Autonomous modeling of repetitive movement for rehabilitation exercise monitoring
1
Issued Date
2022-12-01
Resource Type
eISSN
14726947
Scopus ID
2-s2.0-85133331640
Pubmed ID
35780122
Journal Title
BMC Medical Informatics and Decision Making
Volume
22
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
BMC Medical Informatics and Decision Making Vol.22 No.1 (2022)
Suggested Citation
Jatesiktat P., Lim G.M., Kuah C.W.K., Anopas D., Ang W.T. Autonomous modeling of repetitive movement for rehabilitation exercise monitoring. BMC Medical Informatics and Decision Making Vol.22 No.1 (2022). doi:10.1186/s12911-022-01907-5 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/84235
Title
Autonomous modeling of repetitive movement for rehabilitation exercise monitoring
Author(s)
Other Contributor(s)
Abstract
Background: Insightful feedback generation for daily home-based stroke rehabilitation is currently unavailable due to the inefficiency of exercise inspection done by therapists. We aim to produce a compact anomaly representation that allows a therapist to pay attention to only a few specific sections in a long exercise session record and boost their efficiency in feedback generation. Methods: This study proposes a data-driven technique to model a repetitive exercise using unsupervised phase learning on an artificial neural network and statistical learning on principal component analysis (PCA). After a model is built on a set of normal healthy movements, the model can be used to extract a sequence of anomaly scores from a movement of the same prescription. Results: The method not only works on a standard marker-based motion capture system but also performs well on a more compact and affordable motion capture system based-on Kinect V2 and wrist-worn inertial measurement units that can be used at home. An evaluation of four different exercises shows its potential in separating anomalous movements from normal ones with an average area under the curve (AUC) of 0.9872 even on the compact motion capture system. Conclusions: The proposed processing technique has the potential to help clinicians in providing high-quality feedback for telerehabilitation in a more scalable way.