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Deep Learning–Based Multimodal Data Fusion: Case Study in Food Intake Episodes Detection Using Wearable Sensors

https://doi.org/10.2196/21926

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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New Trends in Image Analysis and Processing – ICIAP 2017

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Learning CNN-based Features for Retrieval of Food Images

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Automatic, wearable-based, in-field eating detection approaches for public health research: a scoping review

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A glasses-type wearable device for monitoring the patterns of food intake and facial activity

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A novel approach for food intake detection using electroglottography

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On the Personalization of Classification Models for Human Activity Recognition

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Automatic Food Intake Monitoring Based on Chewing Activity: A Survey

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WiWeHAR: Multimodal Human Activity Recognition Using Wi-Fi and Wearable Sensing Modalities

inferredcites →https://doi.org/10.1109/bhi.2016.7455916openalex-snapshot

Detecting food intake acoustic events in noisy recordings using template matching

inferredcites →https://doi.org/10.1109/bhi.2018.8333386openalex-snapshot

Free-living eating event spotting using EMG-monitoring eyeglasses

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iHear Food: Eating Detection Using Commodity Bluetooth Headsets

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Automatic Detection of Periods of Eating Using Wrist Motion Tracking

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A Comparative Study of Food Intake Detection Using Artificial Neural Network and Support Vector Machine

inferredcites →https://doi.org/10.1109/iembs.2010.5627796openalex-snapshot

Detection of periods of food intake using Support Vector Machines

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Food Intake Monitoring: Automated Chew Event Detection in Chewing Sounds

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Genetic Algorithm-Based Classifiers Fusion for Multisensor Activity Recognition of Elderly People

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A Novel Chewing Detection System Based on PPG, Audio, and Accelerometry

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Monitoring Chewing and Eating in Free-Living Using Smart Eyeglasses

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Physical Activity Recognition Using Posterior-Adapted Class-Based Fusion of Multiaccelerometer Data

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Ensemble Learning Approach via Kalman Filtering for a Passive Wearable Respiratory Monitor

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inferredcites →https://doi.org/10.1109/jbhi.2020.2984907openalex-snapshot

A Data Driven End-to-End Approach for In-the-Wild Monitoring of Eating Behavior Using Smartwatches

inferredcites →https://doi.org/10.1109/jiot.2017.2656151openalex-snapshot

Your Glasses Know Your Diet: Dietary Monitoring Using Electromyography Sensors

inferredcites →https://doi.org/10.1109/jsen.2011.2166066openalex-snapshot

Motion Measurement Using Inertial Sensors, Ultrasonic Sensors, and Magnetometers With Extended Kalman Filter for Data Fusion

inferredcites →https://doi.org/10.1109/jsen.2011.2172411openalex-snapshot

A Sensor System for Automatic Detection of Food Intake Through Non-Invasive Monitoring of Chewing

inferredcites →https://doi.org/10.1109/jsen.2017.2782492openalex-snapshot

Human Activity Classification in Smartphones Using Accelerometer and Gyroscope Sensors

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A Data Fusion-Based Hybrid Sensory System for Older People’s Daily Activity and Daily Routine Recognition

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The case for smartwatch-based diet monitoring

inferredcites →https://doi.org/10.1109/percomw.2018.8480222openalex-snapshot

A Privacy-Preserving Wearable Camera Setup for Dietary Event Spotting in Free-Living

inferredcites →https://doi.org/10.1109/tase.2009.2021981openalex-snapshot

Robust Location-Aware Activity Recognition Using Wireless Sensor Network in an Attentive Home

inferredcites →https://doi.org/10.1109/tbme.2011.2178070openalex-snapshot

Multisensor Data Fusion for Physical Activity Assessment

inferredcites →https://doi.org/10.1109/tbme.2016.2604856openalex-snapshot

Hierarchical Complex Activity Representation and Recognition Using Topic Model and Classifier Level Fusion

inferredcites →https://doi.org/10.1109/tbme.2016.2631246openalex-snapshot

Unobtrusive and Wearable Systems for Automatic Dietary Monitoring

inferredcites →https://doi.org/10.1109/thms.2018.2884717openalex-snapshot

A Multisensor Multiclassifier Hierarchical Fusion Model Based on Entropy Weight for Human Activity Recognition Using Wearable Inertial Sensors

inferredcites →https://doi.org/10.1109/titb.2010.2091684openalex-snapshot

Using the Dempster–Shafer Theory of Evidence With a Revised Lattice Structure for Activity Recognition

inferredcites →https://doi.org/10.1109/tmc.2016.2599158openalex-snapshot

An Adaptive Bayesian System for Context-Aware Data Fusion in Smart Environments

inferredcites →https://doi.org/10.1109/tsmca.2010.2093883openalex-snapshot

Wearable Sensor-Based Hand Gesture and Daily Activity Recognition for Robot-Assisted Living

inferredcites →https://doi.org/10.1109/wacv.2009.5403087openalex-snapshot

Recognition and volume estimation of food intake using a mobile device

inferredcites →https://doi.org/10.1109/wh.2016.7764549openalex-snapshot

Eating gestures detection by tracking finger motion

inferredcites →https://doi.org/10.1109/wowmom.2018.8449755openalex-snapshot

Annapurna: Building a Real-World Smartwatch-Based Automated Food Journal

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A practical approach for recognizing eating moments with wrist-mounted inertial sensing

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Detecting Mastication

inferredcites →https://doi.org/10.1145/2851581.2892426openalex-snapshot

Assisting Food Journaling with Automatic Eating Detection

inferredcites →https://doi.org/10.1145/3130902openalex-snapshot

EarBit

inferredcites →https://doi.org/10.1145/3131894openalex-snapshot

Recognizing Eating from Body-Worn Sensors

inferredcites →https://doi.org/10.1145/3191736openalex-snapshot

Detecting Eating Episodes by Tracking Jawbone Movements with a Non-Contact Wearable Sensor

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Applying Multivariate Segmentation Methods to Human Activity Recognition From Wearable Sensors’ Data

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The SPLENDID Eating Detection Sensor: Development and Feasibility Study

inferredcites →https://doi.org/10.23919/acc.2018.8430775openalex-snapshot

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Assessing Eating Behaviour Using Upper Limb Mounted Motion Sensors: A Systematic Review

inferredcites →https://doi.org/10.7551/mitpress/11474.003.0014openalex-snapshot

Neural Information Processing Systems

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A Comparative Study of Deep Learning Algorithms for Detecting Food Intake

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Skeleton-Based Intake Gesture Detection With Spatial-Temporal Graph Convolutional Networks

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Food Intake Detection in the Face of Limited Sensor Signal Annotations

inferred← citeshttps://doi.org/10.1109/iceeict53079.2022.9768487openalex-snapshot

Hybrid BiLSTM-HMM based event detection and classification system for food intake recognition

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Psychotic Relapse Prediction in Schizophrenia Patients Using A Personalized Mobile Sensing-Based Supervised Deep Learning Model

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Advances in Sensing Techniques and Deep Learning for Food Quality Detection: Opportunities, Challenges, and Perspectives from Image Recognition to Multimodal Data Fusion

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