A Block-Wised and Sparsely-Connected ANN for the Prediction of Human Joint Moment Based on the Improved Hill Musculoskeletal Model

Abstract

Human joint moment plays an important role in rehabilitation assessment and human-robot interaction, which cannot be measured directly but can only be predicted via indirect measurement by an artificial neural network (ANN). However, most existing ANN models for human joint moment prediction use fully-connected network which has complex structure and no inclusion of domain knowledge. Thus, this study introduced a novel block-wised and sparsely-connected ANN model (BSANN) for human joint moment prediction, which significantly reduced the computational and storage costs. In this BSANN model, by using an improved Hill musculoskeletal (HMS) model, a single-output fully-connected network was established as a block to take each electromyograph (EMG) signal for the prediction of the muscle moment, and all muscle moments were connected together as inputs to obtain the joint moment. Compared to the ANN, our BSANN model decreased 80.7% connections and keeps good prediction accuracy. It provides embedded portable systems a powerful tool to predict joint moment.