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.
Original language | English |
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Title of host publication | Unknown Host Publication |
Place of Publication | http://portal.acm.org/citation.cfm?doid=1839294.1839352 |
Publisher | Association for Computing Machinery |
Pages | Artical 48 |
Number of pages | 6 |
ISBN (Print) | 978-1-4503-0071-1 |
DOIs | |
Publication status | Published (in print/issue) - 2010 |
Event | The 3rd International Conference on PErvasive Technologies Related to Assistive Environments - Samos, Greeece Duration: 1 Jan 2010 → … |
Conference
Conference | The 3rd International Conference on PErvasive Technologies Related to Assistive Environments |
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Period | 1/01/10 → … |