Edison Desarrollos, 44002 Teruel, Spain.
Department of Computer Science and Engineering of Systems, University of Zaragoza, 44003 Teruel, Spain.
Sensors (Basel). 2018 Aug 13;18(8):2652. doi: 10.3390/s18082652.
The trend of using wearables for healthcare is steeply increasing nowadays, and, consequently, in the market, there are several gadgets that measure several body features. In addition, the mixed use between smartphones and wearables has motivated research like the current one. The main goal of this work is to reduce the amount of times that a certain smartband (SB) measures the heart rate (HR) in order to save energy in communications without significantly reducing the utility of the application. This work has used an SB Sony 2 for measuring heart rate, Fit API for storing data and Android for managing data. The current approach has been assessed with data from HR sensors collected for more than three months. Once all HR measures were collected, then the current approach detected hourly ranges whose heart rate were higher than normal. The hourly ranges allowed for estimating the time periods of weeks that the user could be at potential risk for measuring frequently in these (60 times per hour) ranges. Out of these ranges, the measurement frequency was lower (six times per hour). If SB measures an unusual heart rate, the app warns the user so they are aware of the risk and can act accordingly. We analyzed two cases and we conclude that energy consumption was reduced in 83.57% in communications when using training of several weeks. In addition, a prediction per day was made using data of 20 users. On average, tests obtained 63.04% of accuracy in this experimentation using the training over the data of one day for each user.
如今,可穿戴设备在医疗保健领域的应用趋势急剧上升,因此市场上出现了许多可测量多种身体特征的小工具。此外,智能手机和可穿戴设备的混合使用激发了当前这项研究的开展。这项工作的主要目标是减少特定智能手环(SB)测量心率(HR)的次数,以节省通信中的能量,而不会显著降低应用程序的实用性。这项工作使用了索尼 2 号 SB 来测量心率,Fit API 来存储数据,以及 Android 来管理数据。目前的方法已经使用了三个月以上从 HR 传感器收集的数据进行了评估。一旦收集了所有的 HR 测量值,当前的方法就会检测到每小时的范围,这些范围的心率高于正常水平。这些小时范围可以估计用户在这些(每小时 60 次)范围内可能需要频繁测量的几周时间。在这些范围内,测量频率较低(每小时六次)。如果 SB 测量到异常的心率,应用程序会警告用户,让他们意识到风险并采取相应的措施。我们分析了两个案例,得出结论,在使用数周的训练时,通信中的能耗减少了 83.57%。此外,还使用 20 名用户的数据对每天进行了预测。在这项实验中,使用每个用户一天的数据进行训练,平均获得了 63.04%的准确率。