Shoulder girdle recognition using electrophysiological and low frequency anatomical contraction signals for prosthesis control  

作　　者：Ejay Nsugbe Ali H.Al-Timemy 

机构地区：[1]Nsugbe Research Labs,Swindon,UK [2]Biomedical Engineering Department,Al-Khwarizmi College of Engineering,University of Baghdad,Baghdad,Iraq

出　　处：《CAAI Transactions on Intelligence Technology》2022年第1期81-94,共14页智能技术学报（英文）

摘　　要：Shoulder disarticulation amputees account for a small portion of upper-limb amputees,thus little emphasis has been devoted to developing functional prosthesis for this cohort of amputees.In this study,shoulder girdle recognition was investigated with acquired data from electrophysiological(electromyography[EMG])and low frequency contraction(accelerometer[Acc])signals from both amputee and non-amputee participants.The contribution of this study is based around the contrast of the classification accuracy(CA)for different sensor configurations using a unique set of signal features.It was seen that the fusion of the EMG-Acc produced an enhancement in the CA in the range of 10%-20%,depending on which windowing parameters were considered.From this,it was seen that the best combination of a windowing scheme and classifier would likely be for the 350 ms and spectral regression discriminant analysis,with a fusion of the EMG-Acc information.The results have thus provided evidence that the two sensors can be combined and used in practice for prosthesis control.Taking a holistic view on the study,the authors conclude by providing a framework on how the shoulder motion recognition could be combined with neuromuscular reprogramming to contribute towards easing the cognitive burden of amputees during the prosthesis control process.

关 键 词：CYBERNETICS electromyography neuromuscular reprogramming pattern recognition prosthesis control upperlimb amputees 

分 类 号：R31[医药卫生—基础医学]