Home Elsevier - Health Sciences Division Advances in Digital Twin Computing and Sensor Networks- Home Digital Twins

Home Digital Twins explores how digital twin technologies can serve as a bridge between the real home and the virtual home. It explains how IoT devices, wearable sensors, artificial intelligence (AI), and machine learning (ML) can aid in the diagnosis, prediction, and prevention of abnormal/critical/extreme problems in real home environments, while also showing how digital twin computing can support the management of home energy consumption to optimize everyday living. The book also considers the security and ethical implications of introducing digital twin technology in the home to ensure the technology benefit the lives of the home's inhabitants. The introduction of sensor networks into the home environment allows automation into the home, utilizing internet of things (IoT) devices to program, for example, door opening, room luminosity, room temperature, and humidity. Digital Twin technology extends the capabilities of the smart home, collecting and analyzing data from the Activities of Daily Living (ADLs) and applying Human Activity Recognition (HAR) algorithms to provide adaptive services, including minimizing power consumption, improving healthcare, and enhancing overall well-being. However, there are many challenges when applying these algorithms to the smart home due to the variability and sparsity of sensor data, which can lead to a mismatch between the real home and the digital home.