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Application of Inertial Measurement Units and Machine Learning Classification in Cerebral Palsy: Randomized
Siavash Khaksar1, Huizhu Pan1, Bita Borazjani1
1School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Bentley, Australia.
Insights
This study developed a digital solution using inertial measurement units (IMUs) and machine learning (ML) to classify cerebral palsy (CP) movement features. This approach offers accurate, efficient data collection for assessing therapy effectiveness in children with CP.
Area of Science:
- Biomedical Engineering
- Rehabilitation Technology
- Machine Learning in Healthcare
Background:
- Cerebral palsy (CP) affects movement and posture, impacting millions globally and in Australia.
- Traditional tools like goniometers and inclinometers are used for joint angle measurement in CP research.
- Current methods can be time-consuming and challenging, particularly for pediatric populations.
Purpose of the Study:
- To develop a digital solution for mass data collection in children with CP using inertial measurement units (IMUs).
- To apply machine learning (ML) algorithms to classify CP movement features and assess therapy effectiveness.
- To reduce the time required for data classification by eliminating the need for Euler, quaternion, and joint measurements.
Main Methods:
- Custom IMUs were developed to record wrist movements in two age groups (approaching 3 and 15 years) of participants with and without CP.
- IMU data were utilized to calculate wrist joint angles and range of motion.
- Nine ML algorithms were employed to classify CP-associated movement features and evaluate treatment efficacy (e.g., wrist extension).
Main Results:
- Wrist joint angle calculations were successfully performed and validated against Vicon motion capture.
- ML algorithms classified CP movement features using raw IMU data.
- The Random Forest algorithm achieved 87.75% accuracy for the older age group, while C4.5 decision tree achieved 89.39% for the younger group.
Conclusions:
- IMUs show potential for accurate active range of motion data collection in children with CP, overcoming challenges of goniometric methods.
- Positive anecdotal feedback suggests IMUs could be valuable for ongoing hand movement monitoring in children.
- The developed digital solution offers a promising avenue for efficient and accurate CP assessment and therapy evaluation.
Background:
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.
Trial Registration:
Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12614001276640, https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=367398; ANZCTR ACTRN12614001275651, https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=367422.
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