2020年8月4日 · In this paper, we present a fully neuromorphic sensor fusion approach for hand-gesture recognition comprised of an event-based vision sensor and three different neuromorphic processors. In particular, we used the event-based camera, called DVS, and two neuromorphic platforms, Loihi and ODIN + MorphIC.

2021年1月1日 · This work shows the application of several procedures to extract features from EMG signals that allow the training of an artificial intelligence model that classifies EMG into the different gestures classes.

2025年1月17日 · This dataset, comprising EMG and hand kinematics data from 8 participants performing 6 different hand gestures, provides a comprehensive resource for investigating position-invariant...

In this study, we develop EMG-based hand/finger gesture classifiers based on fixed electrode placement using machine learning methods. Ten healthy subjects performed ten hand/finger gestures, including seven IF gestures. EMG signals were measured from three channels, and six time-domain (TD) features were extracted from each channel.

In this study, a novel approach based on deep learning has been proposed to improve the accuracy rate in the prediction of hand movements. Firstly, 4-channel surface EMG (sEMG) signals have been measured while simulating 7 different hand gestures (Extension, Flexion, Open Hand, Punch, Radial Deviation, Rest, and Ulnar Deviation) from 30 ...

Electromyography (EMG) based CNN for hand gesture classification. CNN classifier and sEMG dataset construction: Convolutional neural network which contains one input layer, three hidden layers and one fully connected output layer was constructed for sEMG classification.

2024年8月9日 · This study aims to demonstrate the capability of a novel HD-EMG forearm sleeve equipped with up to 150 electrodes to capture high-resolution muscle activity, decode complex hand gestures, and...

In this study, we introduce a novel classifier for hand gesture recognition based on electromyography (EMG). Our approach utilizes individual EMG sensors placed

In this paper, a comparative study of classifying different hand gestures of two well-known surface Electromyogram (sEMG) data sets, Rami Khusaba EMG repository

2023年11月1日 · We adopted a systematic review to identify hand gestures using electromyography-based machine learning classifier. We surveyed the hand gesture recognition process and proposed a standard recognition model. Artificial neural networks show the highest accuracy for gesture recognition, and support vector machines were mostly used classifiers.