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Critical Reviews™ in Biomedical Engineering
SJR: 0.26 SNIP: 0.375 CiteScore™: 1.4

ISSN Imprimir: 0278-940X
ISSN En Línea: 1943-619X

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Critical Reviews™ in Biomedical Engineering

DOI: 10.1615/CritRevBiomedEng.2014011728
pages 451-466

A Bidirectional Model of Postural Sway Using Force Plate Data

Karla D. Bustamante Valles
Marquette University, Orthopaedic and Rehabilitation Engineering Center (OREC), 735 N. 17th Street, Suite 105, Milwaukee, WI 53233; Instituto Tecnologico y de Estudios Superiores de Monterrey (ITESM), Chihuahua Campus, Chihuahua, Mexico
Ubong I. Udoekwere
Marquette University, Orthopaedic and Rehabilitation Engineering Center (OREC), 735 N. 17th Street, Suite 105, Milwaukee, WI 53233
Jason T Long
Marquette University, Orthopaedic and Rehabilitation Engineering Center (OREC), 735 N. 17th Street, Suite 105, Milwaukee, WI 53233
Jennifer M. Schneider
Marquette University, Orthopaedic and Rehabilitation Engineering Center (OREC), 735 N. 17th Street, Suite 105, Milwaukee, WI 53233
Susan A. Riedel
Marquette University, Orthopaedic and Rehabilitation Engineering Center (OREC), 735 N. 17th Street, Suite 105, Milwaukee, WI 53233
Gerald F. Harris
Department of Orthopaedic Surgery, Medical College of Wisconsin, Milwaukee, WI; Orthopaedic and Rehabilitation Engineering Center (OREC), Department of Biomedical Engineering, Marquette University, Milwaukee, WI

SINOPSIS

This work was designed to expand on our previous anterior-posterior postural control model to include medial-lateral sway of unperturbed posture during quiet standing. The bidirectional model simulates two decoupled inverted pendulums, each restricted to sway in either the anterior-posterior (AP) direction (ankle strategy) or medial-lateral (ML) direction (hip strategy), and each controlled by a Proportional-Integral-Derivative (PID) controller. Postural data was collected from 31 healthy participants under different sensory test conditions: eyes closed, eyes open, and eyes open with real-time visual feedback. Simulation iterations of the bidirectional model were run for each sensory test condition to adjust the PID controller parameters until modeled sway metrics did not differ significantly from experimental metrics at p ≤ 0.01. Simulations did not show significant changes in the AP sway controller parameters among the 3 sensory test conditions. The model did show significant changes in ML sway controller parameters, namely stiffness and time delay. Significant differences were also seen in the experimental sway metrics under the three different sensory test conditions. The multi-sensory evaluation and bidirectional sway model offer unique insight for further exploration of postural pathology, control mechanisms and planar coupling that includes both ankle and hip strategies.


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