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

ABSTRACT: Wearable cameras provide an objective method to visually confirm and automate the detection of health-risk behaviors such as smoking and overeating, which is critical for developing and testing adaptive treatment interventions. Despite the potential of wearable camera systems, adoption is hindered by inadequate clinician input in the design, user privacy concerns, and user burden. To address these barriers, we introduced HabitSense, an open-source, multi-modal neck-worn platform developed with input from focus groups with clinicians (N=36) and user feedback from in-wild studies involving 105 participants over 35 days. Optimized for monitoring health-risk behaviors, the platform utilizes RGB, thermal, and inertial measurement unit sensors to detect eating and smoking events in real time. In

SUBMITTER: Fernandes GJ 

PROVIDER: S-EPMC11879279 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature