# Assessing the Accuracy of a Wrist Motion Tracking Method for Counting Bites across Demographic and Food Variables

arXiv: https://arxiv.org/abs/1806.05352 · HTML: https://mlsanity.com/paper/1806.05352/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2018.

## Built on by (most cited first, 7 of 7)

- [Dietary Assessment with Multimodal ChatGPT: A Systematic Analysis](https://mlsanity.com/paper/2312.08592/) (2023)
- [Egocentric Image Captioning for Privacy-Preserved Passive Dietary Intake Monitoring](https://mlsanity.com/paper/2107.00372/) (2021)
- [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)
- [Robust Multimodal Learning Framework For Intake Gesture Detection Using Contactless Radar and Wearable IMU Sensors](https://mlsanity.com/paper/2507.07261/) (2025)
- [An Intelligent Passive Food Intake Assessment System with Egocentric Cameras](https://mlsanity.com/paper/2105.03142/) (2021)
- [Predicting the meal macronutrient composition from continuous glucose monitors](https://mlsanity.com/paper/2206.11878/) (2022)
- [Skeleton-Based Intake Gesture Detection With Spatial-Temporal Graph Convolutional Networks](https://mlsanity.com/paper/2504.10635/) (2025)

## Abstract (arXiv)

This paper describes a study to test the accuracy of a method that tracks wrist motion during eating to detect and count bites. The purpose was to assess its accuracy across demographic (age, gender, ethnicity) and bite (utensil, container, hand used, food type) variables. Data were collected in a cafeteria under normal eating conditions. A total of 271 participants ate a single meal while wearing a watch-like device to track their wrist motion. Video was simultaneously recorded of each participant and subsequently reviewed to determine the ground truth times of bites. Bite times were operationally defined as the moment when food or beverage was placed into the mouth. Food and beverage choices were not scripted or restricted. Participants were seated in groups of 2-4 and were encouraged to eat naturally. A total of 24,088 bites of 374 different food and beverage items were consumed. Overall the method for automatically detecting bites had a sensitivity of 75% with a positive predictive value of 89%. A range of 62-86% sensitivity was found across demographic variables, with slower eating rates trending towards higher sensitivity. Variations in sensitivity due to food type showed a modest correlation with the total wrist motion during the bite, possibly due to an increase in head-towards-plate motion and decrease in hand-towards-mouth motion for some food types. Overall, the findings provide the largest evidence to date that the method produces a reliable automated measure of intake during unrestricted eating.