Automated Assessment of Balance Rehabilitation Exercises With a Data-Driven Scoring Model: Algorithm Development and Validation Study.
Vassilios Tsakanikas, Dimitris Gatsios, Athanasios Pardalis and 9 others
PMID 36044258WHAT IT FOUND
An automated scoring model judged balance exercises from sensor data as well as two physiotherapists did, especially for sitting and standing tasks.
It was less reliable for complex walking exercises.
Key findings
01The automated model scored balance exercises with agreement close to that seen between two physiotherapists.
02The model was less accurate for walking exercises than for sitting exercises.
03The model agreed with a physiotherapist better than the earlier rule-based scoring model did.
STILL TO COME
How it was doneWhat they foundWhat it means for PTs
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What it does not show
The data came from a pilot with 20 recruited participants and 19 analysed, so the model may not work as well in a larger or more diverse group. The system was tested in a clinic setup, not in the home environment it is designed for. The model needs to be told the exercise type before scoring, so it cannot support free-choice exercise sessions. The supplied text does not report changes in clinical outcomes such as falls, balance scores, function, adherence, or safety during home use. The dataset was not large enough to test deep learning models, which the authors say might show higher accuracy. Validation used 10-fold cross-validation within the same dataset, not an independent external sample. Two-observer agreement was assessed only on 38 sessions from 4 patients, not on all 1313 exercises.
The easy way to misread this
Do not conclude that automated home balance rehabilitation has been shown to improve patient recovery. The study tested a scoring algorithm on 19 patients in a clinic, did not report clinical outcomes, and cannot recognise the exercise being performed.