Heart Rate as a Predictor of Challenging Behaviours among Children with Autism from Wearable Sensors in Social Robot Interactions
Heart Rate as a Predictor of Challenging Behaviours among Children with Autism from Wearable Sensors in Social Robot Interactions
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DOI：
10.3390/robotics12020055
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发表时间：
2023-04-01
期刊：
影响因子：
3.7
通讯作者：
Cabibihan, John-John
中科院分区：
文献类型：
--
作者：
Alban, Ahmad Qadeib;Alhaddad, Ahmad Yaser;Cabibihan, John-John
自闭症儿童在各种技能(如沟通和社交)方面面临挑战，他们表现出具有挑战性的行为。这些具有挑战性的行为对他们的家人、治疗师和照顾者来说是一种挑战，特别是在治疗期间。在这项研究中，我们研究了几种机器学习技术和数据模式，这些技术和数据模式是在自闭症儿童与社交机器人和玩具互动时使用可穿戴传感器获得的，以检测他们的挑战行为。每个孩子都佩戴了一个收集数据的可穿戴设备。会议的视频注释被用来确定挑战行为的发生。提取的时间特征(即平均值、标准差、最小值和最大值)与四种机器学习技术相结合被认为可以检测挑战行为。本研究还对心率变异性(HRV)的变化进行了研究。XGBoost算法获得了最好的性能(即，99%的准确率)。此外，生理特征的表现优于动力学特征，心率是预测性能的主要贡献特征。一个HRV参数(即RMSSD)被发现与挑战行为的发生相关。这项工作强调了开发工具和方法来检测自闭症儿童在与社交机器人进行辅助会话期间具有挑战性的行为的重要性。
Children with autism face challenges in various skills (e.g., communication and social) and they exhibit challenging behaviours. These challenging behaviours represent a challenge to their families, therapists, and caregivers, especially during therapy sessions. In this study, we have investigated several machine learning techniques and data modalities acquired using wearable sensors from children with autism during their interactions with social robots and toys in their potential to detect challenging behaviours. Each child wore a wearable device that collected data. Video annotations of the sessions were used to identify the occurrence of challenging behaviours. Extracted time features (i.e., mean, standard deviation, min, and max) in conjunction with four machine learning techniques were considered to detect challenging behaviors. The heart rate variability (HRV) changes have also been investigated in this study. The XGBoost algorithm has achieved the best performance (i.e., an accuracy of 99%). Additionally, physiological features outperformed the kinetic ones, with the heart rate being the main contributing feature in the prediction performance. One HRV parameter (i.e., RMSSD) was found to correlate with the occurrence of challenging behaviours. This work highlights the importance of developing the tools and methods to detect challenging behaviors among children with autism during aided sessions with social robots.