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

Asim Waris, Muhammad Zia Ur Rehman, Imran Khan Niazi, Mads Jochumsen, Kevin Englehart, Winnie Jensen, Heidi Haavik, Ernest Nlandu Kamavuako

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

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.

Original languageEnglish
Article number3385
JournalSensors (Basel, Switzerland)
Volume20
Issue number12
ISSN1424-8220
DOIs
Publication statusPublished - 15 Jun 2020

Fingerprint Dive into the research topics of 'A Multiday Evaluation of Real-Time Intramuscular EMG Usability with ANN'. Together they form a unique fingerprint.

  • Cite this