  We here present work a generalized low-level technical framework aimed to
provide musical biofeedback in post-stroke balance and gait rehabilitation,
built by an iterative user-centered process. The framework comprises wireless
wearable inertial sensors and a software interface developed using inexpensive
and open-source tools. The interface enables layered and adjustable music
synthesis, real-time control over biofeedback parameters in several training
modes, and extensive supplementary functionality. We evaluated the system in
terms of technical performance, finding that the system has sufficiently low
loop delay (~90 ms), good sensor range (>9 m) and low computational load even
in its most demanding operation mode. In a series of expert interviews,
selected training interactions using the system were deemed by clinicians to be
meaningful and relevant to clinical protocols with comprehensible feedback
(albeit sometimes unpleasant or disturbing) for a wide patient demographic.
Future studies will focus on using this framework with real patients to both
develop the interactions further and measure their effects during therapy.
