Gesture recognition for transhumeral prosthesis control using EMG and NIR  

作　　者：Ejay Nsugbe Carol Phillips Mike Fraser Jess McIntosh 

机构地区：[1]University of Bristol,Queen's Building,University Walk,Bristol BS81TR,UK [2]Department of Radiology,University Hospitals Bristol,NHS Foundation Trust,Bristol,UK

出　　处：《IET Cyber-Systems and Robotics》2020年第3期122-131,共10页智能系统与机器人（英文）

摘　　要：A key challenge associated with myoelectric prosthesis limbs is the acquisition of a good quality gesture intent signal from the residual anatomy of an amputee.In this study,the authors aim to overcome this limitation by observing the classification accuracy of the fusion of wearable electromyography(EMG)and near-infrared(NIR)to classify eight hand gesture motions across 12 able-bodied participants.As part of the study,they investigate the classification accuracy across a multi-layer perceptron neural network,linear discriminant analysis and quadratic discriminant analysis for different sensing configurations,i.e.EMG-only,NIR-only and EMG-NIR.A separate offline ultrasound scan was conducted as part of the study and served as a ground truth and contrastive basis for the results picked up from the wearable sensors,and allowed for a closer study of the anatomy along the humerus during gesture motion.Results and findings from the work suggest that it could be possible to further develop transhumeral prosthesis using affordable,ergonomic and wearable EMG and NIR sensing,without the need for invasive neuromuscular sensors or further hardware complexity.

关 键 词：ANATOMY PROSTHESIS EMG 

分 类 号：R74[医药卫生—神经病学与精神病学]