A Sensing-Based Framework for Medication Compliance Monitoring

Abstract

Medication compliance is challenging to monitor and identify because of their complex nature that is significantly influenced by the patient's behavior. Medication compliance monitoring systems are fundamental contributions to the connected health paradigm. In this paper, we propose a compliance sensing framework that does not require on-body sensing modalities or costly installation and maintenance. It uses a smart pill-bottle that utilizes Inertial Measurement Units (IMUs) and machine learning approaches for activity classification. The IMUs collect data about user behavior while taking medication. The collected signals are subject to pre-processing, from which features can be extracted for activity detection algorithms. Our initial results show promise in correctly discriminating medication-taking 90 % of the time compare to only moving the bottle.