Min Cheol-Hong, Tewfik Ahmed H, Kim Youngchun, Menard Rigel
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. 2009;2009:3489-92. doi: 10.1109/IEMBS.2009.5334572.
In this study, we investigate various locations of sensor positions to detect stereotypical self-stimulatory behavioral patterns of children with Autism Spectrum Disorder (ASD). The study is focused on finding optimal detection performance based on sensor location and number of sensors. To perform this study, we developed a wearable sensor system that uses a 3 axis accelerometer. A microphone was used to understand the surrounding environment and video provided ground truth for analysis. The recordings were done on 2 children diagnosed with ASD who showed repeated self-stimulatory behaviors that involve part of the body such as flapping arms, body rocking and vocalization of non-word sounds. We used time-frequency methods to extract features and sparse signal representation methods to design over-complete dictionary for data analysis, detection and classification of these ASD behavioral events. We show that using single sensor on the back achieves 95.5% classification rate for rocking and 80.5% for flapping. In contrast, flapping events can be recognized with 86.5% accuracy using wrist worn sensors.
在本研究中，我们调查了传感器位置的不同情况，以检测自闭症谱系障碍（ASD）儿童的刻板自我刺激行为模式。该研究专注于基于传感器位置和传感器数量来找到最佳检测性能。为了进行这项研究，我们开发了一种使用三轴加速度计的可穿戴传感器系统。使用麦克风来了解周围环境，视频则为分析提供了地面真值。记录是在两名被诊断为ASD的儿童身上进行的，他们表现出重复的自我刺激行为，包括身体的一部分，如拍打手臂、身体摇晃和发出无意义的声音。我们使用时频方法来提取特征，并使用稀疏信号表示方法来设计过完备字典，用于对这些ASD行为事件进行数据分析、检测和分类。我们表明，在背部使用单个传感器时，摇晃行为的分类率达到95.5%，拍打行为的分类率达到80.5%。相比之下，使用腕部佩戴的传感器时，拍打事件的识别准确率为86.5%。
