aceThe Sleep-Wake Classification Performance of Pediatric-Trained Machine Learning Algorithms for Raw Accelerometer Data
Study objectives Actigraphy sleep-wake classification methods increasingly seek to leverage raw acceleration data and machine-learning-based classification, but performance evaluation in pediatrics is limited. We trained machine-learning models using pediatric data and compared their sleep-wake classification performance with existing algorithms for children. Methods Sixty-five children (46% female, ages 5.3-17.7 years) completed in-lab overnight polysomnography and wore a GENEActiv device on their non-dominant…
A machine-learning classifier trained on children's data scored sleep and wake more accurately than an adult-trained random forest and GGIR algorithms when checked against lab sleep recordings.
The best-performing pediatric classifier still underestimated total sleep duration by an average of 25 minutes per night.
The evaluation involved only 65 children, one wrist-worn device model and in-lab overnight recordings, so performance in other devices, settings or populations is not shown by the study.
Evidence
- Peer-reviewedSLEEP2026-10-24
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Truvace Impact Record TRV-2026-2267, v1: “The Sleep-Wake Classification Performance of Pediatric-Trained Machine Learning Algorithms for Raw Accelerometer Data.” Truvace, 2026-10-26. /record/TRV-2026-2267 (accessed at citation time). sha256 0dc51b835d8debd9…
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