Evaluating Squat Performance with a Single Inertial Measurement Unit
|Title:||Evaluating Squat Performance with a Single Inertial Measurement Unit||Authors:||O'Reilly, Martin; Whelan, Darragh; Chanialidis, Charalampos; Friel, Nial; Delahunt, Eamonn; Ward, Tomás; Caulfield, Brian||Permanent link:||http://hdl.handle.net/10197/8673||Date:||12-Jun-2015||Online since:||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.||Funding Details:||Science Foundation Ireland||Type of material:||Conference Publication||Publisher:||IEEE||Keywords:||Biomedical measurement; Body sensor networks; Feature extraction; Sensitivity analysis||DOI:||10.1109/BSN.2015.7299380||Language:||en||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/|
|Appears in Collections:||Public Health, Physiotherapy and Sports Science Research Collection|
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