. (2004). Quantifying self-organization with optimal predictors. Physical Review Letters, 93(11), 1-4. doi.org/10.1103/PhysRevLett.93.118701. Retrieved from https://arxiv.org/pdf/nlin/0409024.pdf
- Shea, C. H., & Wulf, G. (2005). Schema theory: A critical appraisal and reevaluation. Journal of Motor Behavior, 37(2), 85-101. Retrieved from http://gwulf.faculty.unlv.edu/wp-content/uploads/2014/05/Shea_Wulf_2005.pdf
- Skraba, Z. P. (2016, September, 1). Stride length vs stride frequency in the 400 metres. Blog zigapskraba. Retrieved from https://zigapskraba.com/2016/09/01/stride-length-vs-stride-frequency-in-the-400-metres/
- Slawinski, J., Houel, N., Bonnefoy-Mazure, A., Lissajoux, K., Bocquet, V., & Termoz, N. (2017). Mechanics of standing and crouching sprint starts. Journal of Sports Sciences, 35(9), 858-865. doi:10.1080/02640414.2016.1194525
- Stephens, C. R. (2015). What isn’t complexity? Nonlinear Sciences > Adaptation and Self-Organizing Systems (nlin.AO), Cornell University, (2) 1-26. Retrieved from https://arxiv.org/ftp/arxiv/papers/1502/1502.03199.pdf
- Stergiou, N., Yu, Y., & Kyvelidou, A. (2013). A perspective on human movement variability with applications in infancy motor development. Kinesiology Review, 2, 93-102. doi:10.1123/krj.2.1.93
- Termoz, N., Lissajoux, K., & Slawinski, J. (2015). Kinetic analysis of different running starts: Impact on forward center of mass acceleration and performance. 33rd International Conference on Biomechanics in Sports, Poitiers, France, June 29 – July 3, 2015. F. Colloud, M. Domalain, & T. Monnet (Eds.). Retrieved from https://ojs.ub.uni-konstanz.de/cpa/article/view/6498
- Thelen, E. & Smith, L. B. (2005). Dynamic systems theories, Handbook, Chapter 6, 258-312. Retrieved from https://cogdev.sitehost.iu.edu/labwork/handbook.pdf
- Turvey, M. T., (1990). Coordination. American Psychologist, 45(8), 938-953. Retrieved from https://doi.org/10.1037/0003-066X.45.8.938
- Verhoeff, R. P., Knippels, M-C. P. J., Gilissen, M. G. R., & Boersma, K. T. (2018). The theoretical nature of systems thinking. Perspectives on systems thinking in biology education. Frontiers in Education, 3(40), 1-11. doi.org/10.3389/feduc.2018.00040
- Wenning, C. J. (2009). Scientific epistemology: How scientists know what they know. Journal of Physics Teacher Education Online, 5(2), 3-15. Retrieved from https://pdfs.semanticscholar.org/8854/19ef96c2a5abec87f7dd53dbd258f78b9427.pdf?_ga=2.120836744.1617789758.1581169402-1044859988.1572144347
- Whiteside, D., Deneweth, J., Pohorence, M. A., Sandoval, B., Russell, J. R., McLean, S. G., Zernicke, R. F., & Goulet, G. C. (2016). Grading the functional movement screen: A comparison of manual (real-time) and objective methods. Journal of Strength and Conditioning Research, 30(4), 924-933. Retrieved from https://pdfs.semanticscholar.org/e295/52e4da590cffadaa33cb7b48063577bc519f.pdf
- Wise, S. P., & Shadmehr, R. (2002). Motor control. In Vilayanur Ramachandran (Ed-in-Chief), Encyclopedia of the Human Brain (p.137-157). Elsevier Science. Retrieved from https://pdfs.semanticscholar.org/3032/af138c2fa5f7acedaa47bbe7f3e07f8162ec.pdf?_ga=2.75013522.1617789758.1581169402-1044859988.1572144347
- Wulf, G. (2013). Attentional focus and motor learning: a review of 15 years. International Review of Sport and Exercise Psychology, 6(1), 77-104. Retrieved from http://gwulf.faculty.unlv.edu/wp-content/uploads/2018/11/Wulf_AF_review_2013.pdf
- Zajac, F. E. (1993). Muscle coordination of movement: A perspective. Journal of Biomechanics, 26, Suppl. 1, 109-124. doi:10.1016/0021-9290(93)90083-q
The Observer—Performer Classification
Dealing with these epistemological questions to construct knowledge and understanding from the experiences derived with the interaction of the observer and the performer, the researchers begin a qualitative methodology with an Observer-Performer Classification (OPC) for grouping observed human movement skilled actions into one of four categories. OPC is a two-dimensional taxonomy that possesses two general characteristics; namely, an internal perception of motion experienced by the performer as preferred or not, and an external assessment of that performance as witnessed by the observer as acceptable or not. Just because a motor action is accepted as correct by the observer and preferred by the performer, does not make it true. Whatever is believed by the observer and the performer, the epistemology perspective asks how can they find out what can be known about such a perceptual experience shaping their belief systems as witnessed by the observer and felt by the performer. Briefly, an OPC-I is that the observer agreed with the preferred behavior of the performer, who now acknowledged feeling ready for practice with a given task where both are assumed to be correct towards a wellbeing experience and knowledge if performance is enhanced within a reasonable period of time; otherwise, it is reclassified OPC-IV. An OPC-II is observer disapproval assumed to be correct and performer approval is assumed to be incorrect. However, knowledge and a wellbeing experience are acquired by providing the performer with show-and-tell information that results with enhanced performance as a reclassified OPC-I; otherwise, it is reclassified OPC-IV. It is assumed that the performer possesses the coordinative