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Open AccessArticle

A Multiday Evaluation of Real-Time Intramuscular EMG Usability with ANN

1
Department of Biomedical Engineering and Sciences, School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan
2
Faculty of Engineering and Applied Sciences, Riphah International University, Islamabad 46000, Pakistan
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Center for Sensory-Motor Interaction, Department of Health Science and Technology, Aalborg University, 9220 Aalborg, Denmark
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Center of Chiropractic Research, New Zealand College of Chiropractic, Auckland 1060, New Zealand
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Faculty of Health and Environmental Sciences, Health and Rehabilitation Research Institute, AUT University, Auckland 0627, New Zealand
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Department of Electrical and Computer Engineering, University of New Brunswick, Fredericton, NB E3B 5A3, Canada
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Centre for Robotics Research, Department of Informatics, King’s College London, London WC2R 2LS, UK
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(12), 3385; https://doi.org/10.3390/s20123385
Received: 3 May 2020 / Revised: 12 June 2020 / Accepted: 12 June 2020 / Published: 15 June 2020
(This article belongs to the Section Intelligent Sensors)
Recent developments in implantable technology, such as high-density recordings, wireless transmission of signals to a prosthetic hand, may pave the way for intramuscular electromyography (iEMG)-based myoelectric control in the future. This study aimed to investigate the real-time control performance of iEMG over time. A novel protocol was developed to quantify the robustness of the real-time performance parameters. Intramuscular wires were used to record EMG signals, which were kept inside the muscles for five consecutive days. Tests were performed on multiple days using Fitts’ law. Throughput, completion rate, path efficiency and overshoot were evaluated as performance metrics using three train/test strategies. Each train/test scheme was categorized on the basis of data quantity and the time difference between training and testing data. An artificial neural network (ANN) classifier was trained and tested on (i) data from the same day (WDT), (ii) data collected from the previous day and tested on present-day (BDT) and (iii) trained on all previous days including the present day and tested on present-day (CDT). It was found that the completion rate (91.6 ± 3.6%) of CDT was significantly better (p < 0.01) than BDT (74.02 ± 5.8%) and WDT (88.16 ± 3.6%). For BDT, on average, the first session of each day was significantly better (p < 0.01) than the second and third sessions for completion rate (77.9 ± 14.0%) and path efficiency (88.9 ± 16.9%). Subjects demonstrated the ability to achieve targets successfully with wire electrodes. Results also suggest that time variations in the iEMG signal can be catered by concatenating the data over several days. This scheme can be helpful in attaining stable and robust performance. View Full-Text
Keywords: intramuscular electromyography (iEMG); prosthetic hand; pattern recognition (PR) intramuscular electromyography (iEMG); prosthetic hand; pattern recognition (PR)
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MDPI and ACS Style

Waris, A.; Zia ur Rehman, M.; Niazi, I.K.; Jochumsen, M.; Englehart, K.; Jensen, W.; Haavik, H.; Kamavuako, E.N. A Multiday Evaluation of Real-Time Intramuscular EMG Usability with ANN. Sensors 2020, 20, 3385.

AMA Style

Waris A, Zia ur Rehman M, Niazi IK, Jochumsen M, Englehart K, Jensen W, Haavik H, Kamavuako EN. A Multiday Evaluation of Real-Time Intramuscular EMG Usability with ANN. Sensors. 2020; 20(12):3385.

Chicago/Turabian Style

Waris, Asim; Zia ur Rehman, Muhammad; Niazi, Imran K.; Jochumsen, Mads; Englehart, Kevin; Jensen, Winnie; Haavik, Heidi; Kamavuako, Ernest N. 2020. "A Multiday Evaluation of Real-Time Intramuscular EMG Usability with ANN" Sensors 20, no. 12: 3385.

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