This paper is in the following e-collection/theme issue:

New Methods (1140) Machine Learning (2095) Wearable Devices and Sensors (1150) Artificial Intelligence (2162) mHealth for Data Collection and Research (1207)

Published on in Vol 24, No 3 (2022): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/27934, first published .
Enabling Eating Detection in a Free-living Environment: Integrative Engineering and Machine Learning Study

Enabling Eating Detection in a Free-living Environment: Integrative Engineering and Machine Learning Study

Enabling Eating Detection in a Free-living Environment: Integrative Engineering and Machine Learning Study

Authors of this article:

Bo Zhang1 Author Orcid Image ;   Kaiwen Deng2 Author Orcid Image ;   Jie Shen1 Author Orcid Image ;   Lingrui Cai3 Author Orcid Image ;   Bohdana Ratitch1 Author Orcid Image ;   Haoda Fu1 Author Orcid Image ;   Yuanfang Guan2 Author Orcid Image
Article Authors Cited by (3) Tweetations (4) Metrics

Journals

  1. Wang L, Allman-Farinelli M, Hekler E, Rangan A. A scoping review of sensor-based capture of eating and drinking occasions that could be used for enhancing personalized nutrition interventions in real-time. Advances in Nutrition 2025:100575 View

Conference Proceedings

  1. Chung Y, Nikooienejad A, Zhang B. 2022 IEEE International Conference on Big Data (Big Data). Automatic Eating Behavior Detection from Wrist Motion Sensor Using Bayesian, Gradient Boosting, and Topological Persistence Methods View
  2. Santoso K, Maharani M, Sidharta S. 2024 International Conference on Information Management and Technology (ICIMTech). Identifying Abnormal Eating Behavior Patterns with Machine Learning for Early Detection of Eating Disorders: A Systematic Literature Review View