Englehart, Kevin

Jensen, Winnie

Kamavuako, Ernest Nlandu

Mendez, Irene

Waris, Asim

English

BACKGROUND AND AIM: Real-time myoelectric experimental protocol is considered as 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 in order to quantify the effect of time on real-time performance measures over multiple days using a Fitts' law approach. &amp;#13; Methods: 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.RESULTS: It was found that on average, Completion rate (98.37 ± 1.47 %) of CDT was significantly better (P&amp;lt;0.01) than BDT (86.25 ± 3.46 %) and WDT (94.22 ± 2.74 %). Throughput (0.40 ± 0.03 bits/s) of CDT was significantly better (P=0.001) than BDT (0.38±0.03 bits/s). Offline analysis showed a different trend due to the difference in the training strategies. &amp;#13; Conclusion: Results suggest that increasing the size of training set over time can be beneficial to assure robust performance of the system over time. &amp;#13.</p

On the robustness of real-time myoelectric control investigations:A multiday Fitts' law approach

 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 &lt; 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. </p

King's Research Portal

https://vbn.aau.dk/ws/files/335131477/Waris_et_al_JNE_Pure_003_.pdf

On the robustness of real-time myoelectric control investigations:A multiday Fitts' law approach

Abstract

Similar works

Full text

Available Versions

VBN

King's Research Portal