Douglass-Kirk, Pedro. 2024. Sonic Sleeve: Reducing Compensatory Movements of the Upper Limb in Participants with Chronic Stroke using Real-time Auditory Feedback. Doctoral thesis, Goldsmiths, University of London [Thesis]

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Abstract or Description

Chronic stroke patients with upper limb impairments are encouraged to undertake many repetitions of movements to aid their rehabilitation. Providing personalised feedback on their repetitions and movement quality could be beneficial. One approach to provide feedback for patients is to map movements directly onto sound. This thesis investigates the use of auditory feedback, particularly to provide patients with real-time knowledge of their movement quality, much as a clinician uses verbal and physical support during therapy sessions. The methodological frameworks underpinning this proof-of-concept work include co-creation, participatory design, and interactive machine learning. Participatory design workshops facilitated collaboration among experts in stroke rehabilitation, music psychology, motor neuroscience, and human computer interaction, resulting in the development Sonic Sleeve, a bespoke stroke rehabilitation system. Iterative case studies parallel to the workshops with service users refined the system. A significant reduction of compensatory movement was observed in the first lab-based experiment that recruited 20 participants with chronic stroke, F(1,18) = 9.424, p=.007, with a large effect size (partial =.344). There was evidence for successful replication with 4 participants with chronic stroke in the home environment. A second set of experiments investigated whether an extended training period with auditory feedback may elicit learning without auditory feedback. However, there was no statistically significant interaction between group and time on the duration of compensatory movement as a proportion of total movement time, F(1.346, 9.422) = 0.453, p = .574, partial η2 = .061. This thesis addresses the limited research on using auditory feedback, specifically patient-selected music, combined with interactive machine learning to reduce compensatory movement and enhance reaching quality in chronic stroke rehabilitation. It makes three key contributions by demonstrating reductions in compensatory movements beyond trunk flexion, integrating patient-selected music to motivate high dose, and introducing an interactive machine learning approach for personalised treatments.

Item Type:	Thesis (Doctoral)
Identification Number (DOI):	https://doi.org/10.25602/GOLD.00037877
Keywords:	Rehabilitation; compensation; kinematics; machine learning; movement; chronic stroke
Departments, Centres and Research Units:	Psychology
Date:	31 October 2024
Item ID:	37877
Date Deposited:	21 Nov 2024 15:59
Last Modified:	21 Nov 2024 16:41
URI:	https://research.gold.ac.uk/id/eprint/37877

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