Pattern recognition and bionic manipulator driving by surface electromyography signals using convolutional neural network

Abstract

With the development of robotics, intelligent neuroprosthesis for amputees is more concerned. Research of robot controlling based on electrocardiogram, electromyography, and electroencephalogram is a hot spot. In medical research, electrode arrays are commonly used as sensors for surface electromyograms. Although these sensors collect more accurate data and sampling at higher frequencies, they have no advantage in terms of portability and ease of use. In recent years, there are also some small surface electromyography sensors for research. The portability of the sensor and the calculation speed of the calculation method directly affect the development of the bionic prosthesis. A consumer-grade surface electromyography device is selected as surface electromyography sensor in this study. We first proposed a data structure to convert raw surface electromyography signals from an array structure into a matrix structure (we called it surface electromyography graph). Then, a convolutional neural network was used to classify it. Discrete surface electromyography signals recorded from three persons 14 gestures (widely used in other research to evaluate the performance of classifier) have been applied to train the classifier and we get an accuracy of 97.27%. The impacts of different components used in convolutional neural network were tested with this data, and subsequently, the best results were selected to build the classifier used in this article. The NinaPro database 5 (one of the biggest surface electromyography data sets) was also used to evaluate our method, which comprises of hand movement data of 10 intact subjects with two myo armbands as sensors, and the classification accuracy increased by 13.76% on average when using double myo armbands and increased by 18.92% on average when using single myo armband. In order to driving the robot hand (bionic manipulator), a group of continuous surface electromyography signals was recorded to train the classifier, and an accuracy of 91.72% was acquired. We also used the same method to collect a set of surface electromyography data from a disabled with hand lost, then classified it using the abovementioned network and achieved an accuracy of 89.37%. Finally, the classifier was deployed to the microcontroller to drive the bionic manipulator, and the full video URL is given in the conclusion, with both the healthy man and the disabled tested with the bionic manipulator. The abovementioned results suggest that this method will help to facilitate the development and application of surface electromyography neuroprosthesis.

Gesture Recognition from Bio-signals Using Hybrid Deep Neural Networks

“…Compared to this research, we achieved a higher average accuracy of 88.73%, evaluated on data from unknown trials (6 total trials for each subject and gesture; 2 unknown used for evaluation, 4 for training). Meanwhile, the Residual CNN from Wan and Han obtained an accuracy of 82.15% for 14 selected hand gestures [11]. Unlike other works, they transformed the raw sEMG signal from single armband into an image, then applied a Residual CNN to classify these images.…”

sEMG-Based Hand-Gesture Classification Using a Generative Flow Model

“…The sEMG used in the method was segmented at a window of 200 sampling points with an overlap of 100 sampling points. Meanwhile, 82.15% accuracy was achieved by a CNN described in [32] for 17 selected hand gestures; however, the CNN required pre-training using sEMG from other databases. The sEMG used in this method was segmented at a window of 16 sampling points.…”

CNN Confidence Estimation for Rejection-Based Hand Gesture Classification in Myoelectric Control

“…For DB5, Thalmic Myo incorporated a notch filter at 50 Hz. Based on filtered sEMG, a min-max normalization was implemented for each subject individually [44]. This normalization method was adopted since it can keep the original distribution of sEMG.…”