Kalantarian Haik, Alshurafa Nabil, Sarrafzadeh Majid
Department of Computer Science, University of California at Los Angeles, Los Angeles, CA 90095 USA.
Department of Preventative Medicine and Computer Science, Northwestern University, Evanston, IL 60208 USA.
IEEE Sens J. 2016 Feb;16(4):1054-1061. doi: 10.1109/jsen.2015.2497279. Epub 2015 Nov 2.
Poor adherence to prescription medication can compromise treatment effectiveness and cost the billions of dollars in unnecessary health care expenses. Though various interventions have been proposed for estimating adherence rates, few have been shown to be effective. Digital systems are capable of estimating adherence without extensive user involvement and can potentially provide higher accuracy with lower user burden than manual methods. In this paper, we propose a smartwatch-based system for detecting several motions that may be predictors of medication adherence, using built-in triaxial accelerometers and gyroscopes. The efficacy of the proposed technique is confirmed through a survey of medication ingestion habits and experimental results on movement classification.
对处方药的依从性差会影响治疗效果，并造成数十亿美元的不必要医疗费用。尽管已经提出了各种干预措施来估计依从率，但很少有措施被证明是有效的。数字系统能够在用户参与度不高的情况下估计依从性，并且与手动方法相比，有可能以更低的用户负担提供更高的准确性。在本文中，我们提出了一种基于智能手表的系统，该系统使用内置的三轴加速度计和陀螺仪来检测几种可能是药物依从性预测指标的运动。通过对药物摄入习惯的调查和运动分类的实验结果，证实了所提出技术的有效性。
