Reservoir Computing Model for Human Hand Locomotion Signal Classification

Human-movement recognition is a novel challenge in soft robotics. In recent years, there have been several attempts to develop soft wearable devices for supporting human-robot interfaces. Many algorithms and programming languages are available to integrate a wearable device with a soft robot. One such promising algorithm is reservoir computing (RC), which includes of a group of recurrently and randomly connected nodes. The RC model can easily process multidimensional signal data and can handle nonlineardata and has been extensively used in robotic control. It has been reported that the RC algorithm can speed up network training and solve complex data sets. However, the main existing limitations in hand-locomotion classification are the considerable run-time and the delayed response. In this study, we figure out the best machine learning algorithms to handle three-dimensional hand-gesture data. We employ a two-part strategy: a loopback filter is included in the preprocessing of the initial dataset to support the 3-dimensional (3D) signs of each hand posture; subsequently, the training dataset is applied to the machine learning algorithm which includes an artificial neural network (ANN), convolutional neural network (CNN), long short-term memory(LSTM), and reservoir computing(RC). Each training network is optimized with various hyperparameters. Furthermore, we compare the performance of several machine-learning algorithms in classifying the three-dimensional hand-signal posture data. The results show that the classification of nonlinear hand-locomotion signals by RC requires a comparatively shorter training duration (12 minutes for training times), and that optimal accuracy 94.17, precision 94.10, and recall 93.99 is realized for time series data.