Assessing accelerometer based gait features to support gait analysis for people with complex regional pain syndrome

Mingjing Yang, Huiru Zheng, Haiying Wang, Sally McClean, Jane Hall, Nigel Harris

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Citations (Scopus)

Abstract

In this paper, we explored the feasibility of analysing gait patterns during the Short Physical Performance Battery test by using an accelerometer to record the movement of the subject. 12 subjects with Complex Regional Pain Syndrome (CRPS) and 10 control subjects were recruited in this study. 21 gait features including temporal, frequency, regularity and symmetric information were extracted from each recording. The differences of each feature value on control subjects and patient subjects were assessed and compared. Features were selected based on the signal to noise ratio (SNR) ranking. Multilayer perceptron neural-networks were employed to differentiate between the normal and abnormal gait patterns. The result shows when using five features the best classification accuracy (97.5%) was achieved. It is feasible to discriminate the patients with CRPS from the control subjects using a small set of gait features extracted from walking acceleration data recorded during the SPPB test.
LanguageEnglish
Title of host publicationUnknown Host Publication
Place of Publicationhttp://portal.acm.org/citation.cfm?doid=1839294.1839352
PagesArtical 48
Number of pages6
DOIs
Publication statusPublished - 2010
EventThe 3rd International Conference on PErvasive Technologies Related to Assistive Environments - Samos, Greeece
Duration: 1 Jan 2010 → …

Conference

ConferenceThe 3rd International Conference on PErvasive Technologies Related to Assistive Environments
Period1/01/10 → …

Fingerprint

Gait analysis
Accelerometers
Multilayer neural networks
Signal to noise ratio
Neural networks

Cite this

Yang, M., Zheng, H., Wang, H., McClean, S., Hall, J., & Harris, N. (2010). Assessing accelerometer based gait features to support gait analysis for people with complex regional pain syndrome. In Unknown Host Publication (pp. Artical 48). http://portal.acm.org/citation.cfm?doid=1839294.1839352. https://doi.org/10.1145/1839294.1839352
Yang, Mingjing ; Zheng, Huiru ; Wang, Haiying ; McClean, Sally ; Hall, Jane ; Harris, Nigel. / Assessing accelerometer based gait features to support gait analysis for people with complex regional pain syndrome. Unknown Host Publication. http://portal.acm.org/citation.cfm?doid=1839294.1839352, 2010. pp. Artical 48
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title = "Assessing accelerometer based gait features to support gait analysis for people with complex regional pain syndrome",
abstract = "In this paper, we explored the feasibility of analysing gait patterns during the Short Physical Performance Battery test by using an accelerometer to record the movement of the subject. 12 subjects with Complex Regional Pain Syndrome (CRPS) and 10 control subjects were recruited in this study. 21 gait features including temporal, frequency, regularity and symmetric information were extracted from each recording. The differences of each feature value on control subjects and patient subjects were assessed and compared. Features were selected based on the signal to noise ratio (SNR) ranking. Multilayer perceptron neural-networks were employed to differentiate between the normal and abnormal gait patterns. The result shows when using five features the best classification accuracy (97.5{\%}) was achieved. It is feasible to discriminate the patients with CRPS from the control subjects using a small set of gait features extracted from walking acceleration data recorded during the SPPB test.",
author = "Mingjing Yang and Huiru Zheng and Haiying Wang and Sally McClean and Jane Hall and Nigel Harris",
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Yang, M, Zheng, H, Wang, H, McClean, S, Hall, J & Harris, N 2010, Assessing accelerometer based gait features to support gait analysis for people with complex regional pain syndrome. in Unknown Host Publication. http://portal.acm.org/citation.cfm?doid=1839294.1839352, pp. Artical 48, The 3rd International Conference on PErvasive Technologies Related to Assistive Environments, 1/01/10. https://doi.org/10.1145/1839294.1839352

Assessing accelerometer based gait features to support gait analysis for people with complex regional pain syndrome. / Yang, Mingjing; Zheng, Huiru; Wang, Haiying; McClean, Sally; Hall, Jane; Harris, Nigel.

Unknown Host Publication. http://portal.acm.org/citation.cfm?doid=1839294.1839352, 2010. p. Artical 48.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Assessing accelerometer based gait features to support gait analysis for people with complex regional pain syndrome

AU - Yang, Mingjing

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AU - McClean, Sally

AU - Hall, Jane

AU - Harris, Nigel

PY - 2010

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N2 - In this paper, we explored the feasibility of analysing gait patterns during the Short Physical Performance Battery test by using an accelerometer to record the movement of the subject. 12 subjects with Complex Regional Pain Syndrome (CRPS) and 10 control subjects were recruited in this study. 21 gait features including temporal, frequency, regularity and symmetric information were extracted from each recording. The differences of each feature value on control subjects and patient subjects were assessed and compared. Features were selected based on the signal to noise ratio (SNR) ranking. Multilayer perceptron neural-networks were employed to differentiate between the normal and abnormal gait patterns. The result shows when using five features the best classification accuracy (97.5%) was achieved. It is feasible to discriminate the patients with CRPS from the control subjects using a small set of gait features extracted from walking acceleration data recorded during the SPPB test.

AB - In this paper, we explored the feasibility of analysing gait patterns during the Short Physical Performance Battery test by using an accelerometer to record the movement of the subject. 12 subjects with Complex Regional Pain Syndrome (CRPS) and 10 control subjects were recruited in this study. 21 gait features including temporal, frequency, regularity and symmetric information were extracted from each recording. The differences of each feature value on control subjects and patient subjects were assessed and compared. Features were selected based on the signal to noise ratio (SNR) ranking. Multilayer perceptron neural-networks were employed to differentiate between the normal and abnormal gait patterns. The result shows when using five features the best classification accuracy (97.5%) was achieved. It is feasible to discriminate the patients with CRPS from the control subjects using a small set of gait features extracted from walking acceleration data recorded during the SPPB test.

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Yang M, Zheng H, Wang H, McClean S, Hall J, Harris N. Assessing accelerometer based gait features to support gait analysis for people with complex regional pain syndrome. In Unknown Host Publication. http://portal.acm.org/citation.cfm?doid=1839294.1839352. 2010. p. Artical 48 https://doi.org/10.1145/1839294.1839352