Lifting activity assessment using surface electromyographic features and neural networks

Lifting activity assessment using surface electromyographic features and neural networks The surface electromyographic (sEMG) data of 12 trunk muscles of 10 workers during the execution of lifting tasks using three lifting indices (LI) were recorded. The aims of this work were to: 1) identify the most sensitive trunk muscles with respect to changes in lifting conditions based on the selected sEMG features and 2) test whether machine-learning techniques (artificial neural networks) used for mapping time and frequency sEMG features on LI levels can improve the biomechanical risk assessment. The results show that the erector spinae longissimus is the trunk muscle for which every sEMG feature can significantly discriminate each pair of LI. Furthermore, only when using multi-domain features (time and frequency) a more complex artificial neural network can lead to an improved biomechanical risk classification related to lifting tasks. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Industrial Ergonomics Elsevier

Loading next page...
 
/lp/elsevier/lifting-activity-assessment-using-surface-electromyographic-features-2h0KJPIV1p
Publisher
Elsevier
Copyright
Copyright © 2018 Elsevier B.V.
ISSN
0169-8141
eISSN
1872-8219
D.O.I.
10.1016/j.ergon.2018.02.003
Publisher site
See Article on Publisher Site

Abstract

The surface electromyographic (sEMG) data of 12 trunk muscles of 10 workers during the execution of lifting tasks using three lifting indices (LI) were recorded. The aims of this work were to: 1) identify the most sensitive trunk muscles with respect to changes in lifting conditions based on the selected sEMG features and 2) test whether machine-learning techniques (artificial neural networks) used for mapping time and frequency sEMG features on LI levels can improve the biomechanical risk assessment. The results show that the erector spinae longissimus is the trunk muscle for which every sEMG feature can significantly discriminate each pair of LI. Furthermore, only when using multi-domain features (time and frequency) a more complex artificial neural network can lead to an improved biomechanical risk classification related to lifting tasks.

Journal

International Journal of Industrial ErgonomicsElsevier

Published: Jul 1, 2018

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off