Ambient Intelligence At-Home Laboratory for Human Everyday Life

Meigal, Alexander; Korzun, Dmitry; Gerasimova-Meigal, Liudmila; Borodin, Alexander V.; Zavyalova, Yulia V.

doi:10.4018/ijertcs.2019040108

Cited by 18 publications

(3 citation statements)

References 34 publications

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“…Where strict medical decisions and critical medical support are not mandatory, the digital services may provide attention points and optional recommendations for personal use. We believe this type of smart human sensors will soon diffuse from the restricted medical lab setting toward the wide market of smart consumer electronics and digital services [ 100 ].…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Sensors for Diagnosis of COVID-19 Disease Using Routine Blood Values for Internet of Things Application

Velichko

Huyut

Belyaev

et al. 2022

Sensors

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Healthcare digitalization requires effective applications of human sensors, when various parameters of the human body are instantly monitored in everyday life due to the Internet of Things (IoT). In particular, machine learning (ML) sensors for the prompt diagnosis of COVID-19 are an important option for IoT application in healthcare and ambient assisted living (AAL). Determining a COVID-19 infected status with various diagnostic tests and imaging results is costly and time-consuming. This study provides a fast, reliable and cost-effective alternative tool for the diagnosis of COVID-19 based on the routine blood values (RBVs) measured at admission. The dataset of the study consists of a total of 5296 patients with the same number of negative and positive COVID-19 test results and 51 routine blood values. In this study, 13 popular classifier machine learning models and the LogNNet neural network model were exanimated. The most successful classifier model in terms of time and accuracy in the detection of the disease was the histogram-based gradient boosting (HGB) (accuracy: 100%, time: 6.39 sec). The HGB classifier identified the 11 most important features (LDL, cholesterol, HDL-C, MCHC, triglyceride, amylase, UA, LDH, CK-MB, ALP and MCH) to detect the disease with 100% accuracy. In addition, the importance of single, double and triple combinations of these features in the diagnosis of the disease was discussed. We propose to use these 11 features and their binary combinations as important biomarkers for ML sensors in the diagnosis of the disease, supporting edge computing on Arduino and cloud IoT service.

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Section: Discussionmentioning

confidence: 99%

Machine Learning Sensors for Diagnosis of COVID-19 Disease Using Routine Blood Values for Internet of Things Application

Velichko

Huyut

Belyaev

et al. 2022

Sensors

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“…This adoption shows important and adequate results for improving the quality of healthcare services in smart environments (Meigal, Korzun, Gerasimova‐Meigal, Borodin, & Zavyalova, 2019; Turcu & Turcu, 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

A semantic‐enabled and context‐aware monitoring system for the internet of medical things

et al. 2020

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The emergence of the Internet of Things (IoT) in the medical field has led to the massive deployment of a myriad of medical connected objects (MCOs). These MCOs are being developed and implemented for remote healthcare monitoring purposes including elderly patients with chronic diseases, pregnant women, and patients with disabilities. Accordingly, different associated challenges are emerging and include the heterogeneity of the gathered health data from these MCOs with ever‐changing contexts. These contexts are relative to the continuous change of constraints and requirements of the MCOs deployment (time, location, state). Other contexts are related to the patient (medical record, state, age, sex, etc.) that should be taken into account to ensure a more precise and appropriate treatment of the patient. These challenges are difficult to address due to the absence of a reference model for describing the health data and their sources and linking these data with their contexts. This article addresses this problem and introduces a semantic‐based context‐aware system (IoT Medicare system) for patient monitoring with MCOs. This system is based on a core domain ontology (HealthIoT‐O), that is, designed to describe the semantic of heterogeneous MCOs and their data. Moreover, an efficient interpretation and management of this knowledge in diverse contexts are ensured through SWRL rules such as the verification of the proper functioning of the MCOs and the analysis of the health data for diagnosis and treatment purposes. A case study of gestational diabetes disease management is proposed to evaluate the effectiveness of the implemented IoT Medicare system. An evaluation phase is provided and focuses on the quality of the elaborated semantic model and the performance of the system.

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“…Intelligent IoT devices should be able to process the incoming information without sending it to the cloud. Smart objects equipped with their own AI capabilities spend significantly less time on the analysis of incoming data and development of a final solution, creating new possibilities, for example, in the development of AmI in the medical industry [9], predicting the behavior of mechanisms [10], local semantic processing of video data [11] and "smart" services in e-tourism [12]. This approach facilitates the development of the concepts of smart spaces and fog computing, when devices detect each other, for example, using wireless technologies [13]; redistribute computational tasks; and optimize the distribution of responses [14,15].…”

Section: Introductionmentioning

confidence: 99%

Neural Network for Low-Memory IoT Devices and MNIST Image Recognition Using Kernels Based on Logistic Map

Velichko

2020

Electronics

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This study presents a neural network which uses filters based on logistic mapping (LogNNet). LogNNet has a feedforward network structure, but possesses the properties of reservoir neural networks. The input weight matrix, set by a recurrent logistic mapping, forms the kernels that transform the input space to the higher-dimensional feature space. The most effective recognition of a handwritten digit from MNIST-10 occurs under chaotic behavior of the logistic map. The correlation of classification accuracy with the value of the Lyapunov exponent was obtained. An advantage of LogNNet implementation on IoT devices is the significant savings in memory used. At the same time, LogNNet has a simple algorithm and performance indicators comparable to those of the best resource-efficient algorithms available at the moment. The presented network architecture uses an array of weights with a total memory size from 1 to 29 kB and achieves a classification accuracy of 80.3–96.3%. Memory is saved due to the processor, which sequentially calculates the required weight coefficients during the network operation using the analytical equation of the logistic mapping. The proposed neural network can be used in implementations of artificial intelligence based on constrained devices with limited memory, which are integral blocks for creating ambient intelligence in modern IoT environments. From a research perspective, LogNNet can contribute to the understanding of the fundamental issues of the influence of chaos on the behavior of reservoir-type neural networks.

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Ambient Intelligence At-Home Laboratory for Human Everyday Life

Cited by 18 publications

References 34 publications

Machine Learning Sensors for Diagnosis of COVID-19 Disease Using Routine Blood Values for Internet of Things Application

Machine Learning Sensors for Diagnosis of COVID-19 Disease Using Routine Blood Values for Internet of Things Application

A semantic‐enabled and context‐aware monitoring system for the internet of medical things

Neural Network for Low-Memory IoT Devices and MNIST Image Recognition Using Kernels Based on Logistic Map

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