Functional human movement assessment using elastic fabric tape sensors and deep learning

Shih-Chao Huang; Xinlun Zhao; Yun-An Lin; Kenneth J. Loh

doi:10.1117/12.2618734

8 June 2022 Functional human movement assessment using elastic fabric tape sensors and deep learning

Shih-Chao Huang, Xinlun Zhao, Yun-An Lin, Kenneth J. Loh

Proceedings Volume PC12123, Smart Biomedical and Physiological Sensor Technology XIV; PC121230B (2022) https://doi.org/10.1117/12.2618734
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

A self-adhesive, elastic fabric, nanocomposite skin-strain sensor (called Motion Tape) has been developed, tested in controlled laboratory environments, and validated through human subject studies. This study aimed to interpret Motion Tape data using deep learning methods to directly predict functional movement parameters (e.g., joint angles and limb positions) and verifying the results using optical motion capture. The approach was to obtain human participant Motion Tape testing data and training the datasets using ground truth values acquired from the optical motion capture system. Predictions of muscle engagement, strain, and range-of-movement of major joints were investigated to validate the proposed methods.

Conference Presentation

Citation Download Citation

Shih-Chao Huang, Xinlun Zhao, Yun-An Lin, and Kenneth J. Loh "Functional human movement assessment using elastic fabric tape sensors and deep learning", Proc. SPIE PC12123, Smart Biomedical and Physiological Sensor Technology XIV, PC121230B (8 June 2022); https://doi.org/10.1117/12.2618734

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