An Auto-Calibration Approach to Robust and Secure Usage of Accelerometers for Human Motion Analysis in FES Therapies

A Functional Electrical stimulation (FES) therapy is a common rehabilitation intervention after stroke, and finite state machine (FSM) has proven to be an effective and intuitive FES control method. The FSM uses the data information generated by the accelerometer to robustly trigger state transitions. In the medical field, it is necessary to obtain highly safe and accurate acceleration data. In order to ensure the accuracy of the acceleration sensor data without affecting the accuracy of the motion analysis, we need to perform acceleration big data calibration. In this context, we propose a method for robustly calculating the auto-calibration gain using redundant acceleration vectors, and then calibrating the data generated by the accelerometer based on the calculated gain. The selection of the acceleration vector involved in the gain calculation is demonstrated by different experiments. The results show that the auto-calibration gain calculated after calibration is very close to 1, and the error is significantly less than before calibration, which indicates that the accelerometer unit is well calibrated