Gesture recognition for transhumeral prosthesis control using EMG and NIR
| Autor: | Ejay Nsugbe, Carol Phillips, Mike Fraser, Jess McIntosh |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
electromyography
medical signal processing gesture recognition multilayer perceptrons artificial limbs linear discriminant analysis signal classification medical control systems trans-humeral prosthesis control myoelectric prosthesis limbs good quality gesture intent signal residual anatomy amputee classification accuracy wearable electromyography hand gesture motions multilayer perceptron neural network quadratic discriminant analysis sensing configurations emg-nir ground truth contrastive basis wearable sensors affordable emg ergonomic emg wearable emg nir sensing able-bodied participants offline ultrasound scan Cybernetics Q300-390 Electronic computers. Computer science QA75.5-76.95 |
| Zdroj: | IET Cyber-systems and Robotics (2020) |
| Druh dokumentu: | article |
| ISSN: | 2631-6315 |
| DOI: | 10.1049/iet-csr.2020.0008 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
|
Plný text