ArticleScientific reports2024
Integrated image and sensor-based food intake detection in free-living.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Identifying temporal eating patterns: a comparison of latent class analysis and dynamic time warping-based cluster analysis.The American journal of clinical nutrition · 2026Article
- Advances and opportunities in measuring dietary intake: from omics to AI.Nature metabolism · 2026Review
- Validity of self-assessment tools for cardiovascular risk behaviors: A systematic review.American journal of preventive cardiology · 2025Review
- Multiple physiological and behavioural parameters identification for dietary monitoring using wearable sensors: a study protocol.BMC nutrition · 2025Article
- Multi-Task NoisyViT for Enhanced Fruit and Vegetable Freshness Detection and Type Classification.Sensors (Basel, Switzerland) · 2025Article
- HabitSense: A Privacy-Aware, AI-Enhanced Multimodal Wearable Platform for mHealth Applications.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2024Article
- Comparison of Self-Reported Dietary Recalls and Real-Time Tools to Track Mealtimes in Older Adults: A Pilot Study.Inquiry : a journal of medical care organization, provision and financingObservational
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Authors and funding
9 authors.
Funding
Abstract
The first step in any dietary monitoring system is the automatic detection of eating episodes. To detect eating episodes, either sensor data or images can be used, and either method can result in false-positive detection. This study aims to reduce the number of false positives in the detection of eating episodes by a wearable sensor, Automatic Ingestion Monitor v2 (AIM-2). Thirty participants wore the AIM-2 for two days each (pseudo-free-living and free-living). The eating episodes were detected by three methods: (1) recognition of solid foods and beverages in images captured by AIM-2; (2) recognition of chewing from the AIM-2 accelerometer sensor; and (3) hierarchical classification to combine confidence scores from image and accelerometer classifiers. The integration of image- and sensor-based methods achieved 94.59% sensitivity, 70.47% precision, and 80.77% F1-score in the free-living environment, which is significantly better than either of the original methods (8% higher sensitivity). The proposed method successfully reduces the number of false positives in the detection of eating episodes.
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Registered trials
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