Upper limb deficits post stroke affect up to 60% of stroke survivors. The assessment of motor deficits post stroke is important for identifying rehabilitation goals and assessing treatment efficacy. Current clinical tools used to assess motor impairment utilize clinical observation to describe the performance of diagnostic motor tasks. However there are some concerns regarding the ability of these scales to fully describe the quality of performance, and detect small but important changes which reflect motor recovery. Kinematic analysis has been increasingly suggested to augment clinical assessment; however, current kinematic tools are not well suited to the time and financial constraints of a clinical environment. The objective of this thesis was to investigate the feasibility of utilizing low-cost, depth sensing technology (Kinect sensor) to augment the current upper limb stroke assessment. Study one characterizes the accuracy of the Kinect sensor, and defines optimal markers and conditions for data collection. Results revealed sufficient ability to quantify metrics for the hand, and the trunk. Study two explored the feasibility of clinical use for the Kinect sensor, specifically its ability to distinguish kinematic performance between the affected and less-affected limbs within an individual, and differences in the affected limb between individuals. Results from study 2 indicated that the Kinect is able to identify interlimb differences and correlations with upper limb impairment scores for some kinematic metrics. Findings from this thesis suggest a potential use for the Kinect in a clinical environment for the purposes of upper limb stroke assessment; however, there are many factors and limitations which need to be considered prior to its use.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:OWTU.10012/8548 |
| Date | 20 June 2014 |
| Creators | Tran, Johnathan |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
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