Wireless Tri-Axial Trunk Accelerometry Detects Deviations in Dynamic Center of Mass Motion Due to Running-Induced Fatigue

Abstract

Small wireless trunk accelerometers have become a popular approach to unobtrusively quantify human locomotion and provide insights into both gait rehabilitation and sports performance. However, limited evidence exists as to which trunk accelerometry measures are suitable for the purpose of detecting movement compensations while running, and specifically in response to fatigue. The aim of this study was therefore to detect deviations in the dynamic center of mass (CoM) motion due to running-induced fatigue using tri-axial trunk accelerometry. Twenty runners aged 18–25 years completed an indoor treadmill running protocol to volitional exhaustion at speeds equivalent to their 3.2 km time trial performance. The following dependent measures were extracted from tri-axial trunk accelerations of 20 running steps before and after the treadmill fatigue protocol: the tri-axial ratio of acceleration root mean square (RMS) to the resultant vector RMS, step and stride regularity (autocorrelation procedure), and sample entropy. Running-induced fatigue increased mediolateral and anteroposterior ratios of acceleration RMS (p < .05), decreased the anteroposterior step regularity (p < .05), and increased the anteroposterior sample entropy (p < .05) of trunk accelerometry patterns. Our findings indicate that treadmill running-induced fatigue might reveal itself in a greater contribution of variability in horizontal plane trunk accelerations, with anteroposterior trunk accelerations that are less regular from step-to-step and are less predictable. It appears that trunk accelerometry parameters can be used to detect deviations in dynamic CoM motion induced by treadmill running fatigue, yet it is unknown how robust or generalizable these parameters are to outdoor running environments.

Energy cost of running instability evaluated with wearable trunk accelerometry

“…ning economy when accelerations were left unfiltered. Sample entropy is becoming an increasingly popular complexity measure to capture both performance-related (32,40) and pathologically related (47) nonlinear dynamics of human gait. Unexpectedly, lower sample entropy ML values, i.e., less complexity, were related with a costlier running gait (Fig.…”

“…2). Standardizing acceleration epochs to amount of running steps as opposed to time windows was done to allow crossstudy comparison (39,40).…”

“…Dynamic stability parameters were extracted from each acceleration axis (vertical, ML, and AP) and quantified first, using both absolute and ratio of each linear acceleration axis RMS relative to the resultant vector RMS to capture movement variability (26,42); second, using step regularity (interstep symmetry) and stride regularity using the unbiased autocorrelation procedure, with perfect regularities equivalent to one (27); and third, using sample entropy to capture the waveform predictability, with values typically in range of 0 to 2 for physiological systems and higher values indicating less periodicity or more unpredictability (36). Detailed procedures and algorithm inputs for the computation and extraction of these dynamic stability parameters are the same as previously explained (40). Spatiotemporal parameters including step frequency (27,39,40) and contact time (13, 39) were additionally computed from vertical trunk accelerations.…”

“…Detailed procedures and algorithm inputs for the computation and extraction of these dynamic stability parameters are the same as previously explained (40). Spatiotemporal parameters including step frequency (27,39,40) and contact time (13, 39) were additionally computed from vertical trunk accelerations.…”

“…Poor trunk coordination could therefore increase energetic cost via larger changes in horizontal momentum (15). In partial support, trunk accelerations in the ML and AP direction have shown to increase due to running induced fatigue (24,40).…”