dynamics that is capable of a different kinematic motor action; however, the performer has yet to experience it because of not knowing what to control kinematically. An OPC-III is observer approval assumed to be correct and performer disapproval is assumed to be incorrect. However, knowledge and a wellbeing experience are acquired by providing the performer with drill-&-feel tactics that result with enhanced performance as a reclassified OPC-I; otherwise, it is reclassified OPC-IV. It is assumed that the performer does not possess the coordinative dynamics concomitant with the requisite flexibility and biological requirements that are necessary and sufficient for the motor action. Caution is advised with OPC-III value judgments that in reality are OPC-IV because the observer is wrong when there is: 1) a physiological or a psychological deficit, 2) an undiagnosed injury or microtrauma, 3) a compensatory action that disguises error, 4) functional variability that is mistaken as error, or 5) an unqualified, inexperienced, or biased observer for a given action or task. An OPC-IV is disapproval that is assumed to be correct by both the observer and the performer. However, both knowledge about what the body senses and learns (22), as well as, a wellbeing experience (8,18) are acquired by providing the performer with FMS prerequisites (e.g., MJM; dFMS) that result with enhanced performance as a reclassified OPC-I; otherwise, research is recommended. The researchers argued that the FMS prerequisites identify hidden skills, optimize momentum, utilize gravity, and explore the notion of worst first move to avoid misuse, overuse, or abuse with respect to injury or poor performance by moving toward optimal health biomechanically and epistemologically. Caution is advised with OPC-IV when a medical referral or other prerequisites (e.g.; nutrition, physiology; psychology) are warranted.
OPC assignments and their key epistemic assumptions for classifying a given motor action is the process by which it can be discovered: how do you know what you know? This approach deals with the scientific epistemological question regarding the nature of the value-judgment relationship between the observer and what can be known from what is observed and how the researchers teach and study this by interacting with what the performer senses and believes that may be true or false. Physical education teachers, coaches, athletic trainers, physical therapists, sports medicine doctors are among those professionals faced with injuries or poor performances at all skill levels. When observing any skill level, especially the Olympic or professional athlete, they are challenged by the dilemma as to what observations provide meaningful information to the athlete. The researchers illustrate this epistemic framework with some FMS prerequisites to discover preferred behaviors that reveal hidden skills for running and squatting with an original study using these four independent tasks of different dimensions as a method to reveal what the researchers know that offers translating practice into theory via these unique FMS prerequisites; namely, MJM and dFMS.
The dynamical systems perspective
Skilled FMS actions like running involving these biological systems compose many elements that require both control and coordination. Biological systems involve coordinating many different component parts that consist of about 100 mechanical degrees of freedom, characterizable by position and velocity, yield a state space of, at least, 200 dimensions (41). Hundreds of muscles operating as linear actuators (47) enable movements and maintain postures involving kinetic and potential energies within a complex, interactive neuro-musculo-skeletal system. Fundamental to position and velocity are the generalized coordinates that define the configuration space of the physical system often modeled as a particle system that represents the whole (i.e.; center of mass of the body), and the concept of self-organization. Dynamical systems emerge without prespecification where the patterns organize themselves in a sequence of complexity to simplicity to complexity (40). Running’s complexity is low because it possesses a simplicity from which a vast number of patterns can be randomly selected and organized by very young children; yet its potential to evolve optimally remains unpredictable. Therefore, can a system’s preferred behaviors (see Task 1) be reduced to a lesser complexity of highly predictable FMS prerequisites to reveal hidden skills with MJM (see Task 2) to identify key configurations and then explain behavior? Can deficient hidden skills be revealed for injury risk assessments and performance readiness evaluations with tactics as dFMS (see Tasks 3 & 4) of greater complexity, yet fairly predictable to skill development of a whole system (e.g., a runner)?
The researchers use dynamical systems concepts to provide systems thinking in physical education that may translate from practice into theory. The complexity and the multi-dimensionality of the sensory-motor system solve Bernstein’s degrees of freedom problem (3,41) by temporary assemblages, which are coordinative structures of muscle complexity such that skill involves adding and removing constraints that reduce and increase the degrees of freedom, respectively.
