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Wellness Protocol for Smart Homes [electronic resource] : An Integrated Framework for Ambient Assisted Living / by Hemant Ghayvat, Subhas Chandra Mukhopadhyay.

By: Ghayvat, Hemant [author.].
Contributor(s): Mukhopadhyay, Subhas Chandra [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Smart Sensors, Measurement and Instrumentation: 24Publisher: Cham : Springer International Publishing : Imprint: Springer, 2017Edition: 1st ed. 2017.Description: XI, 160 p. 115 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319520483.Subject(s): Electronics | Measurement | Measuring instruments | Data mining | Biomedical engineering | Environmental monitoring | Electronics and Microelectronics, Instrumentation | Measurement Science and Instrumentation | Data Mining and Knowledge Discovery | Biomedical Engineering and Bioengineering | Environmental MonitoringAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 621.381 Online resources: Click here to access online
Contents:
Introduction -- Literature Survey -- Wellness Protocol Development and Implementation -- Issues and Mitigation of WSNs-based Smart Building Systems -- Activity Detection and Wellness Pattern Generation -- Wellness Pattern Generation and Forecasting -- Conclusion and Future Works.
In: Springer Nature eBookSummary: This book focuses on the development of wellness protocols for smart home monitoring, aiming to forecast the wellness of individuals living in ambient assisted living (AAL) environments. It describes in detail the design and implementation of heterogeneous wireless sensors and networks as applied to data mining and machine learning, which the protocols are based on. Further, it shows how these sensor and actuator nodes are deployed in the home environment, generating real-time data on object usage and other movements inside the home, and therefore demonstrates that the protocols have proven to offer a reliable, efficient, flexible, and economical solution for smart home systems. Documenting the approach from sensor to decision making and information generation, the book addresses various issues concerning interference mitigation, errors, security and large data handling. As such, it offers a valuable resource for researchers, students and practitioners interested in interdisciplinary studies at the intersection of wireless sensing processing, radio communication, the Internet of Things and machine learning, and in how they can be applied to smart home monitoring and assisted living environments.
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Introduction -- Literature Survey -- Wellness Protocol Development and Implementation -- Issues and Mitigation of WSNs-based Smart Building Systems -- Activity Detection and Wellness Pattern Generation -- Wellness Pattern Generation and Forecasting -- Conclusion and Future Works.

This book focuses on the development of wellness protocols for smart home monitoring, aiming to forecast the wellness of individuals living in ambient assisted living (AAL) environments. It describes in detail the design and implementation of heterogeneous wireless sensors and networks as applied to data mining and machine learning, which the protocols are based on. Further, it shows how these sensor and actuator nodes are deployed in the home environment, generating real-time data on object usage and other movements inside the home, and therefore demonstrates that the protocols have proven to offer a reliable, efficient, flexible, and economical solution for smart home systems. Documenting the approach from sensor to decision making and information generation, the book addresses various issues concerning interference mitigation, errors, security and large data handling. As such, it offers a valuable resource for researchers, students and practitioners interested in interdisciplinary studies at the intersection of wireless sensing processing, radio communication, the Internet of Things and machine learning, and in how they can be applied to smart home monitoring and assisted living environments.

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