landing, and the mediolateral and anteroposterior accelerations were then plotted to produce a postural sway plot. The path length of this sway trace was calculated from the sum of the difference between 2 sequential data points (sample ( x + 1) − sample ( x )). Therefore, to quantify hop landing
Jonathan M. Williams, Michael Gara, and Carol Clark
Olivier Caron, Thierry Gélat, Patrice Rougier, and Jean-Pierre Blanchi
The center of foot pressure (CP) motions, representing the net neuromuscular control, was compared to the center of gravity (CG) motions, representing the net performance. The comparison focused on the trajectory path length parameter along the mediolateral and antero-posterior axes because these two variables depend on amplitude versus frequency relationship. This relationship was used to evaluate the CG motions based on the CP motions. Seven subjects stood still on a force plate with eyes open and eyes closed. The results showed that the ratio of (CP – CG)/CP trajectory path length was personal for each subject. These results suggest different levels of passive (ligaments, elastic properties) and active (reflex activity) stiffness. For some subjects, this ratio was significantly lower for the eyes open condition than for the eyes closed condition, indicating a decrease of the active stiffness for the eyes open condition. Therefore, a CG – CP comparative analysis appeared helpful in understanding the control of balance and necessary to quantify the subjects’ net performance.
Ryan Morrison, Kyle M. Petit, Chris Kuenze, Ryan N. Moran, and Tracey Covassin
force plate that provides an objective balance assessment via estimation of COP path length. 7 Preliminary investigation of the BTrackS has yielded a specificity of 0.90 in concussed college athletes. 8 The BTrackS has also displayed good-to-excellent test–retest reliability when participants were
Hananeh Younesian, Thomas Legrand, Ludovic Miramand, Sarah Beausoleil, and Katia Turcot
, loading rate ratio, foot flat ratio, push-up ratio) and other gait parameters (eg, speed, cadence, step length, path length, minimum toe clearance) have been evaluated using IMUs in different populations such as the elderly, multiple sclerosis, osteoarthritis, and iLLA to analyze their walking performance. 20
Ilha G. Fernandes, Matheus A. Souza, Matheus L. Oliveira, Bianca Miarka, Michelle A. Barbosa, Andreia C. Queiroz, and Alexandre C. Barbosa
. The following COP parameters were extracted from the raw data by using the Explore Balance software (Balance Tracking System): path length (total sway length in centimeters), mean velocity (path length divided by trial duration in centimeter per second), mean distance from the center (the average
Morteza Sadeghi, Gholamali Ghasemi, and Mohammadtaghi Karimi
the excursion of the center of pressure (COP) in anteroposterior (AP) plane, the excursion of center of pressure in the mediolateral (ML) plane, the path length of center of pressure in AP plane, the path length of center of pressure in ML plane, the velocity of center of pressure in the AP plane, and
Vincent Shieh, Ashwini Sansare, Minal Jain, Thomas Bulea, Martina Mancini, and Cris Zampieri
were exported from the NeuroCom research module as de-identified text files. The following parameters of interest were calculated offline: sway area, sway velocity, and path length. We used a custom-made MATLAB script (r2017a, The MathWorks Inc.). Mancini et al. ( 2012 ) describes details of the exact
Eryk P. Przysucha and M. Jane Taylor
The purpose of this study was to compare the postural sway profiles of 20 boys with and without Developmental Coordination Disorder (DCD) on two conditions of a quiet standing task: eyes open and eyes closed. Anterior-posterior (AP) sway, medio-lateral sway (LAT), area of sway, total path length, and Romberg’s quotient were analyzed. When visual information was available, there was no difference between groups in LAT sway or path length. However, boys with DCD demonstrated more AP sway (p < .01) and greater area of sway (p < .03), which resulted in pronounced excursions closer to their stability limits. Analysis of Romberg’s quotient indicated that boys with DCD did not over-rely on visual information.
Eryk P. Przysucha, M. Jane Taylor, and Douglas Weber
This study compared the nature of postural adaptations and control tendencies, between 7 (n = 9) and 11-year-old boys (n = 10) with Developmental Coordination Disorder (DCD) and age-matched, younger (n = 10) and older (n = 9) peers in a leaning task. Examination of anterior-posterior, medio-lateral, maximum and mean area of sway, and path length revealed one significant interaction as older, unaffected boys swayed more than all other groups (p < .01). As a group, boys with DCD displayed smaller anterior-posterior (p < .01) and area of sway (p < .01). Analysis of relative time spent in the corrective phase (p < .002) revealed that boys with DCD spent 54% under feedback control while boys without DCD spent 78%. This was attributed to reduced proprioceptive sensitivity, as confirmed by significant differences between the groups (p < .009) in spectral analysis of peak frequency of sway.
Weimo Zhu, Zorica Nedovic-Budic, Robert B. Olshansky, Jed Marti, Yong Gao, Youngsik Park, Edward McAuley, and Wojciech Chodzko-Zajko
To introduce Agent-Based Model (ABM) to physical activity (PA) research and, using data from a study of neighborhood walkability and walking behavior, to illustrate parameters for an ABM of walking behavior.
The concept, brief history, mechanism, major components, key steps, advantages, and limitations of ABM were first introduced. For illustration, 10 participants (age in years: mean = 68, SD = 8) were recruited from a walkable and a nonwalkable neighborhood. They wore AMP 331 triaxial accelerometers and GeoLogger GPA tracking devices for 21 days. Data were analyzed using conventional statistics and highresolution geographic image analysis, which focused on a) path length, b) path duration, c) number of GPS reporting points, and d) interaction between distances and time.
Average steps by subjects ranged from 1810−10,453 steps per day (mean = 6899, SD = 3823). No statistical difference in walking behavior was found between neighborhoods (Walkable = 6710 ± 2781, Nonwalkable = 7096 ± 4674). Three environment parameters (ie, sidewalk, crosswalk, and path) were identified for future ABM simulation.
ABM should provide a better understanding of PA behavior’s interaction with the environment, as illustrated using a real-life example. PA field should take advantage of ABM in future research.