NeckSense: A Multi-Sensor Necklace for Detecting Eating Activities in Free-Living Conditions

Shibo Zhang*, Yuqi Zhao, Dzung Tri Nguyen, Runsheng Xu, Sougata Sen, Josiah Hester, Nabil Alshurafa

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

We present the design, implementation, and evaluation of a multi-sensor, low-power necklace, NeckSense, for automatically and unobtrusively capturing fine-grained information about an individual's eating activity and eating episodes, across an entire waking day in a naturalistic setting. NeckSense fuses and classifies the proximity of the necklace from the chin, the ambient light, the Lean Forward Angle, and the energy signals to determine chewing sequences, a building block of the eating activity. It then clusters the identified chewing sequences to determine eating episodes. We tested NeckSense on 11 participants with and 9 participants without obesity, across two studies, where we collected more than 470 hours of data in a naturalistic setting. Our results demonstrate that NeckSense enables reliable eating detection for individuals with diverse body mass index (BMI) profiles, across an entire waking day, even in free-living environments. Overall, our system achieves an F1-score of 81.6% in detecting eating episodes in an exploratory study. Moreover, our system can achieve an F1-score of 77.1% for episodes even in an all-day-long free-living setting. With more than 15.8 hours of battery life, NeckSense will allow researchers and dietitians to better understand natural chewing and eating behaviors. In the future, researchers and dietitians can use NeckSense to provide appropriate real-time interventions when an eating episode is detected or when problematic eating is identified.

Original languageEnglish (US)
Article number72
JournalProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Volume4
Issue number2
DOIs
StatePublished - Jun 15 2020

Keywords

  • automated dietary monitoring
  • eating activity detection
  • free-living studies
  • human activity recognition
  • neck-worn sensor
  • sensor fusion
  • wearable

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Human-Computer Interaction

Fingerprint Dive into the research topics of 'NeckSense: A Multi-Sensor Necklace for Detecting Eating Activities in Free-Living Conditions'. Together they form a unique fingerprint.

Cite this