Estimation of Upper Limb Dynamic Interaction Force Based on Multimodal Information.

This paper investigates methods for estimating interaction forces during the dynamic process of upper limb elevation. By collecting data from two modalities- electromyographic (EMG) signals of the unilateral forearm and joint angles-along with synchronized interaction force data, a deep learning methodology merging convolutional neural network and long short-term memory network (CNN-LSTM) is adopted to generate a predictive model for characterizing dynamic interactive force, ultimately achieving the task of dynamic force estimation. The comparative analysis of estimation performance using two types of data, EMG signals and EMG-inertial measurement unit (IMU) signals, along with the performance comparison between the CNN-LSTM model and support vector regression (SVR) model for the dynamic force estimation task, demonstrates the advantages of multimodal data and the CNN-LSTM model in facilitating the estimation of dynamic interaction forces in the upper limb.