Radar-based monitoring system for medication tampering using data augmentation and multivariate time series classification

Abstract

Inadvertent use of medication that has been tampered with can cause serious harm. Monitoring how and when medication was last used or touched is important for mitigating risks. In this paper, we present a new radar-based monitoring system that can detect eight different types of tampering methods with three types of medication containers. Our system works by using a FMCW and CW Doppler radar to capture motion speed, direction, and range, which we use for classifying activities. For monitoring activities at home, our system can be set up underneath a kitchen cabinet to monitor medication left out on the countertop. As our system uses radar, we can preserve privacy of individuals as the signatures from the radar are specific to the locations of the antennas and not necessarily associated with an individual. For classifying activities we created a processing pipeline that extracts a set of features from the raw multivariate time series signals from the radar. We then used three types of data augmentation techniques including jittering, scaling, and magnitude warping, to increase our data sets and increase our classification model accuracy. In addition, we evaluated our system using 5-fold cross validation and with different types of augmentation data sets. Our system can achieve 99% accuracy using a logistic regression classifier with multiple people.