Machine Learning-Based Aggression Detection in Children with ADHD Using Sensor-Based Physical Activity Monitoring.
Machine Learning-Based Aggression Detection in Children with ADHD Using Sensor-Based Physical Activity Monitoring.
复制标题
使用基于传感器的体育活动监测的ADHD儿童基于机器学习的攻击检测。
DOI：
10.3390/s23104949
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发表时间：
2023-05-21
期刊：
影响因子：
--
通讯作者：
Najafi B
中科院分区：
文献类型：
--
作者：
Park C;Rouzi MD;Atique MMU;Finco MG;Mishra RK;Barba-Villalobos G;Crossman E;Amushie C;Nguyen J;Calarge C;Najafi B
儿童的攻击性非常普遍，可能会产生毁灭性的后果，但目前还没有客观的方法来跟踪其在日常生活中的频率。本研究旨在研究使用可穿戴传感器衍生的身体活动数据和机器学习来客观地识别儿童的身体攻击事件。参与者（n = 39）年龄在7至16岁，有和没有ADHD，佩戴腰戴活动监测器（ActiGraph，GT3X+）长达一周，在12个月内三次，同时收集人口统计学，人体测量学和临床数据。机器学习技术，特别是随机森林，被用来分析识别1分钟时间分辨率的物理攻击事件的模式。共收集了119次攻击事件，持续7.3 ± 13.1 min，共872个1 min时间段，包括132个身体攻击时间段。该模型在区分身体攻击事件时具有较高的精确度（80.2%）、准确度（82.0%）、召回率（85.0%）、F1评分（82.4%）和曲线下面积（89.3%）。传感器衍生的矢量幅度特征（更快的三轴加速度）是模型中的第二个贡献特征，并显着区分侵略和非侵略时期。如果在更大的样本中进行验证，该模型可以为远程检测和管理儿童攻击事件提供实用有效的解决方案。
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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影响因子：
2.9
作者：
Kam HJ;Lee K;Cho SM;Shin YM;Park RW
通讯作者：
Park RW
影响因子：
4.4
作者：
Kraut AA;Langner I;Lindemann C;Banaschewski T;Petermann U;Petermann F;Mikolajczyk RT;Garbe E
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Garbe E
影响因子：
4.7
作者：
Guerrera, Silvia;Menghini, Deny;Vicari, Stefano
通讯作者：
Vicari, Stefano
影响因子：
3.1
作者：
Jeannet, Pierre-Yves;Aminian, Kamiar;Paraschiv-Ionescu, Anisoara
通讯作者：
Paraschiv-Ionescu, Anisoara
影响因子：
5.1
作者：
Calarge, Chadi A.;Schlechte, Janet A.;Burns, Trudy L.;Zemel, Babette S.
通讯作者：
Zemel, Babette S.