ArticleJMIR formative research2022
Objective Measurement of Hyperactivity Using Mobile Sensing and Machine Learning: Pilot Study.
Article in JMIR formative research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Individualized prediction models in ADHD: a systematic review and meta-regression.Molecular psychiatry · 2024Pooled it
- Automated Detection of Neurodevelopmental Disorders Using Face-to-Face Mobile Technology Among Typically Developing Greek Children: Randomized Controlled Trial.JMIR formative research · 2024Trial
- Associations Between ADHD Symptoms and Diagnosis Across the Preschool Period.Journal of attention disorders · 2026Article
- Wearable-Derived Axis-Specific Motor Signatures of ADHD Symptoms in Children and Adolescents.Biosensors · 2026Article
- Applying artificial intelligence in neurodevelopmental disorders management and research.European journal of medical research · 2026Review
- Leveraging point-of-view camera and MediaPipe for objective hyperactivity assessment in preschool ADHD.Frontiers in psychiatry · 2026Article
- Wearables in ADHD: Monitoring and Intervention-Where Are We Now?Diagnostics (Basel, Switzerland) · 2025Review
- Wearable sensors in paediatric neurology.Developmental medicine and child neurology · 2025Review
- Artificial intelligence in ADHD assessment: a comprehensive review of research progress from early screening to precise differential diagnosis.Frontiers in artificial intelligence · 2025Review
- Digital assessments for children and adolescents with ADHD: a scoping review.Frontiers in digital health · 2024Article
- Machine Learning-Based Aggression Detection in Children with ADHD Using Sensor-Based Physical Activity Monitoring.Sensors (Basel, Switzerland) · 2023Article
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7 authors.
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Abstract
backgroundAlthough hyperactivity is a core symptom of attention-deficit/hyperactivity disorder (ADHD), there are no objective measures that are widely used in clinical settings.
objectiveWe describe the development of a smartwatch app to measure hyperactivity in school-age children. The LemurDx prototype is a software system for smartwatches that uses wearable sensor technology and machine learning to measure hyperactivity. The goal is to differentiate children with ADHD combined presentation (a combination of inattentive and hyperactive/impulsive presentations) or predominantly hyperactive/impulsive presentation from children with typical levels of activity.
methodsIn this pilot study, we recruited 30 children, aged 6 to 11 years, to wear a smartwatch with the LemurDx app for 2 days. Parents also provided activity labels for 30-minute intervals to help train the algorithm. Half of the participants had ADHD combined presentation or predominantly hyperactive/impulsive presentation (n=15), and half were in the healthy control group (n=15).
resultsThe results indicated high usability scores and an overall diagnostic accuracy of 0.89 (sensitivity=0.93; specificity=0.86) when the motion sensor output was paired with the activity labels.
conclusionsState-of-the-art sensors and machine learning may provide a promising avenue for the objective measurement of hyperactivity.
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