MT-Diet: Automated smartphone based diet assessment with infrared images

Lee, Junghyo; Banerjee, Ayan; Gupta, Sandeep

doi:10.1109/percom.2016.7456506

Cited by 14 publications

(8 citation statements)

References 14 publications

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“…IDEA can also be used to automatically annotate eating actions for future use in a personalized model. When combined with image based food type identification projects such as MT-Diet [2], IDEA can be applied to build a nutritional retrieval system. Also, the IDEA methodology will be useful for wristband based sign language recognition projects such as DyFAV [5].…”

Section: Discussionmentioning

confidence: 99%

Idea

Lee

Paudyal

Banerjee

et al. 2018

Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization

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Eating activity monitoring using wearable sensors can potentially enable interventions based on eating speed for critical healthcare problems such as obesity or diabetes. We propose a novel methodology, IDEA that performs accurate eating action identification and provides feedback on eating speed. IDEA uses a single wristband with IMU sensors and functions without any manual intervention from the user. The F1 score for eating action identification was 0.92.

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Section: Discussionmentioning

confidence: 99%

Idea

Lee

Paudyal

Banerjee

et al. 2018

Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization

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“…In this regard, Guo et al [129] puts forward the concept of visual crowdsensing(VCS), and summarizes the task models, characteristics, important technologies and applications of VCS in recent years. According to the summaryof VCS [129], its application scope can be divided into: floor plan generation [130], scene reconstruction [131], event reconstruction [132], indoor localization [133], indoor navigation [134], personal wellness and health [135], disaster relief [136], and city awareness [137]. In most cases, MCS is better than traditional visual perception methods that rely on fixed visual perception devices for monitoring.…”

Section: E Event Reconstructionmentioning

confidence: 99%

“…Social [139] Health [135] Smart City [131], [137] Architecture [130] Navigation [131], [133], [134] Rescue [136] Image or video information fusion [132], [140] Event location [139] Event matching and clustering [138] Crowdsourcing analysis [141] Object Pose Measurement…”

Section: A Vision Acquisitionmentioning

confidence: 99%

Visual Perception Enabled Industry Intelligence: State of the Art, Challenges and Prospects

Yang

Wang

Jiang

et al. 2021

IEEE Trans. Ind. Inf.

168

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Visual perception refers to the process of organizing, identifying and interpreting visual information in environmental awareness and understanding. With the rapid progress of multimedia acquisition technology, research on visual perception has been a hot topic in the academical field and industrial applications. Especially after the introduction of artificial intelligence theory, intelligent visual perception has been widely used to promote the development of industrial production towards intelligence. In this paper, we review the previous research and application of visual perception in different industrial fields such as product surface defect detection, intelligent agricultural production, intelligent driving, image synthesis and event reconstruction. The applications basically cover most of the intelligent visual perception processing technologies. Through this survey, it will provide a comprehensive reference for research on this direction. Finally, this paper also summarizes the current challenges of visual perception and predicts its future development trends.

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“…Recent works [11,15,19,24,33,42] that provide nutrition information from a meal generally focus on the entire served food and not on the amount and type of food that was actually consumed. To be able to access nutrition information from the actual food consumed, before and after images of the food plate have been used.…”

Section: Integration With Nutrition Estimation Systemsmentioning

confidence: 99%

A User-adaptive Modeling for Eating Action Identification from Wristband Time Series

Lee

Paudyal

Banerjee

et al. 2019

ACM Trans. Interact. Intell. Syst.

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Eating activity monitoring using wearable sensors can potentially enable interventions based on eating speed to mitigate the risks of critical healthcare problems such as obesity or diabetes. Eating actions are polycomponential gestures composed of sequential arrangements of three distinct components interspersed with gestures that may be unrelated to eating. This makes it extremely challenging to accurately identify eating actions. The primary reasons for the lack of acceptance of state-of-the-art eating action monitoring techniques include the following: (i) the need to install wearable sensors that are cumbersome to wear or limit the mobility of the user, (ii) the need for manual input from the user, and (iii) poor accuracy in the absence of manual inputs. In this work, we propose a novel methodology, IDEA, that performs accurate eating action identification within eating episodes with an average F1 score of 0.92. This is an improvement of 0.11 for precision and 0.15 for recall for the worst-case users as compared to the state of the art. IDEA uses only a single wristband and provides feedback on eating speed every 2 min without obtaining any manual input from the user. CCS Concepts: • Human-centered computing → Ubiquitous and mobile computing;

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MT-Diet: Automated smartphone based diet assessment with infrared images

Cited by 14 publications

References 14 publications

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Idea

Visual Perception Enabled Industry Intelligence: State of the Art, Challenges and Prospects

A User-adaptive Modeling for Eating Action Identification from Wristband Time Series

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