Multiple Inertial Measurement Unit Combination and Location for Center of Pressure Prediction in Gait

Frontiers in Bioengineering and Biotechnology
Chao-Che WuYun-Ju Lee

Abstract

Center of pressure (COP) during a gait cycle indicates crucial information with regard to fall risk such as balance capacity. The drawbacks of conventional research instruments include inconvenient use during activities of daily living and expensive costs. The present study illustrates the promising fall-relevant information predicted by acceleration and angular velocity data from different placement sensors with machine learning techniques. This approach is inspired by the emerging machine learning technique, specifically the long short-term memory (LSTM), which is often used in time series data and aims to decrease the burden of the user while using the novel wearable technology. The Jaccard similarity coefficient, which implies the consistency of profile alignment between prediction and real situation, achieved 94% accuracy in the walking direction. Furthermore, the number of sensors used and the placement influenced the feasibility of an application. The outcome revealed that the accuracy could exceed 90% with only one sensor placed on the foot in the walking direction, and the toe would be the best location for sensor placement. To examine the performance of machine learning, the current study employed two parameters from ...Continue Reading

References

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May 16, 2000·Journal of Biomechanics·A Hreljac, R N Marshall
Aug 10, 2000·Journal of the American Podiatric Medical Association·M W Cornwall, T G McPoil
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May 13, 2008·Gait & Posture·Paola CatalfamoDavid Ewins
Aug 14, 2012·Journal of Biomechanics·Chand T JohnScott L Delp
May 14, 2016·PLoS Computational Biology·Jared Markowitz, Hugh Herr

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