Literature DB >> 30921651

An automatic pre-processing method to detect and reject signal artifacts from full-shift field-work sEMG recordings of bilateral trapezius activity.

Tove Østensvik1, Helmer Belbo2, Kaj Bo Veiersted3.   

Abstract

Bipolar surface EMG (sEMG) signals of the trapezius muscles bilaterally were recorded continuously with a frequency of 800 Hz during full-shift field-work by a four-channel portable data logger. After recordings of 60 forest machine operators in Finland, Norway and Sweden, we discovered erroneous data. In short of any available procedure to handle these data, a method was developed to automatically discard erroneous data in the raw data reading files (Discarding Erroneous EPOchs (DESEPO) method. The DESEPO method automatically identifies, discards and adjusts the use of signal disturbances in order to achieve the best possible data use. An epoch is a 0.1 s period of raw sEMG signals and makes the basis for the RMS calculations. If erroneous signals constitute more than 30% of the epoch signals, this classifies for discharge of the present epoch. Non-valid epochs have been discarded, as well as all the subsequent epochs. The valid data for further analyses using the automatic detection resulted in an increase of acceptable data from an average of 2.15-6.5 h per day. The combination of long-term full-shift recordings and automatic data reduction procedures made it possible to use large amount of data otherwise discarded for further analyses.
Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.

Keywords:  Erroneous signals; Full-shift sEMG recording; Low-level muscle force contraction; Raw data pre-processing; Trapezius

Mesh:

Year:  2019        PMID: 30921651     DOI: 10.1016/j.jelekin.2019.03.009

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


  3 in total

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Authors:  Erdem Yavuz; Can Eyupoglu
Journal:  Med Biol Eng Comput       Date:  2019-08-07       Impact factor: 2.602

2.  Lw-CNN-Based Myoelectric Signal Recognition and Real-Time Control of Robotic Arm for Upper-Limb Rehabilitation.

Authors:  Benzhen Guo; Yanli Ma; Jingjing Yang; Zhihui Wang; Xiao Zhang
Journal:  Comput Intell Neurosci       Date:  2020-12-28

3.  Muscle force estimation from lower limb EMG signals using novel optimised machine learning techniques.

Authors:  Chiako Mokri; Mahdi Bamdad; Vahid Abolghasemi
Journal:  Med Biol Eng Comput       Date:  2022-01-14       Impact factor: 2.602

  3 in total

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