Detecting changes in human motion using stochastic distance measures

Muhammad Choudry; Matthew Pillar; Tyson Beach; Dana Kulic; Jack P. Callaghan

doi:10.1109/IEMBS.2011.6090939

Detecting changes in human motion using stochastic distance measures

Muhammad Choudry, Matthew Pillar, Tyson Beach, Dana Kulic, Jack P. Callaghan

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

2 Citations (Scopus)

Abstract

We propose a stochastic framework to analyze and compare differences in human motions for applications in injury prevention, rehabilitation, sports training and performance research. Human motions are modeled as Hidden Markov Models and the differences between the motions are measured using the Kullback-Leibler distance metric. The distance metric is recomputed with degrees of freedom excluded to determine which degree of freedom most influences the difference between a set of motions. The proposed system is tested on a human motion dataset consisting of lifting movements under differing load weights and ankle bracing conditions. Results indicate that the algorithm is capable of successfully determining which joints are impacted and ranking them according to importance.

Original language	English
Title of host publication	33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	3475-3478
Number of pages	4
ISBN (Print)	9781424441211
DOIs	https://doi.org/10.1109/IEMBS.2011.6090939
Publication status	Published - 26 Dec 2011
Externally published	Yes
Event	International Conference of the IEEE Engineering in Medicine and Biology Society 2011 - Boston Marriott Copley Place Hotel, Boston, United States of America Duration: 30 Aug 2011 → 3 Sep 2011 Conference number: 33rd

Publication series

Name	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)	1557-170X

Conference

Conference	International Conference of the IEEE Engineering in Medicine and Biology Society 2011
Abbreviated title	EMBC 2011
Country	United States of America
City	Boston
Period	30/08/11 → 3/09/11

Access to Document

10.1109/IEMBS.2011.6090939

Link to publication in Scopus

Cite this

Choudry, M., Pillar, M., Beach, T., Kulic, D., & Callaghan, J. P. (2011). Detecting changes in human motion using stochastic distance measures. In 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011 (pp. 3475-3478). [6090939] (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/IEMBS.2011.6090939

Choudry, Muhammad ; Pillar, Matthew ; Beach, Tyson ; Kulic, Dana ; Callaghan, Jack P. / Detecting changes in human motion using stochastic distance measures. 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011. IEEE, Institute of Electrical and Electronics Engineers, 2011. pp. 3475-3478 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

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title = "Detecting changes in human motion using stochastic distance measures",

abstract = "We propose a stochastic framework to analyze and compare differences in human motions for applications in injury prevention, rehabilitation, sports training and performance research. Human motions are modeled as Hidden Markov Models and the differences between the motions are measured using the Kullback-Leibler distance metric. The distance metric is recomputed with degrees of freedom excluded to determine which degree of freedom most influences the difference between a set of motions. The proposed system is tested on a human motion dataset consisting of lifting movements under differing load weights and ankle bracing conditions. Results indicate that the algorithm is capable of successfully determining which joints are impacted and ranking them according to importance.",

author = "Muhammad Choudry and Matthew Pillar and Tyson Beach and Dana Kulic and Callaghan, {Jack P.}",

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Choudry, M, Pillar, M, Beach, T, Kulic, D & Callaghan, JP 2011, Detecting changes in human motion using stochastic distance measures. in 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011., 6090939, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, IEEE, Institute of Electrical and Electronics Engineers, pp. 3475-3478, International Conference of the IEEE Engineering in Medicine and Biology Society 2011, Boston, United States of America, 30/08/11. https://doi.org/10.1109/IEMBS.2011.6090939

Detecting changes in human motion using stochastic distance measures. / Choudry, Muhammad; Pillar, Matthew; Beach, Tyson; Kulic, Dana; Callaghan, Jack P.

33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011. IEEE, Institute of Electrical and Electronics Engineers, 2011. p. 3475-3478 6090939 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

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

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AB - We propose a stochastic framework to analyze and compare differences in human motions for applications in injury prevention, rehabilitation, sports training and performance research. Human motions are modeled as Hidden Markov Models and the differences between the motions are measured using the Kullback-Leibler distance metric. The distance metric is recomputed with degrees of freedom excluded to determine which degree of freedom most influences the difference between a set of motions. The proposed system is tested on a human motion dataset consisting of lifting movements under differing load weights and ankle bracing conditions. Results indicate that the algorithm is capable of successfully determining which joints are impacted and ranking them according to importance.

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Choudry M, Pillar M, Beach T, Kulic D, Callaghan JP. Detecting changes in human motion using stochastic distance measures. In 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011. IEEE, Institute of Electrical and Electronics Engineers. 2011. p. 3475-3478. 6090939. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). https://doi.org/10.1109/IEMBS.2011.6090939