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Updated: Feb 21, 2026

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

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Published on: July 27, 2018

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

Jin Wang^1,2, Hua Fang¹, Stephanie Carreiro³

¹Department of Quantitative Health Science, University of Massachusetts Medical School, Worcester, USA.

International Conference on Computing, Networking, and Communications : [Proceedings]. International Conference on Computing, Networking and Communications

|October 11, 2017

Summary

This study introduces a novel method for real-time substance use detection using wearable biosensors. It offers a more timely and less intrusive alternative to traditional drug testing for behavioral interventions.

Keywords:

Behavioral Intervention Data Mining Data stream Substance Use Wearable biosensor

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Published on: July 27, 2018

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High-throughput and Comprehensive Drug Surveillance Using Multisegment Injection-Capillary Electrophoresis-Mass Spectrometry

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Published on: April 23, 2019

Area of Science:

Biomedical Engineering
Data Science
Public Health

Background:

Real-time substance use detection is crucial for effective behavioral interventions against drug abuse.
Current methods like self-reporting and urine screening have significant limitations in timeliness and intrusiveness.
Wearable biosensor technology presents a promising but underexplored avenue for real-time drug use monitoring.

Purpose of the Study:

To develop and evaluate a novel method for real-time detection of substance use events using wearable biosensor data.
To address the limitations of traditional drug detection methods for timely intervention.
To establish thresholds for parameter detection in real-time substance use event identification.

Main Methods:

Utilized a sliding window technique to process continuous data streams from wearable biosensors.
Employed a distance-based outlier detection algorithm to identify potential substance use events.
Performed numerical analyses to determine optimal parameter thresholds for event detection.

Main Results:

Successfully developed a real-time substance use event detection method using wearable biosensor data.
Empirically identified specific parameter thresholds for detecting cocaine use.
Demonstrated the adaptability of the proposed method for detecting other substance use events.

Conclusions:

The proposed method offers a viable solution for real-time substance use detection via wearable biosensors.
This approach overcomes the limitations of traditional methods, enabling more timely interventions.
The method shows potential for broad application in monitoring various substance use patterns.