SmrtFridge

Sharma, Amit; Misra, Archan; Subramaniam, Vengateswaran; Lee, Mi Kyung

doi:10.1145/3356250.3360028

Cited by 7 publications

(8 citation statements)

References 13 publications

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“…Some research groups developed so-called smart fridges that automatically register food items and quantities. In [ 40 ], image analysis of a thermal image taken by an infrared (IR) sensor embedded in a fridge provides an estimation of a drink volume. Another study proposed a system called the Playful Bottle system [ 41 ], which consists of a smartphone attached to a common drinking mug.…”

Section: Resultsmentioning

confidence: 99%

Technology to Automatically Record Eating Behavior in Real Life: A Systematic Review

Hiraguchi,

Perone,

Toet

et al. 2023

Sensors

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To monitor adherence to diets and to design and evaluate nutritional interventions, it is essential to obtain objective knowledge about eating behavior. In most research, measures of eating behavior are based on self-reporting, such as 24-h recalls, food records (food diaries) and food frequency questionnaires. Self-reporting is prone to inaccuracies due to inaccurate and subjective recall and other biases. Recording behavior using nonobtrusive technology in daily life would overcome this. Here, we provide an up-to-date systematic overview encompassing all (close-to) publicly or commercially available technologies to automatically record eating behavior in real-life settings. A total of 1328 studies were screened and, after applying defined inclusion and exclusion criteria, 122 studies were included for in-depth evaluation. Technologies in these studies were categorized by what type of eating behavior they measure and which type of sensor technology they use. In general, we found that relatively simple sensors are often used. Depending on the purpose, these are mainly motion sensors, microphones, weight sensors and photo cameras. While several of these technologies are commercially available, there is still a lack of publicly available algorithms that are needed to process and interpret the resulting data. We argue that future work should focus on developing robust algorithms and validating these technologies in real-life settings. Combining technologies (e.g., prompting individuals for self-reports at sensed, opportune moments) is a promising route toward ecologically valid studies of eating behavior.

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Section: Resultsmentioning

confidence: 99%

Technology to Automatically Record Eating Behavior in Real Life: A Systematic Review

Hiraguchi,

Perone,

Toet

et al. 2023

Sensors

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“…Therefore, this type of decision tree method is used, as shown in Table 23. [62] 2011 X Lloret et al [49] 2012 X Sandholm et al [75] 2014 X Prapulla et al [69] 2015 X Panchal et al [67] 2015 X X Edward et al [19] 2017 X Shama et al [77] 2017 X Wu et al [89] 2017 X-Distance (Sharp GP2YOA41SKOF) Anand et al [6] 2018 HC-SR04T Hossain et al [34] 2018 HC-SR04T Barfeh et al [8] 2019 X Bayya [9] 2019 X Khan et al [41] 2019 X Shariff et al [78] 2019 X X Sharma et al [79] 2019 X Mallikarjun et al [52] 2020 X HC-SR04T Velasco et al [86] 2020 X Das et al [16] 2021 X X Jaipriya et al [37] 2021 X Krishnamoorthy et al [46] 2021 X Nejakar et al [63] 2022 X…”

Section: Household Replenishment Systems Analysismentioning

confidence: 99%

“…Several types of gas sensors were involved in some of the proposed systems, as shown in Table 5. [62] 2011 X Kaldeli et al [38] 2013 X Lloret et al [49] 2012 X Sandholm et al [75] 2012 LED Son et al [82] 2014 NFC Kale et al [39] 2015 X Kwon et al [47] 2016 Switch Esmaeili [5] 2017 X Hafidh et al [32] 2017 X Hossain et al [34] 2018 X Khan et al [41] 2019 Camera Sharma et al [79] 2019 X Mohammad et al [56] 2020 X Velasco et al [86] 2020 X Datey [17] 2021 X Krishnamoorthy et al [46] 2021 Servo motor Lee et al [48] 2021 X Sharma et al [80] 2021 X…”

