Remote Care and Collaboration for Empty Nest Family: Smart Home, Digital Twin and Mixed Reality

Hu, Guoqing; Wang, Yu'ao; Mao, Mengyuan; Zhao, Yi

doi:10.1109/icvr55215.2022.9847779

Cited by 5 publications

(2 citation statements)

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“…For example, the "Smart Home Virtual Tour" created by Kučera and Haffner et al [26] uses multiple sensors connected to an Arduino microcontroller to enable virtual tours and events to react to each other. Hu and Mao et al [17] proposed a digital twin and mixed reality based remote collaboration system for smart homes (RCSSH) in which cameras, environmental sensors (for temperature, harmful gas, smoke/fire), weight sensors, audio sensors (by headphones), and infrared sensors are deployed. Remote assistance is provided to users by collecting data from sensors in a mixed reality environment.…”

Section: Technology Mediationmentioning

confidence: 99%

Multi-channel Sensor Network Construction, Data Fusion and Challenges for Smart Home

Zhang,

Ananda,

et al. 2023

Proceedings of the Eleventh International Symposium of Chinese CHI

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Both sensor networks and data fusion are essential foundations for developing the smart home Internet of Things (IoT) and related fields. We proposed a multi-channel sensor network construction method involving hardware, acquisition, and synchronization in the smart home environment and a smart home data fusion method (SHDFM) for multi-modal data (position, gait, voice, pose, facial expression, temperature, and humidity) generated in the smart home environment to address the configuration of a multi-channel sensor network, improve the quality and efficiency of various human activities and environmental data collection, and reduce the difficulty of multi-modal data fusion in the smart home. SHDFM contains 5 levels, with inputs and outputs as criteria to provide recommendations for multi-modal data fusion strategies in the smart home. We built a real experimental environment using the proposed method in this paper. To validate our method, we created a real experimental environment -a physical setup in a home-like scenario where the multi-channel sensor network and data fusion techniques were deployed and evaluated. The acceptance and testing results show that the proposed construction and data fusion methods can be applied to the examples with high robustness, replicability, and scalability. Besides, we discuss how smart homes with multi-channel sensor networks can support digital twins.

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Section: Technology Mediationmentioning

confidence: 99%

Multi-channel Sensor Network Construction, Data Fusion and Challenges for Smart Home

Zhang,

Ananda,

et al. 2023

Proceedings of the Eleventh International Symposium of Chinese CHI

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“…She can use deictic gestures to indicate key nodes and links during her presentation by pointing, with the physical model providing a shared context between Alice and her remote audience. Alice can also use the desktop visualization as a digital twin of the network [40] and use the sensing network to interact with the twin. As she touches different nodes, the sensing network allows the twin to highlight those nodes in red and display additional information about the corresponding student in the network.…”

Section: Explanation and Communicationmentioning

confidence: 99%

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Bae

Yang

Gyory

et al. 2021

Interaction Design and Children

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Interaction is critical for data analysis and sensemaking. However, designing interactive physicalizations is challenging as it requires cross-disciplinary knowledge in visualization, fabrication, and electronics. Interactive physicalizations are typically produced in an unstructured manner, resulting in unique solutions for a specific dataset, problem, or interaction that cannot be easily extended or adapted to new scenarios or future physicalizations. To mitigate these challenges, we introduce a computational design pipeline to 3D print network physicalizations with integrated sensing capabilities. Networks are ubiquitous, yet their complex geometry also requires significant engineering considerations to provide intuitive, effective interactions for exploration. Using our pipeline, designers can readily produce network physicalizations supporting selection-the most critical atomic operation for interaction-by touch through capacitive sensing and computational inference. Our computational design pipeline introduces a new design paradigm by concurrently considering the form and interactivity of a physicalization into one cohesive fabrication workflow. We evaluate our approach using (i) computational evaluations, (ii) three usage scenarios focusing on general visualization tasks, and (iii) expert interviews. The design paradigm introduced by our pipeline can lower barriers to physicalization research, creation, and adoption.

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Survey on Smart Home Automation Systems: Tools, Technology, Architecture and Communication Interfaces

Sharma

Reddy

2022

2022 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)

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Remote Care and Collaboration for Empty Nest Family: Smart Home, Digital Twin and Mixed Reality

Cited by 5 publications

References 0 publications

Multi-channel Sensor Network Construction, Data Fusion and Challenges for Smart Home

Multi-channel Sensor Network Construction, Data Fusion and Challenges for Smart Home

Touching Information with DIY Paper Charts & AR Markers

Survey on Smart Home Automation Systems: Tools, Technology, Architecture and Communication Interfaces

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