Trujillo-Priego Ivan A, Lane Christianne J, Vanderbilt Douglas L, Deng Weiyang, Loeb Gerald E, Shida Joanne, Smith Beth A
Division of Biokinesiology and Physical Therapy, University of Southern California, Los Angeles, CA 90089-9006, USA.
Department of Preventative Medicine, Division of Biostatistics, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089-9234, USA.
Technologies (Basel). 2017 Sep;5(3). doi: 10.3390/technologies5030039. Epub 2017 Jun 23.
We developed a wearable sensor algorithm to determine the number of arm movement bouts an infant produces across a full day in the natural environment. Full-day infant arm movement was recorded from 33 infants (22 infants with typical development and 11 infants at risk of atypical development) across multiple days and months by placing wearable sensors on each wrist. Twenty second sections of synchronized video data were used to compare the algorithm against visual observation as the gold standard for counting the number of arm movement bouts. Overall, the algorithm counted 173 bouts and the observer identified 180, resulting in a sensitivity of 90%. For each bout produced across the day, we then calculated the following kinematic characteristics: duration, average and peak acceleration, average and peak angular velocity, and type of movement (one arm only, both arms for some portion of the bout, or both arms for the entire bout). As the first step toward developing norms, we present average values of full-day arm movement kinematic characteristics across the first months of infancy for infants with typical development. Identifying and quantifying infant arm movement characteristics produced across a full day has potential application in early identification of developmental delays and the provision of early intervention therapies to support optimal infant development.
我们开发了一种可穿戴传感器算法，以确定婴儿在自然环境中一整天内产生的手臂运动次数。通过在每个手腕上放置可穿戴传感器，对33名婴儿（22名发育正常的婴儿和11名有发育异常风险的婴儿）在多天和数月内进行全天婴儿手臂运动记录。使用20秒的同步视频数据片段，将该算法与作为计数手臂运动次数金标准的视觉观察进行比较。总体而言，该算法计数了173次运动，观察者识别出180次，灵敏度为90%。对于一整天内产生的每次运动，我们随后计算了以下运动学特征：持续时间、平均加速度和峰值加速度、平均角速度和峰值角速度，以及运动类型（仅一只手臂、运动的某些部分双臂运动或整个运动双臂运动）。作为制定规范的第一步，我们呈现了发育正常婴儿在婴儿期前几个月全天手臂运动运动学特征的平均值。识别和量化婴儿一整天内产生的手臂运动特征在早期识别发育迟缓以及提供早期干预疗法以支持婴儿最佳发育方面具有潜在应用价值。
