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 opensource
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 (∼90 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
Original languageEnglish
JournalIEEE Transactions on Human-Machine Systems
Issue number2
Pages (from-to)220-231
Number of pages12
Publication statusPublished - 2022


  • Balance
  • Biological control systems
  • Music
  • Real-time systems
  • Sensors
  • Software
  • Task analysis
  • Training
  • biofeedback
  • gait
  • interactive sonification
  • music intervention
  • neurorehabilitation
  • stroke


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