title: 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.
authors.affiliation: Human Movement Biomechanics Research Group, Department of Kinesiology, KU Leuven, Leuven, Belgium; Measure, Model & Manage Bioresponses (M3-BIORES) Group, Department of Biosystems, KU Leuven, Leuven, Belgium; Movement Laboratory, Department of Sport Science, Stellenbosch University, Stellenbosch, Western Cape, South Africa.
authors.forename: Kurt H
authors.initials: KH
authors.lastname: Schütte
authors.affiliation: Human Movement Biomechanics Research Group, Department of Kinesiology, KU Leuven, Leuven, Belgium.
authors.forename: Ellen A
authors.initials: EA
authors.lastname: Maas
authors.affiliation: Measure, Model & Manage Bioresponses (M3-BIORES) Group, Department of Biosystems, KU Leuven, Leuven, Belgium.
authors.forename: Vasileios
authors.initials: V
authors.lastname: Exadaktylos
authors.affiliation: Measure, Model & Manage Bioresponses (M3-BIORES) Group, Department of Biosystems, KU Leuven, Leuven, Belgium.
authors.forename: Daniel
authors.initials: D
authors.lastname: Berckmans
authors.affiliation: Movement Laboratory, Department of Sport Science, Stellenbosch University, Stellenbosch, Western Cape, South Africa.
authors.forename: Rachel E
authors.initials: RE
authors.lastname: Venter
authors.affiliation: Human Movement Biomechanics Research Group, Department of Kinesiology, KU Leuven, Leuven, Belgium.
authors.forename: Benedicte
authors.initials: B
authors.lastname: Vanwanseele
mesh_terms: D000054:Acceleration; D000293:Adolescent; D000328:Adult; D001696:Biomechanical Phenomena; D005221:Fatigue; D006801:Humans; D009038:Motion; D012420:Running / Q000502:physiology*; D060726:Torso / Q000502:physiology; D059015:Wireless Technology