Classification of squat quality with inertial measurement units in the single leg squat mobility test

Rezvan Kianifar; Alex Lee; Sachin Raina; Dana Kulic

doi:10.1109/EMBC.2016.7592162

Classification of squat quality with inertial measurement units in the single leg squat mobility test

Rezvan Kianifar, Alex Lee, Sachin Raina, Dana Kulic

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

19 Citations (Scopus)

Abstract

Many assessment and diagnosis protocols in rehabilitation, orthopedic surgery and sports medicine rely on mobility tests like the Single Leg Squat (SLS). In this study, a set of three Inertial Measurement Units (IMUs) were used to estimate the joint pose during SLS and to classify the SLS as poor, moderate or good. An Extended Kalman Filter pose estimation method was used to estimate kinematic joint variables, and time domain features were generated based on these variables. The most important features were then selected and used to train Support Vector Machine (SVM), Linear Multinomial Logistic Regression, and Decision Tree classifiers. The results of feature selection highlight the importance of the ankle internal rotation (IR) angle in classifying SLS. Classification results on a human motion dataset achieved an accuracy of 98% for the two-class problem using SVM, while for 3 class classification, the maximum accuracy was 73% using Decision Tree.

Original language	English
Title of host publication	2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2016)
Editors	Jose Principe, Jose Carmena, Justin Sanchez
Place of Publication	Piscataway NJ USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	6273-6276
Number of pages	4
ISBN (Electronic)	9781457702204
ISBN (Print)	9781457702198
DOIs	https://doi.org/10.1109/EMBC.2016.7592162
Publication status	Published - 2016
Externally published	Yes
Event	International Conference of the IEEE Engineering in Medicine and Biology Society 2016 - Walt Disney World Resort, Orlando, United States of America Duration: 16 Aug 2016 → 20 Aug 2016 Conference number: 38th https://embc.embs.org/2016/ https://ieeexplore.ieee.org/xpl/conhome/7580725/proceeding (Proceedings)

Conference

Conference	International Conference of the IEEE Engineering in Medicine and Biology Society 2016
Abbreviated title	EMBC 2016
Country/Territory	United States of America
City	Orlando
Period	16/08/16 → 20/08/16
Internet address	https://embc.embs.org/2016/ https://ieeexplore.ieee.org/xpl/conhome/7580725/proceeding (Proceedings)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/EMBC.2016.7592162

Cite this

Kianifar, R., Lee, A., Raina, S., & Kulic, D. (2016). Classification of squat quality with inertial measurement units in the single leg squat mobility test. In J. Principe, J. Carmena, & J. Sanchez (Eds.), 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2016) (pp. 6273-6276). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/EMBC.2016.7592162

Kianifar, Rezvan ; Lee, Alex ; Raina, Sachin et al. / Classification of squat quality with inertial measurement units in the single leg squat mobility test. 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2016) . editor / Jose Principe ; Jose Carmena ; Justin Sanchez. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2016. pp. 6273-6276

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abstract = "Many assessment and diagnosis protocols in rehabilitation, orthopedic surgery and sports medicine rely on mobility tests like the Single Leg Squat (SLS). In this study, a set of three Inertial Measurement Units (IMUs) were used to estimate the joint pose during SLS and to classify the SLS as poor, moderate or good. An Extended Kalman Filter pose estimation method was used to estimate kinematic joint variables, and time domain features were generated based on these variables. The most important features were then selected and used to train Support Vector Machine (SVM), Linear Multinomial Logistic Regression, and Decision Tree classifiers. The results of feature selection highlight the importance of the ankle internal rotation (IR) angle in classifying SLS. Classification results on a human motion dataset achieved an accuracy of 98% for the two-class problem using SVM, while for 3 class classification, the maximum accuracy was 73% using Decision Tree.",

author = "Rezvan Kianifar and Alex Lee and Sachin Raina and Dana Kulic",

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Kianifar, R, Lee, A, Raina, S & Kulic, D 2016, Classification of squat quality with inertial measurement units in the single leg squat mobility test. in J Principe, J Carmena & J Sanchez (eds), 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2016) . IEEE, Institute of Electrical and Electronics Engineers, Piscataway NJ USA, pp. 6273-6276, International Conference of the IEEE Engineering in Medicine and Biology Society 2016, Orlando, Florida, United States of America, 16/08/16. https://doi.org/10.1109/EMBC.2016.7592162

Classification of squat quality with inertial measurement units in the single leg squat mobility test. / Kianifar, Rezvan; Lee, Alex; Raina, Sachin et al.
2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2016) . ed. / Jose Principe; Jose Carmena; Justin Sanchez. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers, 2016. p. 6273-6276.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

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PY - 2016

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AB - Many assessment and diagnosis protocols in rehabilitation, orthopedic surgery and sports medicine rely on mobility tests like the Single Leg Squat (SLS). In this study, a set of three Inertial Measurement Units (IMUs) were used to estimate the joint pose during SLS and to classify the SLS as poor, moderate or good. An Extended Kalman Filter pose estimation method was used to estimate kinematic joint variables, and time domain features were generated based on these variables. The most important features were then selected and used to train Support Vector Machine (SVM), Linear Multinomial Logistic Regression, and Decision Tree classifiers. The results of feature selection highlight the importance of the ankle internal rotation (IR) angle in classifying SLS. Classification results on a human motion dataset achieved an accuracy of 98% for the two-class problem using SVM, while for 3 class classification, the maximum accuracy was 73% using Decision Tree.

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Kianifar R, Lee A, Raina S, Kulic D. Classification of squat quality with inertial measurement units in the single leg squat mobility test. In Principe J, Carmena J, Sanchez J, editors, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2016) . Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. 2016. p. 6273-6276 doi: 10.1109/EMBC.2016.7592162

Classification of squat quality with inertial measurement units in the single leg squat mobility test

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