Demonstration of a sensor-based app for self-monitoring of medicine intake

Curci, Selima;Mura, Alessandro;Riboni, Daniele

2017-01-01

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Abstract

Accurate adherence to prescribed medications is essential for the effectiveness of therapies, but several studies show that when patients are responsible for treatment administration, poor adherence is prevalent. Existing apps to support self-administration of drugs may interfere with the normal routine of patients by providing unnecessary reminders. More sophisticated solutions, including the use of smart packaging and ingestible sensors, are currently restricted to patients involved in a few clinical studies. In this paper, we demonstrate a novel app to support self-administration of drugs without interfering with the patient's routines. The system relies on cheap wireless sensors attached to medicine boxes to detect medicine intake. The app uses machine learning to detect intake events, and active learning to improve recognition based on the user's feedback. In the demonstration, we show a working prototype of the system, which includes a Web dashboard for physicians to monitor the rate of intakes. Copyright is held by the author/owner(s).