Toward the Autism Motor Signature: Gesture patterns during smart tablet gameplay identify children with autism.
Toward the Autism Motor Signature: Gesture patterns during smart tablet gameplay identify children with autism.
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DOI：
10.1038/srep31107
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发表时间：
2016-08-24
影响因子：
4.6
通讯作者：
Delafield-Butt JT
中科院分区：
文献类型：
--
作者：
Anzulewicz A;Sobota K;Delafield-Butt JT
自闭症是一种从婴儿期开始就很明显的发育障碍。然而，其临床鉴定需要专家诊断培训。新的证据表明，运动时间和整合的中断可能是这种疾病的基础，为其早期识别提供了一个潜在的新的计算标记。在这项研究中，我们使用带有触敏屏幕和嵌入式惯性运动传感器的智能平板电脑记录了37名3-6岁自闭症儿童和45名年龄和性别匹配的正常发育儿童的运动运动学和手势力。通过对儿童运动模式的机器学习分析，识别自闭症的准确率高达93%。分析显示，这些模式包括接触时更大的力和一个手势内不同的力分布，手势运动学更快、更大，更多地使用远端空间。这些数据支持了运动中断是自闭症的核心特征的概念，并证明自闭症可以通过有趣的智能设备游戏来计算评估。
Autism is a developmental disorder evident from infancy. Yet, its clinical identification requires expert diagnostic training. New evidence indicates disruption to motor timing and integration may underpin the disorder, providing a potential new computational marker for its early identification. In this study, we employed smart tablet computers with touch-sensitive screens and embedded inertial movement sensors to record the movement kinematics and gesture forces made by 37 children 3–6 years old with autism and 45 age- and gender-matched children developing typically. Machine learning analysis of the children’s motor patterns identified autism with up to 93% accuracy. Analysis revealed these patterns consisted of greater forces at contact and with a different distribution of forces within a gesture, and gesture kinematics were faster and larger, with more distal use of space. These data support the notion disruption to movement is core feature of autism, and demonstrate autism can be computationally assessed by fun, smart device gameplay.