Related Experiment Video
Updated: Jul 1, 2025
A Method for Quantifying Upper Limb Performance in Daily Life Using Accelerometers
Published on: April 21, 2017
Measuring functional hand use in children with unilateral cerebral palsy using accelerometry and machine learning
Sunaal P Mathew1,2, Jaclyn Dawe3,4, Kristin E Musselman3,4,5
1Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada.
Insights
Wearable sensors with machine learning show promise for tracking functional hand use in children with unilateral cerebral palsy. Traditional methods struggle to differentiate between functional and non-functional hand movements.
Area of Science:
- Biomedical Engineering
- Rehabilitation Technology
- Pediatric Neurology
Background:
- Unilateral cerebral palsy (CP) significantly impacts functional hand use in children.
- Objective and quantitative methods are needed to assess hand function in daily activities.
- Current assessment methods may not fully capture the nuances of functional hand use during naturalistic tasks.
Purpose of the Study:
- To investigate the efficacy of wearable sensors for measuring functional hand use in children with unilateral CP.
- To compare machine learning algorithms with traditional activity count methods for analyzing hand function data.
- To determine the feasibility of using accelerometry for quantitative monitoring of hand use.
Main Methods:
- Collected dual wrist-worn accelerometry data from 10 children with unilateral CP during play sessions.
- Utilized video observers to label functional and non-functional hand use instances.
- Compared machine learning models against activity count approaches for classifying hand movements.
Main Results:
- The best-performing machine learning model achieved high precision and recall (F1=0.896) when trained individually, correlating strongly (r=0.990) with video observations.
- Individualized machine learning models demonstrated significant correlation and strong agreement with expert video analysis.
- Performance decreased significantly (F1=0.584) in a leave-one-subject-out validation, indicating a need for robust individualization.
- The activity count method failed to differentiate functional from non-functional hand use and showed no significant correlation with video observations.
Conclusions:
- Wearable accelerometry combined with machine learning holds potential for quantitative monitoring of functional hand use in children with unilateral CP.
- Further development is needed to enhance the generalizability of machine learning models.
- Traditional activity count methods are insufficient for distinguishing functional from non-functional hand use in this population.
Aim:
To investigate wearable sensors for measuring functional hand use in children with unilateral cerebral palsy (CP).
Method:
Dual wrist-worn accelerometry data were collected from three females and seven males with unilateral CP (mean age = 10 years 2 months [SD 3 years]) while performing hand tasks during video-recorded play sessions. Video observers labelled instances of functional and non-functional hand use. Machine learning was compared to the conventional activity count approach for identifying unilateral hand movements as functional or non-functional. Correlation and agreement analyses compared the functional usage metrics derived from each method.
Results:
The best-performing machine learning approach had high precision and recall when trained on an individual basis (F1 = 0.896 [SD 0.043]). On an individual basis, the best-performing classifier showed a significant correlation (r = 0.990, p < 0.001) and strong agreement (bias = 0.57%, 95% confidence interval = -4.98 to 6.13) with video observations. When validated in a leave-one-subject-out scenario, performance decreased significantly (F1 = 0.584 [SD 0.076]). The activity count approach failed to detect significant differences in non-functional or functional hand activity and showed no significant correlation or agreement with the video observations.
Interpretation:
With further development, wearable accelerometry combined with machine learning may enable quantitative monitoring of everyday functional hand use in children with unilateral CP.
What This Paper Adds:
Wearable (wrist-worn) accelerometry with machine learning shows potential for measuring functional hand use in children with unilateral cerebral palsy. Traditional activity count accelerometry cannot distinguish functional from non-functional hand use.
More Related Videos
06:25Capturing Representative Hand Use at Home Using Egocentric Video in Individuals with Upper Limb Impairment
Published on: December 23, 2020
07:34Functional MRI in Conjunction with a Novel MRI-compatible Hand-induced Robotic Device to Evaluate Rehabilitation of Individuals Recovering from Hand Grip Deficits
Published on: November 23, 2019