IFRA: a machine learning-based Instrumented Fall Risk Assessment Scale derived from Instrumented Timed Up and Go test in stroke patients
Journal:
arXiv
Published Date:
Jan 16, 2025
Abstract
Effective fall risk assessment is critical for post-stroke patients. The
present study proposes a novel, data-informed fall risk assessment method based
on the instrumented Timed Up and Go (ITUG) test data, bringing in many mobility
measures that traditional clinical scales fail to capture. IFRA, which stands
for Instrumented Fall Risk Assessment, has been developed using a two-step
process: first, features with the highest predictive power among those
collected in a ITUG test have been identified using machine learning
techniques; then, a strategy is proposed to stratify patients into low, medium,
or high-risk strata. The dataset used in our analysis consists of 142
participants, out of which 93 were used for training (15 synthetically
generated), 17 for validation and 32 to test the resulting IFRA scale (22
non-fallers and 10 fallers). Features considered in the IFRA scale include gait
speed, vertical acceleration during sit-to-walk transition, and turning angular
velocity, which align well with established literature on the risk of fall in
neurological patients. In a comparison with traditional clinical scales such as
the traditional Timed Up & Go and the Mini-BESTest, IFRA demonstrates
competitive performance, being the only scale to correctly assign more than
half of the fallers to the high-risk stratum (Fischer's Exact test p = 0.004).
Despite the dataset's limited size, this is the first proof-of-concept study to
pave the way for future evidence regarding the use of IFRA tool for continuous
patient monitoring and fall prevention both in clinical stroke rehabilitation
and at home post-discharge.
