Open AccessShoulder girdle recognition using electrophysiological and low frequency anatomical contraction signals for prosthesis control
Author(s) -
Nsugbe Ejay,
AlTimemy Ali H.
Publication year - 2022
Publication title -
caai transactions on intelligence technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.613
H-Index - 15
ISSN - 2468-2322
DOI - 10.1049/cit2.12058
Subject(s) - electromyography , shoulder girdle , prosthesis , physical medicine and rehabilitation , computer science , artificial intelligence , sensor fusion , pattern recognition (psychology) , engineering , speech recognition , medicine , anatomy
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.