research-article

AI-Assisted Food Intake Activity Recognition Using 3D mmWave Radars

Authors:

Shervin Shirmohammadi,

Cheng-Hsin HsuAuthors Info & Claims

MADiMa '22: Proceedings of the 7th International Workshop on Multimedia Assisted Dietary Management

Pages 81 - 89

https://doi.org/10.1145/3552484.3555753

Published: 24 October 2022 Publication History

Abstract

The automatic recognition of when and for how long a person is eating a certain food or drinking has applications in telecare, smarthome data monetization, and diet control. Existing food recognition systems either recognize the type of the food, but not when and for how long the person was eating and drinking, or use invasive sensors or privacy-intruding cameras which users are hesitant to install in their homes. In this paper, we propose a non-invasive system, using Artificial Intelligence to process 3D point cloud data collected from a 3D mmWave radar, that can distinguish a person's eating and drinking activities from other daily activities. This is challenging because eating and drinking activities are much more fine-grained than activities that existing systems can detect, such as sitting, running, walking, etc. Performance evaluations show that our proposed system significantly outperforms a representative state-of-the-art activity recognition system, RadHAR, by at least 27% and can reach 96.56% and 96.73% accuracy for two different training/testing split setups.

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Cited By

Raeis HKazemi MShirmohammadi S(2024)CAE-MAS: Convolutional Autoencoder Interference Cancellation for Multiperson Activity Sensing With FMCW Microwave RadarIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2024.336657573(1-10)Online publication date: 2024
https://doi.org/10.1109/TIM.2024.3366575
Wang CKumar TDe Raedt WCamps GHallez HVanrumste B(2024)Evaluation Metrics for Food Intake Activity Recognition Using Segment-Wise IoU2024 IEEE International Symposium on Medical Measurements and Applications (MeMeA)10.1109/MeMeA60663.2024.10596709(1-6)Online publication date: 26-Jun-2024
https://doi.org/10.1109/MeMeA60663.2024.10596709
Wang CKumar TDe Raedt WCamps GHallez HVanrumste B(2024)Eat-Radar: Continuous Fine-Grained Intake Gesture Detection Using FMCW Radar and 3D Temporal Convolutional Network With AttentionIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2023.333970328:2(1000-1011)Online publication date: Feb-2024
https://doi.org/10.1109/JBHI.2023.3339703
Show More Cited By

Index Terms

AI-Assisted Food Intake Activity Recognition Using 3D mmWave Radars
1. Information systems
  1. Information systems applications
    1. Multimedia information systems

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cover image ACM Conferences

MADiMa '22: Proceedings of the 7th International Workshop on Multimedia Assisted Dietary Management

October 2022

97 pages

ISBN:9781450395021

DOI:10.1145/3552484

General Chairs:
Stavroula Mougiakakou
University of Bern, Switzerland
,
Giovanni Maria Farinella
University of Catania, Italy
,
Keiji Yanai
The University of Electro-Communications, Tokyo, Japan
,
Dario Allegra
University of Catania, Italy

Copyright © 2022 ACM.

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Published: 24 October 2022

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MM '22: The 30th ACM International Conference on Multimedia

October 10, 2022

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MADiMa '22 Paper Acceptance Rate 9 of 10 submissions, 90%;

Overall Acceptance Rate 16 of 24 submissions, 67%

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Cited By

Raeis HKazemi MShirmohammadi S(2024)CAE-MAS: Convolutional Autoencoder Interference Cancellation for Multiperson Activity Sensing With FMCW Microwave RadarIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2024.336657573(1-10)Online publication date: 2024
https://doi.org/10.1109/TIM.2024.3366575
Wang CKumar TDe Raedt WCamps GHallez HVanrumste B(2024)Evaluation Metrics for Food Intake Activity Recognition Using Segment-Wise IoU2024 IEEE International Symposium on Medical Measurements and Applications (MeMeA)10.1109/MeMeA60663.2024.10596709(1-6)Online publication date: 26-Jun-2024
https://doi.org/10.1109/MeMeA60663.2024.10596709
Wang CKumar TDe Raedt WCamps GHallez HVanrumste B(2024)Eat-Radar: Continuous Fine-Grained Intake Gesture Detection Using FMCW Radar and 3D Temporal Convolutional Network With AttentionIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2023.333970328:2(1000-1011)Online publication date: Feb-2024
https://doi.org/10.1109/JBHI.2023.3339703
Diraco GRescio GSiciliano PLeone A(2023)Review on Human Action Recognition in Smart Living: Sensing Technology, Multimodality, Real-Time Processing, Interoperability, and Resource-Constrained ProcessingSensors10.3390/s2311528123:11(5281)Online publication date: 2-Jun-2023
https://doi.org/10.3390/s23115281
Moghaddam MNazari Shirehjini AShirmohammadi S(2023)Device-Free Fine-Grained Dining Activity Sensing2023 IEEE Sensors Applications Symposium (SAS)10.1109/SAS58821.2023.10254010(1-6)Online publication date: 18-Jul-2023
https://doi.org/10.1109/SAS58821.2023.10254010
Wang CKumar TDe Raedt WCamps GHallez HVanrumste B(2023)Evaluation of Different Radar Placements for Food Intake Monitoring Using Deep Learning2023 IEEE Radar Conference (RadarConf23)10.1109/RadarConf2351548.2023.10149626(1-6)Online publication date: 1-May-2023
https://doi.org/10.1109/RadarConf2351548.2023.10149626

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