Systematic Review on the use of Machine Learning in Low Back Pain Rehabilitation
Published in Open Access in the first issue of the international journal International Journal of Digital Health. This systematic review is registered with PROSPERO (ID 232769).
Low back pain (LBP) is the leading cause of disability worldwide and an important cause of work absenteeism in the active population. As a recurrent condition, prevention is crucial. Home exercises are effective, but adherence and accurate performance of the exercises are difficult to monitor doctors and therapists. Machine learning (ML) applied to rehabilitation systems could be a solution to address telerehabilitation for people with chronic LBP if it holds sufficient accuracy in monitoring adherence performance while providing patient guidance. The aim was to search and review studies that have used ML techniques for the rehabilitation of people with LBP. To develop an understanding of the outcomes measured, the clinical setting (face-to-face rehabilitation or remote rehabilitation) where interventions took place, and the clinical research methodology that has been used.
Amorim, P., Paulo, J., Silva, P. A., Peixoto, P., Castelo-Branco, M., & Martins, H. (2021). Machine Learning Applied to Low Back Pain Rehabilitation – A Systematic Review. International Journal of Digital Health, 1, 10. doi:10.29337/ijdh.34