ArticlePhysiological measurement2008
Non-invasive monitoring of chewing and swallowing for objective quantification of ingestive behavior.
Article in Physiological measurement, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
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Who cites it
62 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A Systematic Review of Sensor-Based Methods for Measurement of Eating Behavior.Sensors (Basel, Switzerland) · 2025Pooled it
- Technology to Automatically Record Eating Behavior in Real Life: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Using Wearable and Non-Invasive Sensors to Measure Swallowing Function: Detection, Verification, and Clinical Application.Sensors (Basel, Switzerland) · 2019Trial
- Evaluating the Accuracy of Declared Eating Schedules by Continuous Glucose Monitoring.Nutrients · 2026Article
- Multimodal deep ensemble classification system with wearable vibration sensor for detecting throat-related events.NPJ digital medicine · 2025Article
- Central Hemodynamic and Thermoregulatory Responses to Food Intake as Potential Biomarkers for Eating Detection: Systematic Review.Interactive journal of medical research · 2024Review
- Developing a Swallow-State Monitoring System Using Nasal Airflow, Surface Electromyography, and Thyroid Cartilage Movement Detection.Bioengineering (Basel, Switzerland) · 2024Article
- Eating Event Recognition Using Accelerometer, Gyroscope, Piezoelectric, and Lung Volume Sensors.Sensors (Basel, Switzerland) · 2024Article
- Detecting Eating and Social Presence with All Day Wearable RGB-T....IEEE...International Conference on Connected Health: Applications, Systems and Engineering Technologies. IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies · 2023Article
- Machine learning modeling practices to support the principles of AI and ethics in nutrition research.Nutrition & diabetes · 2022Article
- Predicting energy intake in adults who are dieting and exercising.International journal of obesity (2005) · 2022Article
- FOODCAM: A Novel Structured Light-Stereo Imaging System for Food Portion Size Estimation.Sensors (Basel, Switzerland) · 2022Article
- Impact of Masticatory Behaviors Measured With Wearable Device on Metabolic Syndrome: Cross-sectional Study.JMIR mHealth and uHealth · 2022Article
- Study of occlusal acoustic parameters in assessing masticatory performance.BMC oral health · 2022Article
- Top-Down Detection of Eating Episodes by Analyzing Large Windows of Wrist Motion Using a Convolutional Neural Network.Bioengineering (Basel, Switzerland) · 2022Article
- Perspective: Opportunities and Challenges of Technology Tools in Dietary and Activity Assessment: Bridging Stakeholder Viewpoints.Advances in nutrition (Bethesda, Md.) · 2022Article
- Short and Long-Term Innovations on Dietary Behavior Assessment and Coaching: Present Efforts and Vision of the Pride and Prejudice Consortium.International journal of environmental research and public health · 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
- Food/Non-Food Classification of Real-Life Egocentric Images in Low- and Middle-Income Countries Based on Image Tagging Features.Frontiers in artificial intelligence · 2021Article
2 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors.
Funding
Abstract
A methodology of studying of ingestive behavior by non-invasive monitoring of swallowing (deglutition) and chewing (mastication) has been developed. The target application for the developed methodology is to study the behavioral patterns of food consumption and producing volumetric and weight estimates of energy intake. Monitoring is non-invasive based on detecting swallowing by a sound sensor located over laryngopharynx or by a bone-conduction microphone and detecting chewing through a below-the-ear strain sensor. Proposed sensors may be implemented in a wearable monitoring device, thus enabling monitoring of ingestive behavior in free-living individuals. In this paper, the goals in the development of this methodology are two-fold. First, a system comprising sensors, related hardware and software for multi-modal data capture is designed for data collection in a controlled environment. Second, a protocol is developed for manual scoring of chewing and swallowing for use as a gold standard. The multi-modal data capture was tested by measuring chewing and swallowing in 21 volunteers during periods of food intake and quiet sitting (no food intake). Video footage and sensor signals were manually scored by trained raters. Inter-rater reliability study for three raters conducted on the sample set of five subjects resulted in high average intra-class correlation coefficients of 0.996 for bites, 0.988 for chews and 0.98 for swallows. The collected sensor signals and the resulting manual scores will be used in future research as a gold standard for further assessment of sensor design, development of automatic pattern recognition routines and study of the relationship between swallowing/chewing and ingestive behavior.
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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.