SmartNecklace: Designing a wearable multi-sensor system for smart eating detection

Eli Cohen*, William Stogin, Haik Kalantarian, Angela F. Pfammatter, Bonnie Spring, Nabil Alshurafa

*Corresponding author for this work

Research output: Contribution to journalConference article


Characterizing eating behaviors to inform and prevent obesity requires nutritionists, behaviorists and interventionists to disrupt subjects' routine with questionnaires and unfamiliar eating environments. Such a disruption may be necessary as a means of self-reection, however, prevents researchers from capturing problematic eating behaviors in a free-living environment. An automated system alleviates many of these disruptions; however, success in automating sensing of eating habits has proven to be a challenge due to high within-subject variability in people's eating habits. Given a positive correlation between eating duration and caloric intake, along with the fact that many problematic eaters spend time alone, this paper presents a passive sensing system designed with the following three goals: Detecting eating episodes through data analytics of passive sensors, detecting time spent alone while eating, and designing a passive sensing system that people will adhere to wearing in the field, without disrupting regular activity or behavior. A real-time coarse multi-layered classification approach is proposed to detect challenging eating episodes with confounding factors using data from piezoelectric, audio, and inertial sensors. The system was tested on 7 participants with 14 eating episodes, resulting in an 80.8%, and 91.3% average F-measure for detection of eating and alone time, respectively. Additionally, results of a survey highlights the importance of user-customization to increase adherence to neck-worn sensors.

Original languageEnglish (US)
JournalHealth Psychology Review
StatePublished - Apr 24 2017
Event11th International Conference on Body Area Networks, BODYNETS 2016 - Turin, Italy
Duration: Dec 15 2016Dec 16 2016


  • Accelerometer
  • Alone
  • Audio
  • Eating detection
  • Passive sensing
  • Piezoelectric sensor
  • Wearables
  • Wireless

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications


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