Poster Abstract: User identification across multiple smart pill bottle systems

Medication adherence is one of the leading factors that can make the difference between life and death, especially for patients managing chronic conditions [2]. Indeed, these issues have driven a recent wave of research, including the development of smart pill bottles that monitor when a pill is extracted. In this poster, we extend our recent work [1], where we present adaptive learning techniques for subject identification across multiple pill bottle systems. We collect inertial signals from 10 subjects taking medication pills and encode the activity signals by transforming them into 2D texture images. Then we use pre-trained Convolutional Neural Network (CNN) models for image-based classification tasks. Our approach achieved improved differentiation capacity over existing models by using deep learning models, modified through domain adaptation and transfer learning.