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Title: Machine learning and statistical approaches to analysis of wearable sensory gait data
Author: Yang, Mingjing
ISNI:       0000 0004 2714 7598
Awarding Body: University of Ulster
Current Institution: Ulster University
Date of Award: 2012
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This thesis alms to investigate how machine learning and statistical approaches can be employed to support the analysis of gait patterns captured by wearable sensors data. The thesis has proposed a machine learning and statistical approaches based framework (MSGAF) for wearable sensory gait data analysis. It has been applied to two clinical applications: discrimination of disturbed gait affected by neurodegenerative diseases and identification of patients with complex region pain syndrome (CRPS). The results demonstrate that: 1) It is feasible to discriminate gait patterns related three neurodegenerative diseases based on wearable sensory gait analysis. 2) It is feasible to assess the physical performance of patients with CRPS based on the analysis of accelerometry gait data. 3) Accelerometry gait data collected in a short distance can provide a large amount of information for gait monitoring in a home based environment. Two new feature selection algorithms have been proposed to find out the optimal feature set for a given condition related to gait disorder. A combination of gait features were proposed to analyse accelerometer based gait data. A device independent tool (i.e. iGAIT) has been developed to display and analyse gait acceleration data. It provides interactive functionality allowing users to manually adjust the analysis progress. A smartphone with an embedded accelerometer has been proposed to be a novel gait measurement technique. The utility and reliability of using a smartphone in gait pattern monitoring was studied. The impact of sampling frequency on the gait features extracted from accelerometer data was systematically investigated. The concept of contextual gait analysis was also proposed, which takes account of the impact of walking contexture and makes it possible to monitor gait pattern in a real life environment for a long term. Two data sets collected by an accelerometer were used, climbing stairs data and urban walking data.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available