A Soft Ground Reaction Force Sensor System Utilizing Time-Delay Recurrent Neural Network

This paper proposes a novel soft sensor system for gait phase analysis with its real-time ground reaction force sensing methodology utilizing Recurrent Neural Network. As the full ground contact of human feet is important for natural and stable walking, the soft sensor system embedded in a bendable foot module is required. The sensor system includes four soft sensor units placed in a rubber outsole. Each sensor unit was fabricated using elastic material, and it can measure exerted normal force through a simple capacitive sensing method. In order to measure accurate force with the sensor system, it is important to compensate nonlinearity and hysteresis inherited from elastic material properties. Recurrent Neural Network with time delays is adopted to be a suitable solution for compensating these undesirable characteristics due to its capability to handle the dynamic behavior of sequential data. The sensor units were calibrated based on the training results of Time-Delay Recurrent Neural Networks. {R}^{2} of sensor units is over than 0.998 and RMSE has dropped dramatically by 64%. The feasibility of the sensor system was validated throughout a real-time ground force measuring experiment. Four gait phases were successfully analyzed according to the data obtained.