Estimating lower extremity joint angles during gait using reduced number of sensors count via deep learning

Md Sanzid Bin Hossain; Hwan Choi; Zhishan Guo

doi:10.1117/12.2643786

12 October 2022 Estimating lower extremity joint angles during gait using reduced number of sensors count via deep learning

Md Sanzid Bin Hossain, Hwan Choi, Zhishan Guo

Author Affiliations +

Proceedings Volume 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022); 123423P (2022) https://doi.org/10.1117/12.2643786
Event: Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 2022, Wuhan, China

Abstract

Estimating lower extremity joint angle during gait is essential for biomechanical analysis and clinical purposes. Traditionally infrared light-based motion capture systems are used to get the joint angle information. However, such an approach is restricted to the lab environment, limiting the applicability of the method in daily living. Inertial Measurement Units (IMU) sensors can solve this limitation but are needed in each body segment, causing discomfort and impracticality in everyday living. As a result, it is desirable to build a system that can measure joint angles in daily living while ensuring user comfort. For this reason, this paper uses deep learning to estimate joint angle during gait using only two IMU sensors mounted on participants' shoes under four different walking conditions, i.e., treadmill, overground, stair, and slope. Specifically, we leverage Gated Recurrent Unit (GRU), 1D, and 2D convolutional layers to create sub-networks and take their average to get a final model in an end-to-end manner. Extensive evaluations are done on the proposed method, which outperforms the baseline and improves the Root Mean Square Error (RMSE) of joint angle prediction by up to 32.96%.

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Md Sanzid Bin Hossain, Hwan Choi, and Zhishan Guo "Estimating lower extremity joint angles during gait using reduced number of sensors count via deep learning", Proc. SPIE 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 123423P (12 October 2022); https://doi.org/10.1117/12.2643786

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