Boateng George, Batsis John A, Halter Ryan, Kotz David
Dartmouth College.
Proc IEEE Int Conf Pervasive Comput Commun Workshops. 2017 Mar;2017. doi: 10.1109/PERCOMW.2017.7917601. Epub 2017 May 4.
Physical activity helps reduce the risk of cardiovascular disease, hypertension and obesity. The ability to monitor a person's daily activity level can inform self-management of physical activity and related interventions. For older adults with obesity, the importance of regular, physical activity is critical to reduce the risk of long-term disability. In this work, we present , an application on the Amulet wrist-worn device that measures daily activity levels (sedentary, moderate and vigorous) of individuals, continuously and in real-time. The app implements an activity-level detection model, continuously collects acceleration data on the Amulet, classifies the current activity level, updates the day's accumulated time spent at that activity level, logs the data for later analysis, and displays the results on the screen. We developed an activity-level detection model using a Support Vector Machine (SVM). We trained our classifiers using data from a user study, where subjects performed the following physical activities: sit, stand, lay down, walk and run. With 10-fold cross validation and leave-one-subject-out (LOSO) cross validation, we obtained preliminary results that suggest accuracies up to 98%, for n=14 subjects. Testing the app revealed a projected battery life of up to 4 weeks before needing to recharge. The results are promising, indicating that the app may be used for activity-level monitoring, and eventually for the development of interventions that could improve the health of individuals.
体育活动有助于降低心血管疾病、高血压和肥胖的风险。监测一个人的日常活动水平的能力可以为体育活动的自我管理和相关干预提供信息。对于肥胖的老年人来说,定期进行体育活动对于降低长期残疾风险至关重要。在这项工作中,我们展示了一款在护身符腕戴设备上的应用程序,它可以连续实时测量个人的日常活动水平(久坐、适度和剧烈)。该应用程序实现了一个活动水平检测模型,持续收集护身符上的加速度数据,对当前活动水平进行分类,更新当天在该活动水平上累计花费的时间,记录数据以供后续分析,并在屏幕上显示结果。我们使用支持向量机(SVM)开发了一个活动水平检测模型。我们使用来自一项用户研究的数据训练我们的分类器,在该研究中,受试者进行了以下体育活动:坐、站、躺、走和跑。通过10折交叉验证和留一法(LOSO)交叉验证,我们获得了初步结果,表明对于n = 14名受试者,准确率高达98%。对该应用程序的测试显示,在需要充电之前,预计电池续航时间长达4周。结果很有前景,表明该应用程序可用于活动水平监测,并最终用于开发能够改善个人健康的干预措施。