IoT Sensor Data Integration in Healthcare using Semantics and Machine Learning Approaches

Balakrishna, Sivadi; Thirumaran, M.; Solanki, Vijender Kumar

doi:10.1007/978-3-030-23983-1_11

Cited by 30 publications

(13 citation statements)

References 11 publications

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“…This not only helps in timely treatment of the patients but also improves the responsiveness and accuracy of the underlying application [ 74 , 75 ]. Moreover, the current medicines taken by the patient are monitored and the risk of new medication is evaluated in terms of any allergic reaction [ 66 , 76 ]. As a result, not only the time is conserved but monetary value remains in place too.…”

Section: A Taxonomy Of Machine Learning Techniques For Big Data Analymentioning

confidence: 99%

A Comprehensive Survey on Machine Learning-Based Big Data Analytics for IoT-Enabled Smart Healthcare System

et al. 2021

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Section: A Taxonomy Of Machine Learning Techniques For Big Data Analymentioning

confidence: 99%

A Comprehensive Survey on Machine Learning-Based Big Data Analytics for IoT-Enabled Smart Healthcare System

et al. 2021

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“…Convolutional neural networks (CNN) consist of a network layer that is used for convolution operation applied to two-dimensional or one-dimensional sensor data [62]. The convolution operator is used to learn the local patterns from the data.…”

Section: Discussionmentioning

confidence: 99%

An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques

Krishnamurthi

Kumar

Gopinathan

et al. 2020

Sensors

194

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In the recent era of the Internet of Things, the dominant role of sensors and the Internet provides a solution to a wide variety of real-life problems. Such applications include smart city, smart healthcare systems, smart building, smart transport and smart environment. However, the real-time IoT sensor data include several challenges, such as a deluge of unclean sensor data and a high resource-consumption cost. As such, this paper addresses how to process IoT sensor data, fusion with other data sources, and analyses to produce knowledgeable insight into hidden data patterns for rapid decision-making. This paper addresses the data processing techniques such as data denoising, data outlier detection, missing data imputation and data aggregation. Further, it elaborates on the necessity of data fusion and various data fusion methods such as direct fusion, associated feature extraction, and identity declaration data fusion. This paper also aims to address data analysis integration with emerging technologies, such as cloud computing, fog computing and edge computing, towards various challenges in IoT sensor network and sensor data analysis. In summary, this paper is the first of its kind to present a complete overview of IoT sensor data processing, fusion and analysis techniques.

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“…It is indicatively stressed that the IoT sensor data is generated from various heterogeneous devices, communication protocols, and data formats that are enormous in nature. Hence, learning approaches, data acquisition, semantic annotation, resources data extraction, semantic reasoning, and clustering can address the problem of sensor data integration and analysis in IoT healthcare data [50]. In a similar study it was argued that modelling features of object tracking on lightweight computing devices, involves limited computing capacity and memory space.…”

Section: Discussionmentioning

confidence: 99%

Internet of Things (IoT)-Enabled Elderly Fall Verification, Exploiting Temporal Inference Models in Smart Homes

Kyriakopoulos

Ntanos

Anagnostopoulos

et al. 2020

IJERPH

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Everyday life of the elderly and impaired population living in smart homes is challenging because of possible accidents that may occur due to daily activities. In such activities, persons often lean over (to reach something) and, if they not cautious, are prone to falling. To identify fall incidents, which could stochastically cause serious injuries or even death, we propose specific temporal inference models; namely, CM-I and CM-II. These models can infer a fall incident based on classification methods by exploiting wearable Internet of Things (IoT) altimeter sensors adopted by seniors. We analyzed real and synthetic data of fall and lean over incidents to test the proposed models. The results are promising for incorporating such inference models to assist healthcare for fall verification of seniors in smart homes. Specifically, the CM-II model achieved a prediction accuracy of 0.98, which is the highest accuracy when compared to other models in the literature under the McNemar’s test criterion. These models could be incorporated in wearable IoT devices to provide early warning and prediction of fall incidents to clinical doctors.

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IoT Sensor Data Integration in Healthcare using Semantics and Machine Learning Approaches

Cited by 30 publications

References 11 publications

A Comprehensive Survey on Machine Learning-Based Big Data Analytics for IoT-Enabled Smart Healthcare System

A Comprehensive Survey on Machine Learning-Based Big Data Analytics for IoT-Enabled Smart Healthcare System

An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques

Internet of Things (IoT)-Enabled Elderly Fall Verification, Exploiting Temporal Inference Models in Smart Homes

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