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RisQ: Recognizing Smoking Gestures with Inertial Sensors on a Wristband.

Abhinav Parate, Meng-Chieh Chiu, Chaniel Chadowitz

Mobisys ... : the ... International Conference on Mobile Systems, Applications and Services. International Conference on Mobile Systems, Applications, and Services

|December 22, 2015

Summary

This study introduces RisQ, a mobile system using a wristband and machine learning to detect smoking gestures and sessions in real-time. It accurately identifies smoking behavior, aiding in the study of smoking-induced diseases.

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Area of Science:

Biomedical Engineering
Computer Science
Public Health

Background:

Smoking-induced diseases are a leading cause of death in the US.
Accurate monitoring of smoking behavior is crucial for intervention and research.
Current methods for tracking smoking sessions often rely on self-reporting, which can be inaccurate.

Purpose of the Study:

To develop and evaluate RisQ, a novel mobile solution for real-time detection of smoking gestures and sessions.
To differentiate smoking gestures from confounding activities like eating and drinking.
To reduce the reliance on self-reports for data collection in smoking studies.

Main Methods:

Utilized a wristband with a 9-axis inertial measurement unit (IMU) to capture arm orientation.
Developed a machine learning pipeline employing an arm trajectory-based method for gesture recognition.
Implemented a probabilistic model to identify individual smoking sessions from gesture sequences.
Leveraged multiple IMUs and 3D animation to minimize self-report data collection burden.

Main Results:

The gesture recognition algorithm achieved high accuracy (95.7%), precision (91%), and recall (81%) for detecting smoking gestures.
A user study demonstrated accurate detection of the number of smoking sessions with minimal false positives.
The system reliably identified the start and end times of smoking sessions.

Conclusions:

RisQ offers a promising, accurate, and real-time solution for monitoring smoking behavior using wearable technology.
This technology can significantly aid in the objective assessment of smoking patterns for public health research and interventions.
The developed methods for gesture recognition and session inference show potential for application in other behavioral monitoring contexts.