Department of Mathematics and Statistics, Wake Forest University, Winston-Salem, NC 27106, USA.
Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA.
Sensors (Basel). 2022 Mar 29;22(7):2618. doi: 10.3390/s22072618.
Physical activity patterns can reveal information about one's health status. Built-in sensors in a smartphone, in comparison to a patient's self-report, can collect activity recognition data more objectively, unobtrusively, and continuously. A variety of data analysis approaches have been proposed in the literature. In this study, we applied the movelet method to classify the activities performed using smartphone accelerometer and gyroscope data, which measure a phone's acceleration and angular velocity, respectively. The movelet method constructs a personalized dictionary for each participant using training data and classifies activities in new data with the dictionary. Our results show that this method has the advantages of being interpretable and transparent. A unique aspect of our movelet application involves extracting unique information, optimally, from multiple sensors. In comparison to single-sensor applications, our approach jointly incorporates the accelerometer and gyroscope sensors with the movelet method. Our findings show that combining data from the two sensors can result in more accurate activity recognition than using each sensor alone. In particular, the joint-sensor method reduces errors of the gyroscope-only method in differentiating between standing and sitting. It also reduces errors in the accelerometer-only method when classifying vigorous activities.
身体活动模式可以揭示一个人的健康状况信息。与患者的自我报告相比，智能手机内置的传感器可以更客观、更不引人注意且持续地收集活动识别数据。文献中已经提出了多种数据分析方法。在这项研究中，我们应用移动小波方法对使用智能手机加速度计和陀螺仪数据进行的活动进行分类，加速度计和陀螺仪分别测量手机的加速度和角速度。移动小波方法使用训练数据为每个参与者构建个性化字典，并使用字典对新数据中的活动进行分类。我们的结果表明，该方法具有可解释性和透明度的优点。我们的移动小波应用的一个独特方面涉及从多个传感器中最佳地提取独特信息。与单传感器应用相比，我们的方法将加速度计和陀螺仪传感器与移动小波方法联合使用。我们的研究结果表明，将两个传感器的数据结合起来可以比单独使用每个传感器更准确地识别活动。特别是，联合传感器方法减少了仅使用陀螺仪方法区分站立和坐姿时的误差。它还减少了仅使用加速度计方法分类剧烈活动时的误差。
