Sitova Zdenka, Abramson Tobi, Gasti Paolo, Balagani Kiran S, Farajidavar Aydin
Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:4168-71. doi: 10.1109/EMBC.2014.6944542.
We have proposed a novel solution to a fundamental problem encountered in implementing non-ingestion based medical adherence monitoring systems, namely, how to reliably identify pill medication intake. We show how wireless wearable devices with tri-axial accelerometer can be used to detect and classify hand gestures of users during solid-phase medication intake. Two devices were worn on the wrists of each user. Users were asked to perform two activities in the way that is natural and most comfortable to them: (1) taking empty gelatin capsules with water, and (2) drinking water and wiping mouth. 25 users participated in this study. The signals obtained from the devices were filtered and the patterns were identified using dynamic time warping algorithm. Using hand gesture signals, we achieved 84.17 percent true positive rate and 13.33 percent false alarm rate, thus demonstrating that the hand gestures could be used to effectively identify pill taking activity.
我们针对基于非摄入式的医疗依从性监测系统实施过程中遇到的一个基本问题，提出了一种新颖的解决方案，即如何可靠地识别药丸药物摄入情况。我们展示了配备三轴加速度计的无线可穿戴设备如何用于在固体药物摄入期间检测和分类用户的手势。每个用户手腕上佩戴两个设备。要求用户以自然且最舒适的方式进行两项活动：(1) 用水服用空明胶胶囊，以及 (2) 喝水并擦拭嘴巴。25名用户参与了这项研究。对从设备获得的信号进行滤波，并使用动态时间规整算法识别模式。利用手势信号，我们实现了84.17%的真阳性率和13.33%的误报率，从而证明手势可用于有效识别服药活动。