The dynamical systems theory perspective describes transitional relationships among the parts of a whole while it fundamentally struggles with two major qualitative problems involving the chaotic behavior of a system (e.g., a performer): 1) discovering steady states of the system and 2) assessing the trust of the system to rely on its initial conditions (e.g., segmental position and its velocity). For the system to discover a steady state, the dynamic system theoretical argument claims the system is attracted from a nearby state to a steadier state. The more complex the system, the more difficult it is to assess its dependence on initial conditions. Modeling complex systems fundamentally strive to resolve collective behaviors that emerge from the relationship of their component parts and their environment during a state of flux.
Modeling a system undergoing transitions often requires mathematically solving an initial value problem (IVP) where the differential equation is an evolution equation specifying how the system evolves over time given its initial conditions of a movement function. Often, these conditions are the position of a configured system and the initial velocities of each configured part. If these conditions are known, then the IVP answers the question of aim in order how to hit the target. However, if the initial conditions are unknown, then the differential equations are solved as boundary value problems (BVP) with a set of additional constraints, called the boundary conditions. The BVP answers the question: Does aiming from a given starting point hit the target? Either an assumed starting point or target could be erroneous, resulting in no solution.
A solution for a differential equation is neither a single value nor necessarily a single solution. In fact, it can have multiple solutions, as well as no solution. If there is a solution, it is a function, which is a rule regarding inputs that are the initial conditions or boundary conditions of an IVP or a BVP, respectively. If the initial conditions of the IVP are unknown (see Task-1), then given a two-point BVP with boundary conditions, the problem is likely to have a solution within a continuous domain provided all the inputs are available to perform the output. This means there is a movement function that indicates how to perform an act that is constrained to behave like the idealization of a mechanism (see Task-2). Constraining a 2-joint system to behave like a robotic MJM provides a means to construct known configurations (i.e., coordinative structures) of reduced complexity and a biomechanical-marker (BMM) for dFMS of greater complexity from which to identify the hypothetical worst first move or a structural problem. The objective of this process is to reveal and self-discover hidden skills with MJM and dFMS that evaluate and organize a steady state of stability and adaptability to learn and perform well during starting a movement (see Task-3), and during the dynamic stability of the motion itself to discriminate perceptive capability clinically (see Task-4).
In the dynamic perspective, some control parameter must be changing in order for the stability of an existing organized system to be disrupted, eliminated, and replaced by a new organization. The key is to identify one or more of the appropriate collective variables that identify a few modes of preferred behavior (i.e., the stable attractor states) of the system. Worthy information about hopeful collective variables or a potential BMM is attained when the system shifts abruptly from one coordinative mode to another: that is, from one attractor state to another. Upon identifying the collective variables, these phase transitions are seen as qualitative changes in behavior where order loses one preferred behavior and regains a more preferred behavior or a worthy behavior. A behavior that is worthy may not be an ideal solution of the precision robot, but rather a realistic solution that best fits a restricted state of one’s intrinsic dynamics.
The Visual Search
The researchers conducted a YouTube visual search of Olympic sprinters as illustrated in Figure 8 with key video screenshots in accordance with the Fair Use Act for educational and research purposes. Video evidence of whole-foot contact were seen for the 100-meter world records of Jesse Owens (Figure 8A) in 1936 Olympic Games and Usain Bolt (Figure 8B) in 2009; as well as, Wayde Van Niekerk’s world records for the 2016 Olympic Games in the 400 meter race (Figure 8C) and for his 300-meter sprint (Figure 8D) at the 2017 IAAF World Challenge.
From the perspective of the visual search, the researchers clinically investigated the controversial notion that sprinting only impacts the forefoot of an Olympic sprinter. Whole-foot contact occurs in a few milliseconds as seen with Olympian Wayde van Niekerk (Figure 8D) and maintained prior to heel-lift (Figure 8E). This observation is further complicated because heel contact with the ground is considered a sprinting error; however, this visual search is empirical evidence, which in theory is the inverse approach where body configurations can be used both clinically and theoretically as input but only at a discrete set of target times and not the entire motion. Mathematically, each phase of motion can be solved independently as separate 2-point BVP solutions that are concatenated to describe the motor task (beyond the scope of this paper). Whole-foot contact with evidence of the BMM is hypothesized exploiting dynamic stability of an effective propulsive force to properly direct and ideally optimize momentum. These screenshots of van Niekerk’s 300 world record are empirical evidence of the BMM at whole-foot contact, which the researchers hypothesize optimizes the momentum-step; thereby, increasing his step length. This hypothesis is somewhat supported by a descriptive analysis investigating stride length comparing van Niekerk to Michael Johnson, the former 300-meter and 400-meter record holder, showed that van Niekerk’s 163 steps averaged 23.8 cm more per step than Johnson’s 180.5 steps (35).