Literature DB >> 18617177

A three-pressure-sensor (3PS) system for monitoring ankle supination torque during sport motions.

Daniel Tik-Pui Fong1, Yue-Yan Chan, Youlian Hong, Patrick Shu-Hang Yung, Kwai-Yau Fung, Kai-Ming Chan.   

Abstract

This study presented a three-pressure-sensor (3PS) system for monitoring ankle supination torque during sport motions. Five male subjects wore a pair of cloth sport shoes and performed 10 trials of walking, running, cutting, vertical jump-landing and stepping-down motions in a random sequence. A pair of pressure insoles (Novel Pedar model W, Germany) was inserted in the shoes for the measurement of plantar pressure at 100Hz. The ankle joint torque was calculated by a standard lower extremity inverse dynamic calculation procedure with the data obtained by a motion capture system (VICON, UK) and a force plate (AMTI, USA), and was presented in a supination/pronation plane with an oblique axis of rotation at the ankle joint. Stepwise linear regression analysis suggested that pressure data at three locations beneath the foot were essential for reconstructing the ankle supination torque. Another group of five male subjects participated in a validation test with the same procedure, but with the pressure insoles replaced by the 3PS system. Estimated ankle supination torque was calculated from the equation developed by the regression analysis. Results suggested that the correlation between the standard and estimated data was high (R=0.938). The overall root mean square error was 6.91Nm, which was about 6% of the peak values recorded in the five sport motions (113Nm). With the good estimation accuracy, tiny size and inexpensive cost, the 3PS system is readily available to be implanted in sport shoe for the estimation and monitoring of ankle supination torque during dynamic sport motions.

Mesh:

Year:  2008        PMID: 18617177     DOI: 10.1016/j.jbiomech.2008.05.035

Source DB:  PubMed          Journal:  J Biomech        ISSN: 0021-9290            Impact factor:   2.712


  2 in total

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Authors:  Yi Li; Ji Wu; Chao Zheng; Rong Rong Huang; Yuhong Na; Fan Yang; Zengshun Wang; Di Wu
Journal:  J Sports Sci Med       Date:  2013-09-01       Impact factor: 2.988

2.  Foot Strike Angle Prediction and Pattern Classification Using LoadsolTM Wearable Sensors: A Comparison of Machine Learning Techniques.

Authors:  Stephanie R Moore; Christina Kranzinger; Julian Fritz; Thomas Stӧggl; Josef Krӧll; Hermann Schwameder
Journal:  Sensors (Basel)       Date:  2020-11-25       Impact factor: 3.576

  2 in total

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