Repository logo
  • Log In
    New user? Click here to register.Have you forgotten your password?
University College Dublin
  • Colleges & Schools
  • Statistics
  • All of DSpace
  • Log In
    New user? Click here to register.Have you forgotten your password?
  1. Home
  2. Institutes and Centres
  3. Insight Centre for Data Analytics
  4. Insight Research Collection
  5. Evaluating Squat Performance with a Single Inertial Measurement Unit
 
  • Details
Options

Evaluating Squat Performance with a Single Inertial Measurement Unit

File(s)
FileDescriptionSizeFormat
Download insight_publication.pdf5.25 MB
Author(s)
O'Reilly, Martin 
Whelan, Darragh 
Chanialidis, Charalampos 
Friel, Nial 
Delahunt, Eamonn 
Ward, Tomás 
Caulfield, Brian 
Uri
http://hdl.handle.net/10197/8673
Date Issued
12 June 2015
Date Available
26T09:50:03Z July 2017
Abstract
Inertial measurement units (IMUs) may be used during exercise performance to assess form and technique. To maximise practicality and minimise cost a single-sensor system is most desirable. This study sought to investigate whether a single lumbar-worn IMU is capable of identifying seven commonly observed squatting deviations. Twenty-two volunteers (18 males, 4 females, age: 26.09±3.98 years, height: 1.75±0.14m, body mass: 75.2±14.2 kg) performed the squat exercise correctly and with 7 induced deviations. IMU signal features were extracted for each condition. Statistical analysis and leave one subject out classifier evaluation were used to assess the ability of a single sensor to evaluate performance. Binary level classification was able to distinguish between correct and incorrect squatting performance with a sensitivity of 64.41%, specificity of 88.01% and accuracy of 80.45%. Multi-label classification was able to distinguish between specific squat deviations with a sensitivity of 59.65%, specificity of 94.84% and accuracy of 56.55%. These results indicate that a single IMU can successfully discriminate between squatting deviations. A larger data set must be collected and more complex classification techniques developed in order to create a more robust exercise analysis IMU-based system.
Sponsorship
Science Foundation Ireland
Type of Material
Conference Publication
Publisher
IEEE
Keywords
  • Biomedical measuremen...

  • Body sensor networks

  • Feature extraction

  • Sensitivity analysis

DOI
10.1109/BSN.2015.7299380
Language
English
Status of Item
Peer reviewed
Description
2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks, MIT, Cambridge, Massachusetts, United States of America, 9-12 June 2015
ISBN
9781467372015
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Owning collection
Insight Research Collection
Scopus© citations
28
Acquisition Date
Jan 26, 2023
View Details
Views
1128
Acquisition Date
Jan 26, 2023
View Details
Downloads
1001
Acquisition Date
Jan 26, 2023
View Details
google-scholar
University College Dublin Research Repository UCD
The Library, University College Dublin, Belfield, Dublin 4
Phone: +353 (0)1 716 7583
Fax: +353 (0)1 283 7667
Email: mailto:research.repository@ucd.ie
Guide: http://libguides.ucd.ie/rru

Built with DSpace-CRIS software - Extension maintained and optimized by 4Science

  • Cookie settings
  • Privacy policy
  • End User Agreement