Section: Gas Sensorsmentioning

confidence: 99%

“…Hossain et al [34] 2018 Raspberry Pi Nasir et al [61] 2018 Arduino Uno WeMos D1R2 WiFi Rezwan et al [71] 2018 Arduino Mega and NodeMCU (ESP8266) Zhongmin et al [93] 2018 Aduino Arm Contex -M3 and STM32F103 ARM Abdel-Basset et al [2] 2019 X Ahmed and Rajesh [4] 2019 Arduino Uno Barfeh et al [8] 2019 Raspberry Pi Bayya [9] 2019 Arduino Uno Khan et al [41] 2019 Raspberry Pi for Fridge and Node MCU for sensors Narayan et al [60] 2019 Arduino Uno R3 AT Mega 328 (IDE Coding) and NodeMCU (ESP8266) Ringe et al [72] 2019 Raspberry Pi Shariff et al [78] 2019 Renesas GR Peach with (RFID and GPS) built-in: but not utilized Sharma et al [79] 2019 X Abd Elminam et al [1] 2020 Arduino Mega Avinash et al [7] 2020 X Das et al [16] 2020 Arduino Uno Atmega Dong et al [18] 2020 Arduino Leonardo…”

Section: Articlementioning

confidence: 99%

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Using IoT and AI to replenish household food supplies: A systematic review

Almassar,

Khasawneh

2024

SCS

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Food wastage because of the lack or incompletion of a household replenishment system is an essential topic to be addressed. An appropriate utilization of Internet of Things (IoT) and Artificial Intelligence (AI) technologies with particular components is needed to design a smart household replenishment system to reduce food wastage. Therefore, this systematic review is dedicated to survey papers utilizing IoT and AI tools for perishable items storage compartments, as they are always full of items that need to be monitored. This study was conducted by following the PRISMA search strategy. It examined 70 papers in chronological order starting from 2000 when LG Electronics invented the first smart refrigerator, and research on technology involvement in food storage compartments increased. This comprehensive research aims to point out the approaches, contributions, used components and limitations of the reviewed papers to develop a unified framework for a household replenishment system. The analysis resulted in 43 approaches using IoT technology, 27 using AI, and recently the use of AIoT has been trending in the past two years. This systematic review provides future directions for researchers acquired from the limitations of the reviewed papers to enhance the household replenishment system by developing and adding required features in smart food storage compartments. Further investigation into smart home appliances would lead to extensive approaches like smart shops, industries, and eventually smart cities.

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“…Only a limited amount of prior work focused on using multi-or hyperspectral imaging in HCI. In comparison, the use of a limited amount of bands of the electromagnetic spectrum -for example using near-infrared for spectroscopy of sucrose contents drinks [15] -is much more common in HCI and UbiComp as such devices are cheaper, smaller and easier to integrate into end-user devices [15,16,13,22]. An example of using multi-spectral imaging is SpeCam presented by Yeo at al.…”

Section: Introductionmentioning

confidence: 99%

Museum Visitor Experiences Based on Hyperspectral Image Data

Löchtefeld,

Papachristos,

Deborah

2023

Lecture Notes in Computer Science

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Hyper-and multispectral imaging allows to collect data from specific wavelength ranges or across the electromagnetic spectrum, including frequencies that are imperceivable for humans. As non-invasive imaging techniques, it has been used in the field of art conservation and art history extensively in the past. In these areas application of hyperspectral imaging include for example conservation monitoring or pigment identification. In the context of museum exhibits, hyperspectral data of artworks offers a unique opportunity to enhance visitor experiences by providing new ways of engaging with artefacts, artworks, and cultural heritage. This paper presents design concepts for creating immersive and meaningful experiences using hyperspectral data. We used an expert led design workshop to explore the possibilities of museum experiences with such data, including considerations such as suited technologies, visitor types and visitor experience.

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SmrtFridge

Cited by 7 publications

References 13 publications

Technology to Automatically Record Eating Behavior in Real Life: A Systematic Review

Technology to Automatically Record Eating Behavior in Real Life: A Systematic Review

Using IoT and AI to replenish household food supplies: A systematic review

Museum Visitor Experiences Based on Hyperspectral Image Data

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