EasiSMR: Recognizing Non-Specific Low Back Pain Symptomatic Muscles Using Multi-Muscles Fusion based Machine Learning

Wang, Nian; Xiao, Jing; Li, Cui

doi:10.1109/compcomm.2018.8780772

Cited by 3 publications

(2 citation statements)

References 10 publications

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“…There is a significant difference in the patterns of muscles firing phase between symptomatic and healthy muscles, resulting in different muscle compensation in order to reduce the load on a painful tissue to protect from further pain [49]. Based on that fact, some studies used ML techniques to accurately locate the symptomatic muscles after collecting sEMG [32]. ML can also be used in the development of an assessment system based on a modified fuzzy inference system consisting in a determined number of rules, which could be used to support treatment decisions [33,40].…”

Section: Discussionmentioning

confidence: 99%

“…Information on inclusion and exclusion criteria were absent in almost all the studies. Ten studies [30, 32-36, 38, 39, 41, 42] made an effort to have a control group but definition of controls was not always the same: in four studies control group included healthy patients [30,32,34,42], one used datasets from healthy persons [35]; in one study [33] the control group was represented by LBP surgical patients; in three studies [36,38,41] the comparison used was experts suggestions/ classification/evaluation (versus ML system suggestions); in one study [34] the algorithm results were compared against a back screening tool results; in one study [39] the results from active LBP patients were compared with healthy controls and with remission LBP patients.…”

Section: Characteristics Of Studies Includedmentioning

confidence: 99%

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Machine Learning Applied to Low Back Pain Rehabilitation – A Systematic Review

Amorim

Paulo

Silva

et al. 2021

International Journal of Digital Health

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Background: 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 by 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 rehabilitation of people with LBP. To develop an understanding on 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.Materials and Methods: a systematic review was performed based on research material obtained from literature indexed on MEDLINE, Cochrane Central Register of Controlled Trials (CENTRAL), Scopus, Web of Science and IEEE Xplore databases to locate papers focused on the use of ML applied to rehabilitation of LBP.Results: after revision of the inclusion and exclusion criteria using the PRISMA methodology, only 14 studies remained for the analysis that is presented as a qualitative synthesis.Conclusions: ML approaches applied to rehabilitation could help health professionals and LBP patients to manage this condition that affects a significant amount of the active population. ML could be applied to support clinical decisions and to guide patients self-manage their LBP remotely, which makes it a potential telerehabilitation solution. More and better studies, with more participants and following guidelines for best research practice are needed to strengthen the clinical evidence.

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