Min Cheol-Hong, Tewfik Ahmed H
Department of Electrical and Computer Engineering at the University of Minnesota - Twin Cities, Minneapolis, MN 55455 USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:220-3. doi: 10.1109/IEMBS.2010.5627850.
In this study, we target to automatically detect behavioral patterns of patients with autism. Many stereotypical behavioral patterns may hinder their learning ability as a child and patterns such as self-injurious behaviors (SIB) can lead to critical damages or wounds as they tend to repeatedly harm one single location. Our custom designed accelerometer based wearable sensor can be placed at various locations of the body to detect stereotypical self-stimulatory behaviors (stereotypy) and self-injurious behaviors of patients with Autism Spectrum Disorder (ASD). A microphone was used to record sounds so that we may understand the surrounding environment and video provided ground truth for analysis. The analysis was done on four children diagnosed with ASD who showed repeated self-stimulatory behaviors that involve part of the body such as flapping arms, body rocking and self-injurious behaviors such as punching their face, or hitting their legs. The goal of this study is to devise novel algorithms to detect these events and open possibility for design of intervention methods. In this paper, we have shown time domain pattern matching with linear predictive coding (LPC) of data to design detection and classification of these ASD behavioral events. We observe clusters of pole locations from LPC roots to select candidates and apply pattern matching for classification. We also show novel event detection using online dictionary update method. We show that our proposed method achieves recall rate of 95.5% for SIB, 93.5% for flapping, and 95.5% for rocking which is an increase of approximately 5% compared to flapping events detected by using wrist worn sensors in our previous study.
在本研究中，我们旨在自动检测自闭症患者的行为模式。许多刻板行为模式可能会妨碍他们儿童时期的学习能力，而诸如自伤行为（SIB）等模式可能会导致严重伤害或创伤，因为他们往往会反复伤害身体的同一部位。我们定制设计的基于加速度计的可穿戴传感器可以放置在身体的不同位置，以检测自闭症谱系障碍（ASD）患者的刻板自我刺激行为（刻板动作）和自伤行为。使用麦克风记录声音，以便我们了解周围环境，视频则为分析提供了地面实况。对四名被诊断为ASD的儿童进行了分析，他们表现出反复的自我刺激行为，包括拍打手臂、身体摇晃等涉及身体部位的行为，以及诸如打脸或打腿等自伤行为。本研究的目标是设计新颖的算法来检测这些事件，并为干预方法的设计开辟可能性。在本文中，我们展示了通过对数据进行线性预测编码（LPC）的时域模式匹配来设计对这些ASD行为事件的检测和分类。我们从LPC根中观察极点位置的聚类以选择候选者，并应用模式匹配进行分类。我们还展示了使用在线字典更新方法的新颖事件检测。我们表明，我们提出的方法对SIB的召回率达到95.5%，对拍打行为的召回率为93.5%，对摇晃行为的召回率为95.5%，与我们之前研究中使用腕部佩戴传感器检测到的拍打事件相比，召回率提高了约5%。
