A Feature Adaptive Learning Method for High-Density sEMG-Based Gesture Recognition

Abstract

Surface electromyography (sEMG) array based gesture recognition, which is widely-used, could provide natural surfaces for human-computer interaction. Currently, most existing gesture recognition methods with sEMG array only work with the fixed and pre-defined electrodes configuration. However, changes in the number of electrodes (i.e., increment or decrement) is common in real scenarios due to the variability of physiological electrodes. In this paper, we study this challenging problem and propose a random forest based ensemble learning method, namely feature incremental and decremental ensemble learning (FIDE). FIDE is able to support continuous changes in the number of electrodes by dynamically maintaining the matrix sketches of every sEMG electrode and spatial structure of sEMG array. To evaluate the performance of FIDE, we conduct extensive experiments on three benchmark datasets, including NinaPro, CSL-hdemg, and CapgMyo. Experimental results demonstrate that FIDE outperforms other state-of-the-art methods and has the potential to adapt to the evolution of electrodes in the changing environments. Moreover, based on FIDE, we implement a multi clients/server collaboration system, namely McS, to support feature adaption in real-world environment. By collecting sEMG using two clients (smartphone and personal computer) and adaptively recognizing gestures in the cloud server, FIDE significantly improves the gesture recognition accuracy in electrode increment and decrement circumstances.

A fuzzy granular logistic regression algorithm for sEMG-based cross-individual prosthetic hand gesture classification

“…However, the average recognition accuracy of subject 7 based on the LogR algorithm reached 85%, which was significantly higher than that of subject 3 (74.6%). Although the subject's hand muscle composition is consistent, the development of the musculature is different, which will have a greater impact on the sEMG signal and gesture recognition [42][43][44][45][46][47][48][49][50][51][52][53][54]. In addition, for gesture activity recognition, the classification accuracies of HC and HO increased We acknowledge some limitations of this study.…”

“…Copaci et al proposed a new classifier based on a Bayesian neural network to recognize six rehabilitation gestures with 98.7% accuracy [49]. Zhang et al proposed an ensemble learning method based on random forests to adaptively learn the gesture features of high-density sEMG, and the results outperformed other advanced algorithms [50]. Karnam et al proposed energy features for sEMG classification, and finally the KNN classifier achieved the highest validation accuracy of 88.8%, exceeding the state-ofthe-art accuracy of 13% [51].…”

A novel unsupervised dynamic feature domain adaptation strategy for cross-individual myoelectric gesture recognition

“…Hand motion recognition is an important research area in the field of human-computer interaction. However, due to individual differences, when a model was trained on known individuals, its performance will be greatly degraded for new individuals [39]. The traditional approach is to collect a large amount of labeled data from new users to retrain a model, which is time-consuming.…”

Enhancing sEMG-Based Finger Motion Prediction with CNN-LSTM Regressors for Controlling a Hand Exoskeleton

“…Indeed, our goal is to create a device that is completely usable by everyone, even those without specific knowledge of human anatomy. To solve the electrode placement problem, we can use dense arrays of sEMG sensors like in [32][33][34][35]. The high number of electrodes and the proximity of one to another reduce the positioning error when placing the array in the measuring zone but, at the same time, increase the cost and complexity of the system while also making it more difficult to fit.…”