11th International Conference on Body Area Networks

Research Article

SmartNecklace: Designing a Wearable Multi-sensor System for Smart Eating Detection

  • @INPROCEEDINGS{10.4108/eai.15-12-2016.2267882,
        author={Eli Cohen and William Stogin and Haik Kalantarian and Nabil Alshurafa and Angela Pfammatter and Bonnie Spring},
        title={SmartNecklace: Designing a Wearable Multi-sensor System for Smart Eating Detection},
        proceedings={11th International Conference on Body Area Networks},
        keywords={wearables passive sensing eating detection alone piezoelectric sensor audio accelerometer wireless},
  • Eli Cohen
    William Stogin
    Haik Kalantarian
    Nabil Alshurafa
    Angela Pfammatter
    Bonnie Spring
    Year: 2017
    SmartNecklace: Designing a Wearable Multi-sensor System for Smart Eating Detection
    DOI: 10.4108/eai.15-12-2016.2267882
Eli Cohen1,*, William Stogin1, Haik Kalantarian2, Nabil Alshurafa1, Angela Pfammatter3, Bonnie Spring3
  • 1: Northwestern University
  • 2: UCLA
  • 3: Northwestern University Chicago, IL 60611
*Contact email: ecohen@u.northwestern.edu


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-reflection, 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 withinsubject 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 multilayered 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.