Substantial amount of research in home-use health monitoring techniques has emerged given growing global health awareness and ageing population in recent decades. These sensor-driven home-use healthcare applications encourage patient involvement at home during daytime activities and nighttime sleep, effectively help assess patients conditions away from clinics and hospitals, and significantly reduce the number of infirmary visits.However, there are two main issues in current wearable/remote sensor-based home-use health monitoring applications: 1) portable human motion analysis systems that are commercially available still require substantial amount of manual effort to process the measurements, which is time consuming and thus impractical for long-term home-use health monitoring, and 2) current sleep-related health monitoring applications are intrusive to the body, limited to measuring the respiration rate and sleep duration, or not clinically validated to demonstrate their efficacy.In this dissertation, we overcome the drawbacks of current health monitoring systems as follows. For lower limb motion analysis, we propose an alternative to state of the art optical motion analysis systems, cost-effective and portable, single-camera system.For upper limb motion analysis, we track all relevant body joints simultaneously, and classify the post-stroke recovery levels based on features extracted from the tracked body-joint trajectories. For abnormal respiratory event detection during sleep, we propose to record video and audio of a patient using a depth camera during his/her sleep, and extract relevant features to train a classifier for detection of the abnormal respiratory events scored manually by a scientific officer based on data collected by a clinical-use sleeping device.The main contribution of this dissertation lies in proposing new application-driven algorithms for advancing cost-effective human limb motion analysis and sleep monitoring healthcare techniques, including an autonomous detection scheme for finding the initial and final frames that are of interest for video analysis, a single marker tracking scheme that is based on the Kalman filter and Structural Similarity image quality assessment,an autonomous gait event detection scheme that is based on the features of the relative positions of the markers, a scheme classification of the post-stroke recovery level by minimization of graph total variation with graph-based signal processing, an alternating-frame depth video coding scheme, a depth video temporal denoising scheme using a motion vector graph smoothness prior, and a dual-ellipse model that can efficiently track the torso motion during a person is sleeping. Experimental results show that, both the autonomous frame-of-interest detection and gait event detection show high detections rates. The validation of tracking in terms of the knee angle, shoulder movement, trunk tilt and elbow movement with a gold standard optical motion analysis system shows R-squared value larger than 0.95. The graph-based classification scheme has the potential to accurately classify participants into different stroke groups. Our depth video coding scheme outperforms a competitor that records only the 8 most significant bits. Our temporal denoising scheme reduces the flickering effect without ever-smoothing. Finally, our trained classifiers can deduce respiratory events with high accuracy. Overall, our proposed limb motion analysis system offers an alternative,inexpensive and convenient solution for clinical gait and upper limb motion analysis,and our proposed sleep monitoring system can reliably detect abnormal respiratory events using our extracted video and audio features.
|Date of Award||7 May 2017|
- University Of Strathclyde
|Sponsors||University of Strathclyde|
|Supervisor||Vladimir Stankovic (Supervisor) & Lina Stankovic (Supervisor)|