CNN-Based Intention Recognition Using Body-Worn Inertial Measurement Units

This study explores the potential of using a sensor system and deep learning algorithm for automatic recognition and classification of human movement intentions to enhance the control of active orthopedic aids such as orthoses, prostheses, and exoskeletons. The sensor system consists of four inertial measurement units (IMU) attached to the thighs, lower back, and chest of a person, measuring acceleration and rotational velocity in three axes each. A dataset was generated from 20 healthy individuals performing various movements from a standing position, and preprocessed and segmented into time windows. The data was fed into a convolutional neural network (CNN) capable of classifying the time windows into seven classes. The network achieved a classification accuracy of up to 82 %. The results demonstrate the potential of the proposed system for successful application in the control of assistance systems, which can improve the operability and effectiveness of orthopedic aids.