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A Neck-Based Wearable for Real-Time Dietary Activity Detection

Many health-related studies rely on human memory to document when and what a person has eaten. Gaps in human memory and purposeful omissions make it difficult for researchers and doctors to reach accurate conclusions about their patients’ lifestyles. Our project focuses on the development of a neck-based wearable that keeps track of a user’s eating periods throughout the day. The purpose of creating a wearable that automatically collects and analyzes eating data is to yield data of higher accuracy and integrity than that obtained by self-reporting. Our device operates on a Raspberry Pi microcontroller and interfaces with double proximity sensors and an accelerometer, which track jaw and body movement. Our project covers the "when" aspect of the larger issue of tracking a user's dietary activity.

Team Members: 

Calvin Ly

Evonne Ng

Navendu Saini

Troy Stidd

Cynthia Wu

Bryan Yao

Colin Zhu