ArticleSensors (Basel, Switzerland)2023
Machine Learning-Based Aggression Detection in Children with ADHD Using Sensor-Based Physical Activity Monitoring.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 37 citations in OpenAlex.
- Using Wearable Technology to Predict the Occurrence of Severe Behavior Problems among Neurodiverse Individuals: A Systematic Review.Perspectives on behavior science · 2026Review
- Advancing Ambulatory Assessment Studies on Psychopathic Traits: A Response to Commentaries.Clinical psychological science : a journal of the Association for Psychological Science · 2025Article
- ViT-BiLSTM Multimodal Learning for Paediatric ADHD Recognition: Integrating Wearable Sensor Data with Clinical Profiles.Sensors (Basel, Switzerland) · 2025Article
- Wearables in ADHD: Monitoring and Intervention-Where Are We Now?Diagnostics (Basel, Switzerland) · 2025Review
- SmartBoot: Real-Time Monitoring of Patient Activity via Remote Edge Computing Technologies.Sensors (Basel, Switzerland) · 2025Article
- Wearable sensors in paediatric neurology.Developmental medicine and child neurology · 2025Review
- Predicting cognitive frailty in community-dwelling older adults: a machine learning approach based on multidomain risk factors.Scientific reports · 2025Article
- Artificial intelligence in ADHD assessment: a comprehensive review of research progress from early screening to precise differential diagnosis.Frontiers in artificial intelligence · 2025Review
- Advanced machine learning techniques reveal multidimensional EEG abnormalities in children with ADHD: a framework for automatic diagnosis.Frontiers in psychiatry · 2025Article
- Improving the Concrete Crack Detection Process via a Hybrid Visual Transformer Algorithm.Sensors (Basel, Switzerland) · 2024Article
- Harnessing Speech-Derived Digital Biomarkers to Detect and Quantify Cognitive Decline Severity in Older Adults.Gerontology · 2024Article
- Putting the usability of wearable technology in forensic psychiatry to the test: a randomized crossover trial.Frontiers in psychiatry · 2024Article
- Breast Cancer Detection with an Ensemble of Deep Learning Networks Using a Consensus-Adaptive Weighting Method.Journal of imaging · 2023Article
- Expanding contingency learning models of aggression in youth: Conceptual and methodological considerations to enhance clinical utility.Aggression and violent behaviorArticle
Corrections and comments
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Authors and funding
11 authors at 1 institution in 1 country.
Funding
Abstract
Aggression in children is highly prevalent and can have devastating consequences, yet there is currently no objective method to track its frequency in daily life. This study aims to investigate the use of wearable-sensor-derived physical activity data and machine learning to objectively identify physical-aggressive incidents in children. Participants (n = 39) aged 7 to 16 years, with and without ADHD, wore a waist-worn activity monitor (ActiGraph, GT3X+) for up to one week, three times over 12 months, while demographic, anthropometric, and clinical data were collected. Machine learning techniques, specifically random forest, were used to analyze patterns that identify physical-aggressive incident with 1-min time resolution. A total of 119 aggression episodes, lasting 7.3 ± 13.1 min for a total of 872 1-min epochs including 132 physical aggression epochs, were collected. The model achieved high precision (80.2%), accuracy (82.0%), recall (85.0%), F1 score (82.4%), and area under the curve (89.3%) to distinguish physical aggression epochs. The sensor-derived feature of vector magnitude (faster triaxial acceleration) was the second contributing feature in the model, and significantly distinguished aggression and non-aggression epochs. If validated in larger samples, this model could provide a practical and efficient solution for remotely detecting and managing aggressive incidents in children.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.