A New Mining Method to Detect Real Time Substance Use Events from Wearable Biosensor Data Stream

Detecting real time substance use is a critical step for optimizing behavioral interventions to prevent drug abuse. Traditional methods based on self-reporting or urine screening are inefficient or intrusive for drug use detection, and inappropriate for timely interventions. For example, self-report suffers from distortion or recall bias; while urine screening often detects drug use that occurred only within the previous 72 hours. Methods for real-time substance use detection are severely underdeveloped, partly due to the novelty of wearable biosensor technique and the lack of substantive clinical data for evaluation. We propose a new real-time drug use event detection method using data obtained from wearable biosensors. Specifically, this method is built upon the slide window technique to process the data stream, and a distance-based outlier detection method to identify substance use events. This novel method is designed to examine how to detect and set up the thresholds of parameters in real-time drug use event detection for wearable biosensor data streams. Our numerical analyses empirically identified the thresholds of parameters used to detect the cocaine use and showed that this proposed method could be adapted to detect other substance use events