Media Laboratory, Massachusetts Institute of Technology, MIT Bldg E14-374 K, 75 Amherst Street, Cambridge, MA 02139, USA.
J Autism Dev Disord. 2011 Jun;41(6):770-82. doi: 10.1007/s10803-010-1102-z.
To overcome problems with traditional methods for measuring stereotypical motor movements in persons with Autism Spectrum Disorders (ASD), we evaluated the use of wireless three-axis accelerometers and pattern recognition algorithms to automatically detect body rocking and hand flapping in children with ASD. Findings revealed that, on average, pattern recognition algorithms correctly identified approximately 90% of stereotypical motor movements repeatedly observed in both laboratory and classroom settings. Precise and efficient recording of stereotypical motor movements could enable researchers and clinicians to systematically study what functional relations exist between these behaviors and specific antecedents and consequences. These measures could also facilitate efficacy studies of behavioral and pharmacologic interventions intended to replace or decrease the incidence or severity of stereotypical motor movements.
为了克服传统方法在测量自闭症谱系障碍（ASD）患者刻板运动方面存在的问题，我们评估了使用无线三轴加速度计和模式识别算法自动检测 ASD 儿童的身体晃动和手拍动作。研究结果表明，平均而言，模式识别算法正确识别了实验室和教室环境中反复观察到的大约 90%的刻板运动。精确高效地记录刻板运动可以使研究人员和临床医生能够系统地研究这些行为与特定的前因和后果之间存在哪些功能关系。这些措施还可以促进旨在替代或减少刻板运动的发生率或严重程度的行为和药物干预的疗效研究。
