Evaluating Squat Performance With a Single Inertial Measurement Unit

O'Reilly, M., Whelan, D., Chanialidis, C. , Friel, N., Delahunt, E., Ward, T. and Caulfield, B. (2015) Evaluating Squat Performance With a Single Inertial Measurement Unit. In: 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN), Cambridge, MA, USA, 09-12 Jun 2015, ISBN 9781467372015 (doi: 10.1109/BSN.2015.7299380)

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.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Chanialidis, Dr Charalampos
• Authors: O'Reilly, M., Whelan, D., Chanialidis, C., Friel, N., Delahunt, E., Ward, T., and Caulfield, B.
• College/School: College of Science and Engineering > School of Mathematics and Statistics > Statistics
• ISSN: 2376-8886
• ISBN: 9781467372015
• Published Online: 19 October 2015