LSTM Network-Based Estimation of Ground Reaction Forces During Walking in Stroke Patients Using Markerless Motion Capture System

We propose a novel approach for estimating ground reaction forces (GRFs) during walking in stroke patients using a markerless motion capture (MMC) system, specifically the Azure Kinect, and a long short-term memory (LSTM) network. GRFs are crucial indicators of walking ability, but their measurement typically requires force plates, which are not readily available in clinical settings. Our study aimed to assess the feasibility of applying artificial neural networks (ANNs) to estimate GRFs in stroke patients using MMC. Our findings demonstrate that the estimated GRFs can serve as reliable clinical indicators of gait ability, with comparable estimation error in the vertical direction for both healthy individuals and stroke patients (L/R: 10.39/9.88% and P/H: 10.70/10.06%). The proposed neural-network-based approach to GRF estimation is more accessible and cost-effective than traditional force plate measurements and has the potential to enhance the development of personalized rehabilitation programs for stroke patients. This research fills a critical gap in the field of medical robotics, providing a practical and innovative method for assessing gait quality, planning, and monitoring rehabilitation in stroke patients.