FEDTWIN – trustworthy digital twin as a service for visually impaired.
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| Title: | FEDTWIN – trustworthy digital twin as a service for visually impaired. |
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| Authors: | Natarajan, Hema Priya1 (AUTHOR) nhp.it@psgtech.ac.in, Annadurai, Pushparaj1 (AUTHOR) |
| Source: | Automatika: Journal for Control, Measurement, Electronics, Computing & Communications. Dec2025, Vol. 66 Issue 4, p923-938. 16p. |
| Subjects: | Digital twin, People with visual disabilities, Cloud computing, Industry 4.0, Mobility of people with disabilities, Federated learning, Health care industry |
| Abstract: | The Industrial Internet of Things (IIoT) revolutionize industries such as manufacturing, logistics, energy, and healthcare by merging smart sensors and devices with sophisticated network connectivity and advanced data analysis. Digital Twin As a Service (DTaaS) for Internet of Healthcare Things (IoHT) in the healthcare industry opens exciting opportunities to create virtual replicas of real healthcare systems and assets. Digital twins relying on cloud platforms, such as Amazon Web Services, Microsoft Azure, and Google Cloud, provide several vital capabilities, including data informed decision making, personalized patient simulation, up to 30% decrease in equipment downtime with predictive maintenance, and operational efficiency of more than 25% through real-time remote monitoring. This paper proposes an all encompassing methodology towards the development and deployment of compositional digital twins utilizing services applied towards assisting the visually challenged with a smart stick. Federated learning has been proposed as one potential approach that could help in preserving the privacy of clients, particularly concerning the protection of patient's confidential information. One of the possible healthcare scenario that demonstrates how digital twin technology guiding visually impaired individuals, with a possible enhancement in the success rate of mobility by 40%. [ABSTRACT FROM AUTHOR] |
| Copyright of Automatika: Journal for Control, Measurement, Electronics, Computing & Communications is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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