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Application of Inertial Measurement Units and Machine Learning Classification in Cerebral Palsy: Randomized Controlled Trial

dc.contributor.authorKhaksar, Siavash
dc.contributor.authorPan, Huizhu
dc.contributor.authorBorazjani, Bita
dc.contributor.authorMurray, Iain
dc.contributor.authorAgrawal, Himanshu
dc.contributor.authorLiu, W
dc.contributor.authorElliott, Catherine
dc.contributor.authorImms, C
dc.contributor.authorCampbell, Amity
dc.contributor.authorWalmsley, Corrin
dc.date.accessioned2021-10-20T17:14:41Z
dc.date.available2021-10-20T17:14:41Z
dc.date.issued2021
dc.identifier.citationKhaksar, S. Khaksar S, Pan H, Borazjani B, Murray I, Agrawal H, Liu W, Elliott C, Imms C, Campbell A, Walmsley C. 2021. Application of Inertial Measurement Units and Machine Learning Classification in Cerebral Palsy: Randomized Controlled Trial. JMIR Rehabilitation and Assistive Technologies. 8 (4): Article No. e29769
dc.identifier.urihttp://hdl.handle.net/20.500.11937/86125
dc.identifier.doi10.2196/29769
dc.relation.sponsoredbyhttp://purl.org/au-research/grants/nhmrc/1057997
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleApplication of Inertial Measurement Units and Machine Learning Classification in Cerebral Palsy: Randomized Controlled Trial
dc.typeJournal Article
dcterms.abstractBackground: Cerebral palsy (CP) is a physical disability that affects movement and posture. Approximately 17 million people worldwide and 34,000 people in Australia are living with CP. In clinical and kinematic research, goniometers and inclinometers are the most commonly used clinical tools to measure joint angles and positions in children with CP. Objective: This paper presents collaborative research between the School of Electrical Engineering, Computing and Mathematical Sciences at Curtin University and a team of clinicians in a multicenter randomized controlled trial involving children with CP. This study aims to develop a digital solution for mass data collection using inertial measurement units (IMUs) and the application of machine learning (ML) to classify the movement features associated with CP to determine the effectiveness of therapy. The results were calculated without the need to measure Euler, quaternion, and joint measurement calculation, reducing the time required to classify the data. Methods: Custom IMUs were developed to record the usual wrist movements of participants in 2 age groups. The first age group consisted of participants approaching 3 years of age, and the second age group consisted of participants approaching 15 years of age. Both groups consisted of participants with and without CP. The IMU data were used to calculate the joint angle of the wrist movement and determine the range of motion. A total of 9 different ML algorithms were used to classify the movement features associated with CP. This classification can also confirm if the current treatment (in this case, the use of wrist extension) is effective. Results: Upon completion of the project, the wrist joint angle was successfully calculated and validated against Vicon motion capture. In addition, the CP movement was classified as a feature using ML on raw IMU data. The Random Forrest algorithm achieved the highest accuracy of 87.75% for the age range approaching 15 years, and C4.5 decision tree achieved the highest accuracy of 89.39% for the age range approaching 3 years. Conclusions: Anecdotal feedback from Minimising Impairment Trial researchers was positive about the potential for IMUs to contribute accurate data about active range of motion, especially in children, for whom goniometric methods are challenging. There may also be potential to use IMUs for continued monitoring of hand movements throughout the day.
dcterms.source.titleJMIR Rehabilitation and Assistive Technologies
dc.date.updated2021-10-20T17:14:40Z
curtin.departmentSchool of Elec Eng, Comp and Math Sci (EECMS)
curtin.accessStatusOpen access
curtin.facultyFaculty of Science and Engineering
curtin.contributor.orcidKhaksar, Siavash [0000-0002-1944-1418]


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