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ActivityAware: An app for real-time daily activity level monitoring on the Amulet wrist-worn device

机译:ActivityAware:用于在Amulet腕戴式设备上进行实时每日活动水平监控的应用程序

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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 ActivityAware, 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 ActivityAware 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.
机译:进行体育锻炼有助于降低罹患心血管疾病,高血压和肥胖症的风险。监视一个人的日常活动水平的能力可以告知自我管理身体活动和相关干预措施的能力。对于肥胖的老年人,定期进行体育锻炼对降低长期残疾风险至关重要。在这项工作中,我们介绍了ActivityAware,这是Amulet腕戴式设备上的应用程序,它可以连续且实时地测量个人的日常活动水平(中度,中度和剧烈)。该应用程序实现一个活动级别检测模型,在护身符上连续收集加速度数据,对当前活动级别进行分类,更新当天在该活动级别上花费的累计时间,记录数据以供以后分析,然后在屏幕上显示结果。我们使用支持向量机(SVM)开发了活动级别的检测模型。我们使用来自用户研究的数据对分类器进行了训练,其中受试者进行了以下身体活动:坐着,站着,躺下,行走和奔跑。通过10倍交叉验证和留一法则(LOSO)交叉验证,我们获得了初步结果,对于n = 14的受试者,其准确性高达98%。测试ActivityAware应用程序后,预计需要长达4周的电池寿命才能充电。结果令人鼓舞,表明该应用程序可用于活动级别的监视,并最终用于开发可以改善个人健康的干预措施。

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