Smart-Log: A Deep-Learning Based Automated Nutrition Monitoring System in the IoT

Sundaravadivel, Prabha; Kesavan, Kavya; Kesavan, Lokeshwar; Mohanty, Saraju P.; Kougianos, Elias

doi:10.1109/tce.2018.2867802

Cited by 117 publications

(53 citation statements)

References 30 publications

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“…Analyzing the papers in Table S3, it can be observed that, according to the authors of these papers, all of the studies focused on smart homes. The authors of these scientific articles made use in their analyses of different types of sensors, including: biomedical sensors [11]; ambient data sensors [11,34,68]; acoustic sensor networks [67]; WiFi-enabled sensors [36]; Passive Infrared (PIR) sensors [30,34]; binary sensors [31,69]; and motion sensors [30,70].…”

Section: Classificationmentioning

confidence: 99%

“…With respect to the reasons for using the Naïve Bayes method with sensor equipment in smart buildings, one can observe that the recognition of human activity was the main subject of the identified papers summarized in Table S3, being addressed in papers [11,30,31,34,[68][69][70]. Meanwhile, several of the above-mentioned scientific papers that use the Naïve Bayes integrated with sensor devices in Smart Buildings also addressed issues regarding assisted living [11,30,31,34,36]. Other reasons for applying the Naïve Bayes method with sensors in smart buildings include obtaining accurate information regarding the positions of surrounding objects, an aspect especially useful for autonomous systems and smart devices [67] or in developing an Internet of Things (IoT)-based fully automated nutrition monitoring system [36].…”

Section: Classificationmentioning

confidence: 99%

“…Meanwhile, several of the above-mentioned scientific papers that use the Naïve Bayes integrated with sensor devices in Smart Buildings also addressed issues regarding assisted living [11,30,31,34,36]. Other reasons for applying the Naïve Bayes method with sensors in smart buildings include obtaining accurate information regarding the positions of surrounding objects, an aspect especially useful for autonomous systems and smart devices [67] or in developing an Internet of Things (IoT)-based fully automated nutrition monitoring system [36].…”

Section: Classificationmentioning

confidence: 99%

“…In [68], a comparison of the supervised learning models was presented: Naïve Bayes (NB), C4.5 Decision Tree, Logistic Regression, K-Nearest Neighbor, and Random Forest were used in order to detect and estimate occupancy in smart homes. In [36], the authors developed a hybrid approach by combining Bayesian algorithms and a 5-layer Perceptron Neural Network method for diet monitoring purposes; the authors of [34] used the Bayes filter algorithm to locate people. In [30], the authors made use of learning classification algorithms, including Naïve Bayes (NB), Support Vector Machine (SVM) and Random Forest (RF).…”

Section: Classificationmentioning

confidence: 99%

“…There is an increasing interest in the scientific literature in studies related to these topics; for example, there have been papers focusing on smart buildings [1][2][3][4][5], smart homes [6][7][8][9][10], smart hospitals [11], smart commercial buildings [12,13], sensor devices [9,[14][15][16][17], supervised machine learning models for classification purposes [1,11,[16][17][18] or for regression purposes [19][20][21][22][23], unsupervised machine learning models for clustering purposes [24][25][26], deep learning techniques [18,27,28], human activity recognition and classification with a view to assisted living [15,[29][30][31][32][33][34][35], Internet of Things (IoT) [21,[36][37][38]…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A Review of the Recent Developments in Integrating Machine Learning Models with Sensor Devices in the Smart Buildings Sector with a View to Attaining Enhanced Sensing, Energy Efficiency, and Optimal Building Management

et al. 2019

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Lately, many scientists have focused their research on subjects like smart buildings, sensor devices, virtual sensing, buildings management, Internet of Things (IoT), artificial intelligence in the smart buildings sector, improving life quality within smart homes, assessing the occupancy status information, detecting human behavior with a view to assisted living, maintaining environmental health, and preserving natural resources. The main purpose of our review consists of surveying the current state of the art regarding the recent developments in integrating supervised and unsupervised machine learning models with sensor devices in the smart building sector with a view to attaining enhanced sensing, energy efficiency and optimal building management. We have devised the research methodology with a view to identifying, filtering, categorizing, and analyzing the most important and relevant scientific articles regarding the targeted topic. To this end, we have used reliable sources of scientific information, namely the Elsevier Scopus and the Clarivate Analytics Web of Science international databases, in order to assess the interest regarding the above-mentioned topic within the scientific literature. After processing the obtained papers, we finally obtained, on the basis of our devised methodology, a reliable, eloquent and representative pool of 146 papers scientific works that would be useful for developing our survey. Our approach provides a useful up-to-date overview for researchers from different fields, which can be helpful when submitting project proposals or when studying complex topics such those reviewed in this paper. Meanwhile, the current study offers scientists the possibility of identifying future research directions that have not yet been addressed in the scientific literature or improving the existing approaches based on the body of knowledge. Moreover, the conducted review creates the premises for identifying in the scientific literature the main purposes for integrating Machine Learning techniques with sensing devices in smart environments, as well as purposes that have not been investigated yet.

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