A Technical Framework for Musical Biofeedback in Stroke Rehabilitation

In this article, we present a technical framework aimed at facilitating musical biofeedback research in poststroke movement rehabilitation. The framework comprises wireless wearable inertial sensors and software built with inexpensive and open-source tools. The software enables layered and adjustabl...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on human-machine systems 2022-04, Vol.52 (2), p.220-231
Main Authors: Kantan, Prithvi, Spaich, Erika G., Dahl, Sofia
Format: Article
Language:English
Subjects:
Balance
Biofeedback
Biological control systems
Gait
Inertial sensing devices
interactive sonification
Microprocessors
Music
music intervention
neurorehabilitation
Open source software
Psychological factors
Real-time systems
Rehabilitation
Sensors
Software
stroke
Task analysis
Training
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this article, we present a technical framework aimed at facilitating musical biofeedback research in poststroke movement rehabilitation. The framework comprises wireless wearable inertial sensors and software built with inexpensive and open-source tools. The software enables layered and adjustable music synthesis 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 subacute stroke patients indicated that the interactions were clinically feasible. Expert interviews with a focus group of clinicians were also conducted, where these interactions were deemed as meaningful and relevant to clinical protocols, with comprehensible feedback (albeit sometimes unpleasant or disturbing) for several patient types. We carried out system benchmarking, finding that the system has sufficiently short loop delays (\sim90 ms) and a healthy sensing range (>9 m) and is computationally efficient (11.1% peak CPU usage on a quad-core processor). Future studies will focus on using this framework with patients to both develop the interactions further and measure their effects on motor learning, performance retention, and psychological factors to help gauge their true clinical potential.
ISSN:2168-2291
2168-2305
DOI:10.1109/THMS.2021.3137013