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Related Experiment Video

Updated: May 13, 2026

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

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

Published on: July 27, 2018

HabitSense: A Privacy-Aware, AI-Enhanced Multimodal Wearable Platform for mHealth Applications.

Glenn J Fernandes¹, Jiayi Zheng¹, Mahdi Pedram²

¹Northwestern University, USA.

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

|March 5, 2025

Summary

This summary is machine-generated.

HabitSense, a wearable camera system, objectively detects health-risk behaviors like smoking and eating using multi-modal sensors. It enhances user privacy and reduces burden through smart activation and on-device obfuscation.

Keywords:

camera eating machine learning multimodal privacy smoking thermal vision transformers wearable

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Last Updated: May 13, 2026

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An Application for Pairing with Wearable Devices to Monitor Personal Health Status

An Application for Pairing with Wearable Devices to Monitor Personal Health Status

Published on: February 3, 2022

Area of Science:

Biomedical Engineering
Human-Computer Interaction
Digital Health

Background:

Wearable cameras offer objective health-risk behavior detection for adaptive interventions.
Barriers to adoption include lack of clinician input, privacy concerns, and user burden.

Purpose of the Study:

To introduce HabitSense, an open-source, multi-modal neck-worn platform for monitoring health-risk behaviors.
To address clinician and user feedback to improve wearable system design and feasibility.

Main Methods:

Developed HabitSense with clinician focus groups (N=36) and user feedback (105 participants, 35 days).
Utilized RGB, thermal, and IMU sensors for real-time eating/smoking event detection.
Implemented SECURE smart activation and on-device obfuscation for privacy and efficiency.

Main Results:

Achieved 92% F1-score in hand-to-mouth gesture recognition for eating/smoking.
SECURE algorithm reduced storage by 48% and increased battery life by 30%.
Demonstrated feasibility and acceptability in a 7-day study with 15 participants.

Conclusions:

Clinician input, field testing, and privacy algorithms are crucial for acceptable wearable health monitoring.
HabitSense is an unobtrusive, lightweight, and reproducible system for real-world health-risk behavior monitoring.