PatientSense: patient discrimination from in-bottle sensors data
PatientSense: patient discrimination from in-bottle sensors data
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PatientSense：根据瓶内传感器数据区分患者
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
R. Martin
中科院分区：
文献类型：
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作者：
Murtadha M. N. Aldeer;Jorge Ortiz;R. Howard;R. Martin
准确计算药物使用情况对于患者及其家人的疗效和安全非常重要。监测对于药物依从性也很重要。这项工作研究了使用配备传感器的药瓶来识别服药者的身份。该瓶子在瓶盖和瓶身上都配备了惯性和开关传感器，使得添加的硬件不显眼、成本低且无线。我们的系统使用惯性数据通过分类技术构建患者区分模型。我们使用 16 名受试者评估了该系统。我们的结果表明，使用二元支持向量机 (SVM)，系统可以以 94% 的准确率从 16 名受试者中区分出一名患者，而使用单个传感器的准确率则为 93%。在一组 3 个对象中准确识别出一个人的准确率高于 91%。
Accurately accounting for medication use is important for the efficacy and safety of patients and family members. Monitoring is also important for medication adherence. This work investigates identification of persons taking medication using a sensor-equipped pill bottle. The bottle is equipped with inertial and switch sensors in both the cap and body, making the added hardware unobtrusive, low-cost, and wireless. Our system uses inertial data to build a patient discrimination model using classification techniques. We evaluated the system using 16 subjects. Our results show that using binary Support Vector Machine (SVM), the system can discriminate one patient among 16 subjects with 94% accuracy, and has a 93% using a single sensor. Identifying the exact person in a set of 3 subjects has an accuracy higher than 91%.