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

arXiv: https://arxiv.org/abs/1911.07179 · HTML: https://mlsanity.com/paper/1911.07179/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2019.

## Built on by (most cited first, 12 of 12)

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- [Eat-Radar: Continuous Fine-Grained Intake Gesture Detection Using FMCW Radar and 3D Temporal Convolutional Network with Attention](https://mlsanity.com/paper/2211.04253/) (2022)
- [IMUCoCo: Enabling Flexible On-Body IMU Placement for Human Pose Estimation and Activity Recognition](https://mlsanity.com/paper/2508.01894/) (2025)
- [Smart-Badge: A wearable badge with multi-modal sensors for kitchen activity recognition](https://mlsanity.com/paper/2210.00888/) (2022)
- [SalienTrack: providing salient information for semi-automated self-tracking feedback with model explanations](https://mlsanity.com/paper/2109.10231/) (2021)
- [SpecTracle: Wearable Facial Motion Tracking from Unobtrusive Peripheral Cameras](https://mlsanity.com/paper/2308.07502/) (2023)
- [What's on Your Plate? Inferring Chinese Cuisine Intake from Wearable IMUs](https://mlsanity.com/paper/2511.05292/) (2025)
- [JEEVHITAA -- An HCAI Ecosystem to Support Collective Care](https://mlsanity.com/paper/2512.06364/) (2025)

## Abstract (arXiv)

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. The 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 with 11 obese and 9 non-obese participants across two studies, where we collected more than 470 hours of data in naturalistic setting. Our result demonstrates that NeckSense enables reliable eating-detection for 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 a F1-score of 77.1% for episodes even in an all-day-around 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, and also enable real-time interventions.