ArticleScientific reports2017
A glasses-type wearable device for monitoring the patterns of food intake and facial activity.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 59 citations in OpenAlex.
- Advances and opportunities in measuring dietary intake: from omics to AI.Nature metabolism · 2026Review
- Controlled and Real-Life Investigation of Optical Tracking Sensors in Smart Glasses for Monitoring Eating Behavior Using Deep Learning: Cross-Sectional Study.JMIR mHealth and uHealth · 2024Article
- Metaverse Wearables for Immersive Digital Healthcare: A Review.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Review
- 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
- Deep Learning-Based Multimodal Data Fusion: Case Study in Food Intake Episodes Detection Using Wearable Sensors.JMIR mHealth and uHealth · 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
- Transparent and attachable ionic communicators based on self-cleanable triboelectric nanogenerators.Nature communications · 2018Article
- Accelerometer-Based Detection of Food Intake in Free-living Individuals.IEEE sensors journal · 2018Article
- Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification.Journal of visualized experiments : JoVE · 2018Article
- A Mobile-Based Diet Monitoring System for Obesity Management.Journal of health & medical informatics · 2018Article
- EarBit: Using Wearable Sensors to Detect Eating Episodes in Unconstrained Environments.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2017Article
- A Survey on Automated Food Monitoring and Dietary Management Systems.Journal of health & medical informatics · 2017Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Here we present a new method for automatic and objective monitoring of ingestive behaviors in comparison with other facial activities through load cells embedded in a pair of glasses, named GlasSense. Typically, activated by subtle contraction and relaxation of a temporalis muscle, there is a cyclic movement of the temporomandibular joint during mastication. However, such muscular signals are, in general, too weak to sense without amplification or an electromyographic analysis. To detect these oscillatory facial signals without any use of obtrusive device, we incorporated a load cell into each hinge which was used as a lever mechanism on both sides of the glasses. Thus, the signal measured at the load cells can detect the force amplified mechanically by the hinge. We demonstrated a proof-of-concept validation of the amplification by differentiating the force signals between the hinge and the temple. A pattern recognition was applied to extract statistical features and classify featured behavioral patterns, such as natural head movement, chewing, talking, and wink. The overall results showed that the average F
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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.