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  5. Evaluating Squat Performance with a Single Inertial Measurement Unit
 
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Evaluating Squat Performance with a Single Inertial Measurement Unit

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
2015-06-12
Date Available
2017-07-26T09:50:03Z
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
Subjects

Biomedical measuremen...

Body sensor networks

Feature extraction

Sensitivity analysis

DOI
10.1109/BSN.2015.7299380
Language
English
Status of Item
Peer reviewed
Conference Details
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/
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insight_publication.pdf

Size

5.25 MB

Format

Adobe PDF

Checksum (MD5)

7d277fe6dd636a08a1bda0e36505db04

Owning collection
Insight Research Collection
Mapped collections
Public Health, Physiotherapy and Sports Science Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
All other content is subject to copyright.

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