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Wrist sensors — An application to acquire sensory data from Android Wear® smartwatches for connected health

机译:腕式传感器—一种用于从AndroidWear®智能手表获取感知数据以实现健康连接的应用程序

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In our days where P4 (predictive, preventive, personalized, and participatory) medicine is recognized as a promising sustainable solution to the problems of healthcare systems, there is an evident demand for individual-level biological, physical, behavioral or environmental data to support the development of efficient P4 medicine interventions. The self-acquisition of such data with the help of wearable sensor technology is the primary objective of the trending Quantified Self (QS) movement. Smart devices (smartphones, smartwatches, wrist sensors, etc.), with dozens of built-sensors and great adoption by the public, emerge as the perfect technology tools for satisfying the data acquisition pursuits of QS. In this effort, smartwatches demonstrate certain sensory advantages when compared to portable smart devices (e.g., smartphones). In this work we introduce Wrist Sensors, a novel Android Wear app for presenting the available sensors of compatible smartwatches and recording measurements from any user-specified subset of them. The recordings are persistently stored and made available in the communicating Android smartphone. The app, which has been made freely available via the Google Play distribution service, can serve as a valuable data acquisition tool to facilitate the development of efficient P4 medicine interventions. In fact, the app has already been used by a published study for acquiring the input dataset for developing a novel real-time bite detection algorithm.
机译:在当今,P4(预测性,预防性,个性化和参与性)医学被认为是解决医疗保健系统问题的有前途的可持续解决方案的时代,对个人水平的生物学,身体,行为或环境数据的支持需求明显增加。开发有效的P4药物干预措施。借助可穿戴传感器技术自动采集此类数据是趋势量化自我(QS)运动的主要目标。智能设备(智能手机,智能手表,手腕传感器等)具有数十种内置传感器并受到了公众的广泛欢迎,它们已成为满足QS数据采集追求的理想技术工具。通过这种努力,与便携式智能设备(例如,智能手机)相比,智能手表展现出某些感官优势。在这项工作中,我们介绍了Wrist Sensors,这是一款新颖的Android Wear应用,用于显示兼容智能手表的可用传感器,并记录用户指定的任何子集的测量值。录音将永久存储并在可通信的Android智能手机中提供。该应用程序已通过Google Play发行服务免费提供,可以用作有价值的数据获取工具,以促进有效的P4药物干预措施的开发。实际上,该应用程序已被一项已发表的研究用于获取输入数据集,以开发一种新颖的实时叮咬检测算法。

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