Author:
Pelaez Murciego Luis,Henrich Mauricio C.,Spaich Erika G.,Dosen Strahinja
Abstract
Abstract
Background
Myoelectric control based on hand gesture classification can be used for effective, contactless human–machine interfacing in general applications (e.g., consumer market) as well as in the clinical context. However, the accuracy of hand gesture classification can be impacted by several factors including changing wrist position. The present study aimed at investigating how channel configuration (number and placement of electrode pads) affects performance in hand gesture recognition across wrist positions, with the overall goal of reducing the number of channels without the loss of performance with respect to the benchmark (all channels).
Methods
Matrix electrodes (256 channels) were used to record high-density EMG from the forearm of 13 healthy subjects performing a set of 8 gestures in 3 wrist positions and 2 force levels (low and moderate). A reduced set of channels was chosen by applying sequential forward selection (SFS) and simple circumferential placement (CIRC) and used for gesture classification with linear discriminant analysis. The classification success rate and task completion rate were the main outcome measures for offline analysis across the different number of channels and online control using 8 selected channels, respectively.
Results
The offline analysis demonstrated that good accuracy (> 90%) can be achieved with only a few channels. However, using data from all wrist positions required more channels to reach the same performance. Despite the targeted placement (SFS) performing similarly to CIRC in the offline analysis, the task completion rate [median (lower–upper quartile)] in the online control was significantly higher for SFS [71.4% (64.8–76.2%)] compared to CIRC [57.1% (51.8–64.8%), p < 0.01], especially for low contraction levels [76.2% (66.7–84.5%) for SFS vs. 57.1% (47.6–60.7%) for CIRC, p < 0.01]. For the reduced number of electrodes, the performance with SFS was comparable to that obtained when using the full matrix, while the selected electrodes were highly subject-specific.
Conclusions
The present study demonstrated that the number of channels required for gesture classification with changing wrist positions could be decreased substantially without loss of performance, if those channels are placed strategically along the forearm and individually for each subject. The results also emphasize the importance of online assessment and motivate the development of configurable matrix electrodes with integrated channel selection.
Publisher
Springer Science and Business Media LLC
Subject
Health Informatics,Rehabilitation
Reference43 articles.
1. Farina D, et al. The extraction of neural information from the surface EMG for the control of upper-limb prostheses: emerging avenues and challenges. IEEE Trans Neural Syst Rehabil Eng. 2014;22(4):797–809.
2. Roche AD, Rehbaum H, Farina D, Aszmann OC. Prosthetic myoelectric control strategies: a clinical perspective. Curr Surg Rep. 2014;2(3):1–1.
3. Bos RA, Nizamis K, Koopman BFJM, Herder JL, Sartori M, Plettenburg DH. A case study with symbihand: an sEMG-controlled electrohydraulic hand orthosis for individuals with duchenne muscular dystrophy. IEEE Trans Neural Syst Rehabil Eng. 2020;28(1):258–66.
4. Lu Z, Tong KY, Zhang X, Li S, Zhou P. Myoelectric pattern recognition for controlling a robotic hand: a feasibility study in stroke. IEEE Trans Biomed Eng. 2019;66(2):365–72.
5. Kyranou I, Vijayakumar S, Erden MS. Causes of performance degradation in non-invasive electromyographic pattern recognition in upper limb prostheses. Front Neurorobot. 2018;12(September):1–22.
Cited by
4 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献