Unobtrusive patient identification using smart pill-bottle systems

Abstract 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 passive 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 two datasets that we collected from 36 subjects. Our results show that using binary Support Vector Machine (SVM), the system can discriminate one individual among 3 people with > 90% accuracy and recall, and has > 80% accuracy and recall, using a single sensor. Using the same technique, our system has 85% accuracy and 72% recall for discriminating one subject in a population of 16. Identifying the exact person in a set of 3 subjects has an accuracy higher than 91%. We also show that we can infer the correct class for a previously unseen subject in a group of 3 subjects by using one-class SVM, with 75% average overall accuracy and 83% average recall.