A study on deep learning structure for real-time control of above-knee prosthesis using biomechanical data

Training data of the deep learning model for the control of the above-knee prosthesis should have a sufficient amount of data to fit with the model. Also, this data can be used for gait classification to control the prosthesis. However, in real-time control, the number of the gait dynamics data counted by the sensor is only measured to the level that the deep learning model is difficult to learn., In this study, the most efficient deep learning model case was developed to resolve this problem in the case of a real-time control situation where data measurement time is insufficient. The data is collected through a hall sensor, load cell and Inertial Measurement Unit (IMU) mounted on the above-knee prosthesis. Subsequently, the collected data is divided into 5 phases (Loading Response (LR), Mid Stance (MS), Push Off (PO), Early Swing (ES), and Late Swing (LS)) of the gait cycle according to the point of inflection of hall sensors and load cells. Afterward, training data of the deep learning were generated by sliding window algorithm and the treated data was exercised on four deep learning models by changing the value of width and depth configurations. The results are assessed on accuracy, loss and F1-Score. In conclusion, the loss function statistically decreased in the case of the values of width and depth of the deep learning model were low. Accuracy and F1-Score were not shown significant difference statistically. Therefore, the results provided an efficient deep learning model for above-knee prosthesis to gait analysis and expected to lead to a more reasonable gait model for the control of the above-knee prosthesis via a more detailed gait classification study in the future. © 2021, Institute of Control, Robotics and Systems. All rights reserved.