Dahl, Sofia

Kantan, Prithvi Ravi

Spaich, Erika G.

English

In this article, we present a technical frameworkaimed at facilitating musical biofeedback research in poststrokemovement rehabilitation. The framework comprises wireless wearableinertial sensors and software built with inexpensive and opensourcetools. The software enables layered and adjustable musicsynthesis and has a generic movement–music mapping module.Using this, we designed digital musical interactions for balance,sit-to-stand, and gait training. Preliminary trials with subacutestroke patients indicated that the interactions were clinically feasible.Expert interviews with a focus group of clinicians were alsoconducted, where these interactions were deemed as meaningfuland relevant to clinical protocols, with comprehensible feedback(albeit sometimes unpleasant or disturbing) for several patienttypes.We carried out system benchmarking, finding that the systemhas sufficiently short loop delays (∼90 ms) and a healthy sensingrange (&gt;9 m) and is computationally efficient (11.1% peak CPUusage on a quad-core processor). Future studies will focus onusing this framework with patients to both develop the interactionsfurther and measure their effects on motor learning, performanceretention, and psychological factors to help gauge their true clinicalpotential

A Technical Framework for Musical Biofeedback in Stroke Rehabilitation

https://vbn.aau.dk/files/469798192/A_Technical_Framework_for_Musical_Biofeedback_in_Stroke_Rehabilitation.pdf

A Technical Framework for Musical Biofeedback in Stroke Rehabilitation

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