Dual Stage Classification of Hand Gestures using Surface Electromyogram
Surface electromyography (sEMG) is becoming exceeding useful in applications involving analysis of human motion such as in human-machine interface, assistive technology, healthcare and prosthetic development. The proposed work presents a novel dual stage classification approach for classification of grasping gestures from sEMG signals. A statistical assessment of these activities is presented to determine the similar characteristics between the considered activities. Similar activities are grouped together. In the first stage of classification, an activity is identified as belonging to a group, which is then further classified as one of the activities within the group in the second stage of classification. The performance of the proposed approach is compared to the conventional single stage classification approach in terms of classification accuracies. The classification accuracies obtained using the proposed dual stage classification are significantly higher as compared to that for single stage classification.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationGeneral ClassificationSimilar Papers 제목 키워드 기반
Memory-Efficient, Limb Position-Aware Hand Gesture Recognition using Hyperdimensional Computing
Electromyogram (EMG) pattern recognition can be used to classify hand gestures and movements for human-machine interface and prosthetics applications, but it often faces reliability issues resulting from limb position ch…
ClassificationGeneral ClassificationGesture RecognitionHand Gesture Recognition+4Spectral Collaborative Representation based Classification for Hand Gestures recognition on Electromyography Signals
In this study, we introduce a novel variant and application of the Collaborative Representation based Classification in spectral domain for recognition of the hand gestures using the raw surface Electromyography signals.…
ClassificationGeneral ClassificationHGR-Net: A Fusion Network for Hand Gesture Segmentation and Recognition
We propose a two-stage convolutional neural network (CNN) architecture for robust recognition of hand gestures, called HGR-Net, where the first stage performs accurate semantic segmentation to determine hand regions, and…
Gesture RecognitionHand Gesture RecognitionHand-Gesture RecognitionHand Gesture Segmentation+3Diverse 3D Hand Gesture Prediction from Body Dynamics by Bilateral Hand Disentanglement
Predicting natural and diverse 3D hand gestures from the upper body dynamics is a practical yet challenging task in virtual avatar creation. Previous works usually overlook the asymmetric motions between two hands and ge…
DisentanglementStatistical Analysis of Time-Frequency Features Based On Multivariate Synchrosqueezing Transform for Hand Gesture Classification
In this study, the four joint time-frequency (TF) moments; mean, variance, skewness, and kurtosis of TF matrix obtained from Multivariate Synchrosqueezing Transform (MSST) are proposed as features for hand gesture recogn…
Gesture RecognitionHand Gesture RecognitionHand-Gesture Recognition