Yao Xuewen, Plötz Thomas, Johnson McKensey, Barbaro Kaya DE
The University of Texas at Austin.
Georgia Institute of Technology.
Proc ACM Interact Mob Wearable Ubiquitous Technol. 2019 Jun;3(2). doi: 10.1145/3328935.
Physical contact is critical for children's physical and emotional growth and well-being. Previous studies of physical contact are limited to relatively short periods of direct observation and self-report methods. These methods limit researchers' understanding of the natural variation in physical contact across families, and its specific impacts on child development. In this study we develop a mobile sensing platform that can provide objective, unobtrusive, and continuous measurements of physical contact in naturalistic home interactions. Using commercially available motion detectors, our model reaches an accuracy of 0.870 (std: 0.059) for a second-by-second binary classification of holding. In addition, we detail five assessment scenarios applicable to the development of activity recognition models for social science research, where required accuracy may vary as a function of the intended use. Finally, we propose a grand vision for leveraging mobile sensors to access high-density markers of multiple determinants of early parent-child interactions, with implications for basic science and intervention.
身体接触对儿童的身体和情感成长以及幸福安康至关重要。以往关于身体接触的研究局限于相对较短时间的直接观察和自我报告方法。这些方法限制了研究人员对不同家庭中身体接触的自然变化及其对儿童发展的具体影响的理解。在本研究中，我们开发了一个移动传感平台，该平台可以在自然的家庭互动中提供对身体接触的客观、不显眼且持续的测量。使用市售的运动探测器，我们的模型在对抱持行为进行逐秒二元分类时达到了0.870（标准差：0.059）的准确率。此外，我们详细介绍了适用于社会科学研究活动识别模型开发的五种评估场景，其中所需的准确率可能会因预期用途而有所不同。最后，我们提出了一个宏伟愿景，即利用移动传感器获取早期亲子互动多个决定因素的高密度标记，这对基础科学和干预具有重要意义。
