Cargando…
Machine Learning-Based Aggression Detection in Children with ADHD Using Sensor-Based Physical Activity Monitoring
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 phy...
Autores principales: | , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10221870/ https://www.ncbi.nlm.nih.gov/pubmed/37430862 http://dx.doi.org/10.3390/s23104949 |
_version_ | 1785049559928406016 |
---|---|
author | Park, Catherine Rouzi, Mohammad Dehghan Atique, Md Moin Uddin Finco, M. G. Mishra, Ram Kinker Barba-Villalobos, Griselda Crossman, Emily Amushie, Chima Nguyen, Jacqueline Calarge, Chadi Najafi, Bijan |
author_facet | Park, Catherine Rouzi, Mohammad Dehghan Atique, Md Moin Uddin Finco, M. G. Mishra, Ram Kinker Barba-Villalobos, Griselda Crossman, Emily Amushie, Chima Nguyen, Jacqueline Calarge, Chadi Najafi, Bijan |
author_sort | Park, Catherine |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10221870 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-102218702023-05-28 Machine Learning-Based Aggression Detection in Children with ADHD Using Sensor-Based Physical Activity Monitoring Park, Catherine Rouzi, Mohammad Dehghan Atique, Md Moin Uddin Finco, M. G. Mishra, Ram Kinker Barba-Villalobos, Griselda Crossman, Emily Amushie, Chima Nguyen, Jacqueline Calarge, Chadi Najafi, Bijan Sensors (Basel) Article 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. MDPI 2023-05-21 /pmc/articles/PMC10221870/ /pubmed/37430862 http://dx.doi.org/10.3390/s23104949 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Park, Catherine Rouzi, Mohammad Dehghan Atique, Md Moin Uddin Finco, M. G. Mishra, Ram Kinker Barba-Villalobos, Griselda Crossman, Emily Amushie, Chima Nguyen, Jacqueline Calarge, Chadi Najafi, Bijan Machine Learning-Based Aggression Detection in Children with ADHD Using Sensor-Based Physical Activity Monitoring |
title | Machine Learning-Based Aggression Detection in Children with ADHD Using Sensor-Based Physical Activity Monitoring |
title_full | Machine Learning-Based Aggression Detection in Children with ADHD Using Sensor-Based Physical Activity Monitoring |
title_fullStr | Machine Learning-Based Aggression Detection in Children with ADHD Using Sensor-Based Physical Activity Monitoring |
title_full_unstemmed | Machine Learning-Based Aggression Detection in Children with ADHD Using Sensor-Based Physical Activity Monitoring |
title_short | Machine Learning-Based Aggression Detection in Children with ADHD Using Sensor-Based Physical Activity Monitoring |
title_sort | machine learning-based aggression detection in children with adhd using sensor-based physical activity monitoring |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10221870/ https://www.ncbi.nlm.nih.gov/pubmed/37430862 http://dx.doi.org/10.3390/s23104949 |
work_keys_str_mv | AT parkcatherine machinelearningbasedaggressiondetectioninchildrenwithadhdusingsensorbasedphysicalactivitymonitoring AT rouzimohammaddehghan machinelearningbasedaggressiondetectioninchildrenwithadhdusingsensorbasedphysicalactivitymonitoring AT atiquemdmoinuddin machinelearningbasedaggressiondetectioninchildrenwithadhdusingsensorbasedphysicalactivitymonitoring AT fincomg machinelearningbasedaggressiondetectioninchildrenwithadhdusingsensorbasedphysicalactivitymonitoring AT mishraramkinker machinelearningbasedaggressiondetectioninchildrenwithadhdusingsensorbasedphysicalactivitymonitoring AT barbavillalobosgriselda machinelearningbasedaggressiondetectioninchildrenwithadhdusingsensorbasedphysicalactivitymonitoring AT crossmanemily machinelearningbasedaggressiondetectioninchildrenwithadhdusingsensorbasedphysicalactivitymonitoring AT amushiechima machinelearningbasedaggressiondetectioninchildrenwithadhdusingsensorbasedphysicalactivitymonitoring AT nguyenjacqueline machinelearningbasedaggressiondetectioninchildrenwithadhdusingsensorbasedphysicalactivitymonitoring AT calargechadi machinelearningbasedaggressiondetectioninchildrenwithadhdusingsensorbasedphysicalactivitymonitoring AT najafibijan machinelearningbasedaggressiondetectioninchildrenwithadhdusingsensorbasedphysicalactivitymonitoring |