Search Results

Control of static posture is constrained by multiple sensory inputs, motor ability, and task constraints. Development of static postural control across the lifespan can be analyzed effectively using nonlinear analyses of center of pressure (CoP) time series, including approximate and sample entropy. In this paper, the key findings from studies using nonlinear analysis tools are reviewed to describe the development of postural control. Preschool children learn to adopt relatively unstable postures (e.g., standing) in which the regularity of CoP initially increases as a consequence of restricting mechanical degrees of freedom. As children age, CoP regularity decreases as degrees of freedom are released, thus enabling a more functional, adaptable type of postural control. Changes to sensory inputs or task constraints also affect the regularity of CoP sway. For example, removing vision, adding vibration, or imposing dual-task conditions affect performer’s CoP regularity differently. One limitation of approximate and sample entropy analysis is the influence of different input parameters on the output and subsequent interpretation. Ongoing refinement to entropy analysis tools concern determining appropriate values for the length of sequence to be matched and the tolerance level used with CoP data.

There has been no direct attempt to evaluate whether gait performed overground and on a treadmill is the same for lower limb amputees. A multiple case study approach was adopted to explore the degenerate movement behavior displayed by three male amputees. Participants walked overground at a self-selected preferred pace and when this speed was enforced on a treadmill (50 stride cycles per condition). The extremities of motion (i.e., maximum flexion) for the hip and knee joints differed between conditions (0.2–3.8°). For two participants, the temporal asymmetry of gait was reduced on the treadmill. Initial data suggest that research on amputees simulating overground walking on a treadmill might need to be interpreted with some caution.

The present paper proposes a technical analysis method for extracting information about movement patterning in studies of motor control, based on a cluster analysis of movement kinematics. In a tutorial fashion, data from three different experiments are presented to exemplify and validate the technical method. When applied to three different basketball-shooting techniques, the method clearly distinguished between the different patterns. When applied to a cyclical wrist supination-pronation task, the cluster analysis provided the same results as an analysis using the conventional discrete relative phase measure. Finally, when analyzing throwing performance constrained by distance to target, the method grouped movement patterns together according to throwing distance. In conclusion, the proposed technical method provides a valuable tool to improve understanding of coordination and control in different movement models, including multiarticular actions.

From a nonlinear dynamics perspective, presence of movement variability before a change in preferred movement patterns is hypothesized to afford the necessary adaptability and flexibility for seeking novel functional behaviors. In this study, four novice participants practiced a discrete multiarticular movement for 12 sessions over 4 weeks. Cluster analysis procedures revealed how changes between preferred movement patterns were affected with and without the presence of variability in movement clusters before a defined change. Performance improved in all participants as a function of practice. Participants typically showed evidence of change between preferred movement clusters and higher variability in the use of movement clusters within a session. However, increasing variability in movement clusters was not always accompanied by transition from one preferred movement cluster to another. In summary, it was observed that intentional and informational constraints play an important role in influencing the specific pathway of change for individual learners as they search for new preferred movement patterns.

Validated assessment tools for movement competence typically involve the isolation and reproduction of specific movement forms, which arguably neglects individuals’ ability to combine and adapt movements to overcome constraints within a dynamic environment. A new movement assessment tool, the General Movement Competence Assessment (GMCA), was developed for this study using Microsoft Kinect. Movement competence of 83 children (36 boys and 47 girls), aged 8–10 years (9.06 ± 0.75 years) was measured using the GMCA. An exploratory approach was undertaken to examine the internal consistency reliability (McDonald’s omega coefficient) and factorial structure of the GMCA for the study sample. Factorial structure was determined using exploratory factor analysis by principal component analysis with varimax rotation. For the sample data, reliability for the GMCA games were acceptable (ω = 0.53–0.89) and indicated that combinations of movement attributes were measured by GMCA games. Factorial analysis extracted four movement constructs accounting for 71.31% of variance. Dexterity was tentatively identified as a new independent construct alongside currently accepted movement constructs (i.e., locomotion, object-control, stability). While further development of the GMCA is still required, initial results are encouraging in view of an objective and theoretically informed approach to assess general movement competence in children.

Purpose: To examine the movement and physiological demands of rugby union officiating in elite competition. Methods: Movement demands of 9 elite officials across 12 Super Rugby matches were calculated, using global positioning system devices. Total distance (in m), relative distance (in m·min⁻¹), and percentage time spent in various speed zones were calculated across a match. Heart-rate (HR) responses were also recorded throughout each match. Cohen d effect sizes were reported to examine the within-match variations. Results: The total distance covered was 8030 (506) m, with a relative distance of 83 (5) m·min⁻¹ and with no differences observed between halves. Most game time was spent at lower movement speeds (76% [2%]; <2.0 m·s⁻¹), with large effects for time spent >7.0 m·s⁻¹ between halves (d = 2.85). Mean HR was 154 (10) beats·min⁻¹ (83.8 [2.9]%HR_max), with no differences observed between the first and second halves. Most game time was spent between 81%HR_max and 90%HR_max (40.5% [7.5%]) with no observable differences between halves. Distances covered above 5.1 m·s⁻¹ were highest during the first 10 min of a match, while distance at speeds 3.7 to 5 m·s⁻¹ decreased during the final 10 min of play. Conclusions: These findings highlight the highly demanding and intermittent nature of rugby union officiating, with only some minor variations in physical and physiological demands across a match. These results have implications for the physical preparation of professional rugby union referees.