Ulster University Logo

Ulster Institutional Repository

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

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Yang, Mingjing, Zheng, Huiru, Wang, Haiying, McClean, Sally, Hall, Jane and Harris, Nigel (2010) Assessing accelerometer based gait features to support gait analysis for people with complex regional pain syndrome. In: The 3rd International Conference on PErvasive Technologies Related to Assistive Environments, Samos, Greeece. ACM. 6 pp. [Conference contribution]

Full text not available from this repository.

URL: http://dx.doi.org/10.1145/1839294.1839352

DOI: doi:10.1145/1839294.1839352

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.

Item Type:Conference contribution (Paper)
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Information Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Faculty of Computing & Engineering > Research Graduate School
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Information and Communication Engineering
Computer Science Research Institute > Intelligent Systems Research Centre
Computer Science Research Institute > Smart Environments
ID Code:15820
Deposited By:Dr Huiru Zheng
Deposited On:29 Oct 2010 09:34
Last Modified:29 Oct 2010 09:34

Repository Staff Only: item control page