Literature DB >> 29689443

The effect of time on EMG classification of hand motions in able-bodied and transradial amputees.

Asim Waris1, Imran Khan Niazi2, Mohsin Jamil3, Omer Gilani3, Kevin Englehart4, Winnie Jensen5, Muhammad Shafique6, Ernest Nlandu Kamavuako7.   

Abstract

While several studies have demonstrated the short-term performance of pattern recognition systems, long-term investigations are very limited. In this study, we investigated changes in classification performance over time. Ten able-bodied individuals and six amputees took part in this study. EMG signals were recorded concurrently from surface and intramuscular electrodes, with intramuscular electrodes kept in the muscles for seven days. Seven hand motions were evaluated daily using linear discriminant analysis and the classification error quantified within (WCE) and between (BCE) days. BCE was computed for all possible combinations between the days. For all subjects, surface sEMG (7.2 ± 7.6%), iEMG (11.9 ± 9.1%) and cEMG (4.6 ± 4.8%) were significantly different (P < 0.001) from each other. A regression between WCE and days (1-7) was on average not significant implying that performance may be considered similar within each day. Regression between BCE and time difference (Df) in days was significant. The slope between BCE and Df (0-6) was significantly different from zero for sEMG (R2 = 89%) and iEMG (R2 = 95%) in amputees. Results indicate that performance continuously degrades as the time difference between training and testing day increases. Furthermore, for iEMG, performance in amputees was directly proportional to the size of the residual limb.
Copyright © 2018 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Classification; Electromyography; Multiday performance; Myoelectric control; Pattern recognition

Mesh:

Year:  2018        PMID: 29689443     DOI: 10.1016/j.jelekin.2018.04.004

Source DB:  PubMed          Journal:  J Electromyogr Kinesiol        ISSN: 1050-6411            Impact factor:   2.368


  5 in total

1.  Two degrees of freedom, dynamic, hand-wrist EMG-force using a minimum number of electrodes.

Authors:  Chenyun Dai; Ziling Zhu; Carlos Martinez-Luna; Thane R Hunt; Todd R Farrell; Edward A Clancy
Journal:  J Electromyogr Kinesiol       Date:  2019-04-16       Impact factor: 2.368

2.  A comparative study of motion detection with FMG and sEMG methods for assistive applications.

Authors:  Muhammad Raza Ul Islam; Asim Waris; Ernest Nlandu Kamavuako; Shaoping Bai
Journal:  J Rehabil Assist Technol Eng       Date:  2020-11-12

3.  Grid Frequency Measurement through a PLHR Analysis Obtained from an ELF Magnetometer.

Authors:  Francisco Portillo; Alfredo Alcayde; Rosa M García; Nuria Novas; José Antonio Gázquez; Manuel Férnadez-Ros
Journal:  Sensors (Basel)       Date:  2022-04-12       Impact factor: 3.847

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

Authors:  Asim Waris; Muhammad Zia Ur Rehman; Imran Khan Niazi; Mads Jochumsen; Kevin Englehart; Winnie Jensen; Heidi Haavik; Ernest Nlandu Kamavuako
Journal:  Sensors (Basel)       Date:  2020-06-15       Impact factor: 3.576

5.  Performance Evaluation of Convolutional Neural Network for Hand Gesture Recognition Using EMG.

Authors:  Ali Raza Asif; Asim Waris; Syed Omer Gilani; Mohsin Jamil; Hassan Ashraf; Muhammad Shafique; Imran Khan Niazi
Journal:  Sensors (Basel)       Date:  2020-03-15       Impact factor: 3.576

  5 in total

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