ArticleIEEE sensors journal2018
Accelerometer-Based Detection of Food Intake in Free-living Individuals.
Article in IEEE sensors journal, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 68 citations in OpenAlex.
- A Systematic Review of Sensor-Based Methods for Measurement of Eating Behavior.Sensors (Basel, Switzerland) · 2025Pooled it
- Using Momentary Assessment and Machine Learning to Identify Barriers to Self-management in Type 1 Diabetes: Observational Study.JMIR mHealth and uHealth · 2022Trial
- Development and Validation of a Universal Eating Monitor (UEM) for Distinguishing the Intake of Multiple Foods and Macronutrients.Nutrients · 2025Article
- iEat: automatic wearable dietary monitoring with bio-impedance sensing.Scientific reports · 2024Article
- Integrated image and sensor-based food intake detection in free-living.Scientific reports · 2024Article
- Enabling Eating Detection in a Free-living Environment: Integrative Engineering and Machine Learning Study.Journal of medical Internet research · 2022Article
- Enhancing Nutrition Care Through Real-Time, Sensor-Based Capture of Eating Occasions: A Scoping Review.Frontiers in nutrition · 2022Article
- Detection of Food Intake Sensor's Wear Compliance in Free-Living.IEEE sensors journal · 2021Article
- Smartwatch-Based Eating Detection: Data Selection for Machine Learning from Imbalanced Data with Imperfect Labels.Sensors (Basel, Switzerland) · 2021Article
- "Automatic Ingestion Monitor Version 2" - A Novel Wearable Device for Automatic Food Intake Detection and Passive Capture of Food Images.IEEE journal of biomedical and health informatics · 2021Article
- A Dual-Padded, Protrusion-Incorporated, Ring-Type Sensor for the Measurement of Food Mass and Intake.Sensors (Basel, Switzerland) · 2020Article
- Retrieval and Timing Performance of Chewing-Based Eating Event Detection in Wearable Sensors.Sensors (Basel, Switzerland) · 2020Article
- Selective Content Removal for Egocentric Wearable Camera in Nutritional Studies.IEEE access : practical innovations, open solutions · 2020Article
- Automatic Count of Bites and Chews From Videos of Eating Episodes.IEEE access : practical innovations, open solutions · 2020Article
- Automatic, wearable-based, in-field eating detection approaches for public health research: a scoping review.NPJ digital medicine · 2020Article
- Evaluation of Pattern Recognition Methods for Head Gesture-Based Interface of a Virtual Reality Helmet Equipped with a Single IMU Sensor.Sensors (Basel, Switzerland) · 2019Article
- Validation of Sensor-Based Food Intake Detection by Multicamera Video Observation in an Unconstrained Environment.Nutrients · 2019Article
- Blood Sugar Level Indication Through Chewing and Swallowing from Acoustic MEMS Sensor and Deep Learning Algorithm for Diabetic Management.Journal of medical systems · 2018Article
- Voice-assisted Food Recall using Voice Assistants.HCI International 2022 - late breaking papers : 24th International Conference on Human-Computer Interaction, HCII 2022, virtual event, June 26 - July 1, 2022, proceedings. HCI for health, well-being, universal access and healthy aging. ...Article
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Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
The goal of this pilot study is to evaluate the feasibility of using a 3-axis accelerometer attached to the frame of eyeglasses for automatic detection of food intake. A 3D acceleration sensor was attached to the temple of the regular eyeglasses. Ten participants wore the device in two visits (first, laboratory; second, free-living) on different days, reporting the food intake episodes using a pushbutton. Hold-one-out procedure was used to test the algorithm for food intake detection. The accelerometer signal was split into epochs of varying durations (3s, 5s, 10s 15s, 20s, 25s, and 30s); 152 time and frequency domain features were computed for each epoch. A two-stage procedure was used for finding the best feature set suitable for classification. The first stage used minimum Redundancy and Maximum Relevance (mRMR) to get the 30 top-ranked features and the second stage used forward feature selection along with a kNN classifier to get the optimum feature set for each hold-one-out set. The best average F1-score combined from laboratory and free-living experiments was 87.9 +/- 13.8% (Mean±Standard Deviation) for 20s epochs; and 84.7 +/- 7.95% for the shortest epoch of 3s. The results suggest that accelerometer may provide a compelling alternative to other sensor modalities, as the proposed sensor does not require direct attachment to the body and, therefore, significantly improves user comfort and social acceptability of the food intake monitoring system.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.