Department of Mathematics and Statistics, Wake Forest University, Winston Salem, NC 27106, USA.
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA 02115, USA.
Sensors (Basel). 2020 Jul 2;20(13):3706. doi: 10.3390/s20133706.
Physical activity, such as walking and ascending stairs, is commonly used in biomedical settings as an outcome or covariate. Researchers have traditionally relied on surveys to quantify activity levels of subjects in both research and clinical settings, but surveys are subjective in nature and have known limitations, such as recall bias. Smartphones provide an opportunity for unobtrusive objective measurement of physical activity in naturalistic settings, but their data tends to be noisy and needs to be analyzed with care. We explored the potential of smartphone accelerometer and gyroscope data to distinguish between walking, sitting, standing, ascending stairs, and descending stairs. We conducted a study in which four participants followed a study protocol and performed a sequence of activities with one phone in their front pocket and another phone in their back pocket. The subjects were filmed throughout, and the obtained footage was annotated to establish moment-by-moment ground truth activity. We introduce a modified version of the so-called movelet method to classify activity type and to quantify the uncertainty present in that classification. Our results demonstrate the promise of smartphones for activity recognition in naturalistic settings, but they also highlight challenges in this field of research.
体力活动，如散步和爬楼梯，在生物医学领域通常被用作结果或协变量。研究人员传统上依赖调查来量化研究和临床环境中受试者的活动水平，但调查具有主观性，并且存在已知的局限性，例如回忆偏差。智能手机为在自然环境中进行非侵入性客观的体力活动测量提供了机会，但它们的数据往往不稳定，需要谨慎分析。我们探讨了智能手机加速度计和陀螺仪数据在区分散步、坐着、站立、上楼梯和下楼梯方面的潜力。我们进行了一项研究，其中四名参与者按照研究方案进行了一系列活动，一个手机放在前口袋，另一个放在后口袋。整个过程都进行了拍摄，并对获得的镜头进行注释，以建立逐时刻的实际活动。我们引入了一种改进的所谓移动小波方法来对活动类型进行分类，并量化分类中存在的不确定性。我们的研究结果表明智能手机在自然环境中的活动识别方面具有潜力，但也突出了该研究领域的挑战。
