On the robustness of real-time myoelectric control investigations: a multiday Fitts' law approach. (21st January 2019)

• Record Type: Journal Article
• Authors: Waris, Asim; Mendez, Irene; Englehart, Kevin; Jensen, Winnie; Kamavuako, Ernest Nlandu
• Is Part Of: Journal of neural engineering. Volume 16:Number 2(2019:Apr.)
• Publication Date: 2019-01-21
• Subjects: pattern recognition -- real-time control -- myoelectric control -- multiday Fitts' law; Neurosciences -- Periodicals; Biomedical engineering -- Periodicals; 612.8
• DOI: 10.1088/1741-2552/aae9d4
• Languages: English
• ISSNs: 1741-2560

Abstract

Abstract: Objective . Real-time myoelectric experimental protocol is considered as a means to quantify usability of myoelectric control schemes. While usability should be considered over time to assure clinical robustness, all real-time studies reported thus far are limited to a single session or day and thus the influence of time on real-time performance is still unexplored. In this study, the aim was to develop a novel experimental protocol to quantify the effect of time on real-time performance measures over multiple days using a Fitts' law approach. Approach . Four metrics: throughput, completion rate, path efficiency and overshoot, were assessed using three train-test strategies: (i) an artificial neural network (ANN) classifier was trained on data collected from the previous day and tested on present day (BDT) (ii) trained and tested on the same day (WDT) and (iii) trained on all previous days including present day and tested on present day (CDT) in a week-long experimental protocol. Main results . It was found that on average, the completion rate (98.37% ± 1.47%) of CDT was significantly better ( P < 0.01) than that of BDT (86.25% ± 3.46%) and WDT (94.22% ± 2.74%). The throughput (0.40 ± 0.03 bits s −1 ) of CDT was significantly better ( P = 0.001) than that of BDT (0.38 ± 0.03 bits s −1 ). Offline analysis showed a different trend due to the difference in the training strategies. Significance . Results suggest that increasing the size of the training set over time can be beneficial to assure robust performance of the system over time.