HuMAn - the Human Motion Anticipation Algorithm Based on Recurrent Neural Networks

Predicting human motion may lead to considerable advantages for human-robot interaction, particularly when precise synchronization between the robot's motion and the user's movement is imperative. The inherent stochastic nature of human behavior, combined with the restricted window of response, can give rise to residual and undesirable forces during interactions, potentially harming the user. Therefore, efficient prediction of human joint movements may enhance the performance of various interaction control frameworks used in wearable robots. This paper proposes the HuMAn algorithm for predicting human joint motion based on a recurrent neural network. This algorithm consists of a long-term memory network, used to interpret sequences of poses, and a prediction layer, employed to build the most likely future user poses within a specified time horizon. Network training was performed using datasets encompassing various subjects and types of motion. The results demonstrate the effectiveness of the proposed algorithm, as evidenced by average general prediction errors below 0.1 radians for predictive horizons of up to 500 milliseconds. Furthermore, a mean absolute error of 0.026 radians was achieved for a periodic treadmill walk. Simulation results demonstrate a large improvement in transparency control performance in a case study with an upper limb exoskeleton robot.