sEMG-Based Hand Movement Regression by Prediction of Joint Angles With Recurrent Neural Networks.

This work takes a step towards a better biosignal based hand gesture recognition by investigating the strategies for a reliable prediction of hand joint angles. Those strategies are especially important for medical applications in order to achieve e.g. good acceptance of hand prostheses among amputees. A recurrent neural network with a small footprint is deployed to estimate the joint positions from surface electromyography data measured at the forearm. As the predictions are expected to be not smooth, different post processing methods and a regularisation term for the objective function of the network are proposed. The experiments were conducted on publicly available databases. The results reveal that both post processing strategies and regularisation have a positive impact on the results with a maximal relative improvement of 6.13 %. On the one hand post processing strategies introduce an additional delay, consequently, the improvement is analysed in context of the caused delay. On the other hand the regularisation strategy does not cause a delay and can be adjusted easily to cope with different ground truths or compensate for certain problems in the hand tracking.

Regression of Hand Movements from sEMG Data with Recurrent Neural Networks.

Most wearable human-machine interfaces concerning hand movements only focus on classifying a limited number of hand gestures. With the introduction of deep learning, surface electromyography based hand gesture classification systems improved drastically. Therefore, it is worth investigating whether the classification can be replaced by a movement regression of all the different movable hand parts. As recurrent neural networks based approaches have proven their abilities of solving the classification problem we also choose them for the regression problem. Experiments were conducted with multiple different network architectures on several databases. Furthermore, due to the lack of a reliable measure to compare different gesture regression approaches we propose an interpretable and reproducible new error measure that can even handle noisy ground truth data. The results reveal the general possibility of regressing detailed hand movements. Even with the relatively simple networks the hand gestures can be regressed quite accurately.