Digital Twin Empowered Wireless Healthcare Monitoring for Smart Home

Chen, Junxin; Wang, Wei; Fang, Bo; Liu, Yu; Yu, Keping; Leung, Victor C. M.; Hu, Xiping

doi:10.1109/jsac.2023.3310097

Cited by 29 publications

(4 citation statements)

References 68 publications

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“…The visible light and IR are listed as independent sections in the following parts. [9], [52]. Qian et al [53] proposed Widar2.0, a system that can achieve passive human positioning and tracking on a single WiFi commercial device.…”

Section: B Contactless Sensing Technologiesmentioning

confidence: 99%

Review of the Open Data Sets for Contactless Sensing

Liang,

Chen,

et al. 2024

IEEE Internet Things J.

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Recent years have witnessed the increasing popularity and dramatic progress of contactless sensing technologies, which are able to conduct remote signal acquisition without body contact. Both the physical signs and the physiological parameters can be acquired with contactless sensing. This paper introduces popular contactless sensing technologies, explores their application scenarios, and delves into the underlying theoretical principles. It comprehensively reviews the open datasets released in this field, encompassing collection scenarios, sample counts, data formats, and volunteer information. The performance baseline, typical work, and accessible links are also furnished. In addition, it includes discussions on the primary challenges and potential solutions in the context of contactless sensing with open datasets. Finally, suggestions for establishing a high-quality dataset are also given to the community.

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Section: B Contactless Sensing Technologiesmentioning

confidence: 99%

Review of the Open Data Sets for Contactless Sensing

Liang,

Chen,

et al. 2024

IEEE Internet Things J.

Self Cite

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“…The Internet of Things can be combined with Machine Learning algorithms to obtain a so-called Digital Twin, a powerful tool for modeling and optimizing complex systems in various domains, such as manufacturing, healthcare, and energy saving [21][22][23]. Digital Twins can effectively be used in buildings to enable the more efficient and intelligent management of energy, security, and comfort [24][25][26] or to improve the healthcare of older people through monitoring and prediction [27,28].…”

Section: Introductionmentioning

confidence: 99%

Enabling End-User Development in Smart Homes: A Machine Learning-Powered Digital Twin for Energy Efficient Management

Cotti,

Guizzardi,

Barricelli

et al. 2024

Future Internet

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End-User Development has been proposed over the years to allow end users to control and manage their Internet of Things-based environments, such as smart homes. With End-User Development, end users are able to create trigger-action rules or routines to tailor the behavior of their smart homes. However, the scientific research proposed to date does not encompass methods that evaluate the suitability of user-created routines in terms of energy consumption. This paper proposes using Machine Learning to build a Digital Twin of a smart home that can predict the energy consumption of smart appliances. The Digital Twin will allow end users to simulate possible scenarios related to the creation of routines. Simulations will be used to assess the effects of the activation of appliances involved in the routines under creation and possibly modify them to save energy consumption according to the Digital Twin’s suggestions.

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“…It describes the relationship between the projection of an object on a two-dimensional image plane and the structure of the object itself, which can help us understand and predict the projection deformation of the object in the image [27]. By utilizing the Kruppa equation, we can recover more accurate three-dimensional information from images, thereby creating more realistic virtual environments and visualization effects [28].…”

Section: Introductionmentioning

confidence: 99%

A Kruppa Equation-Based Virtual Reconstruction Method for 3D Images Under Visual Communication Scenes

Yang,

Yang

2023

IEEE Access

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Visual communication (VC) manages to demonstrate optimal perception effect through reasonable element planning. It has been a more prevalent tendency to introduce 3D image reconstruction into VC activities. Currently, there still lacks reliable solution for this purpose under VC scenes. To deal with this issue, we propose a Kruppa equation-based virtual reconstruction method for 3D images under VC scenes. Firstly, the feature descriptor procedure in scale-invariant feature transform (SIFT) algorithm is introduced into image matching. Then, Harris corner extraction (HCE) algorithm is utilized for selection of feature points. And according to HCE algorithm, we develop a specific direction feature description method. In the proposal, main camera points are calibrated separately, which not only makes the calibration accuracy of the main points higher, but also reduces the unknown parameters of Kruppa equation. Finally, we conduct some experiments to verify efficiency of the proposal. The obtained results show that calibration accuracy of the proposal is 12% higher than other comparison methods, and better virtual reconstruction results can be achieved.

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Digital Twin Empowered Wireless Healthcare Monitoring for Smart Home

Cited by 29 publications

References 68 publications

Review of the Open Data Sets for Contactless Sensing

Review of the Open Data Sets for Contactless Sensing

Enabling End-User Development in Smart Homes: A Machine Learning-Powered Digital Twin for Energy Efficient Management

A Kruppa Equation-Based Virtual Reconstruction Method for 3D Images Under Visual Communication Scenes

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