This study presents the development and deployment of a modular digital twin system designed to enhance sustainable facility management within a smart campus context. The system was implemented at the Faculty of Engineering, Chiang Mai University, and integrates 3D spatial modeling, real-time environmental and energy sensor data, and multiscale dashboard visualization. Grounded in stakeholder-driven requirements, the platform emphasizes energy management, which is the top priority among campus administrators and technicians. The development process followed a four-phase methodology: (1) stakeholder consultation and requirement analysis; (2) physical data acquisition and 3D model generation; (3) sensor deployment using IoT technologies with NB-IoT and LoRaWAN protocols; and (4) real-time data integration via Firebase and standardized APIs. A suite of dashboards was developed to support interactive monitoring across faculty, building, floor, and room levels. System testing with campus users demonstrated high usability, intuitive spatial navigation, and actionable insights for energy consumption analysis. Feedback indicated strong interest in features supporting data export and predictive analytics. The platform’s modular and hardware-agnostic architecture enables future extensions, including occupancy tracking, water monitoring, and automated control systems. Overall, the digital twin system offers a replicable and scalable model for data-driven facility management aligned with sustainability goals. Its real-time, multiscale capabilities contribute to operational transparency, resource optimization, and climate-responsive campus governance, setting the foundation for broader applications in smart cities and built environment innovation.

Existing Environmental, Social, and Governance (ESG) monitoring approaches rely primarily on static reports and dashboard-based interfaces, limiting real-time analysis and interactive exploration of sustainability data in complex built environments. In addition, current digital twin systems often lack integration with IoT-based sensing or depend on cloud-based rendering infrastructures, increasing deployment complexity and restricting accessibility. This study proposes a lightweight Web3D-based digital twin framework for real-time ESG monitoring in smart buildings. The system integrates an independently developed IoT sensor network with a browser-native 3D visualization platform, enabling real-time monitoring of ESG indicators—including electricity consumption—without requiring proprietary software or dedicated rendering hardware. ESG indicators are derived using a rule-based classification aligned with the WELL Building Standard v1. The framework was validated through a 12-month real-world deployment involving 60 IoT sensors. Results demonstrate stable performance, achieving 66 FPS rendering, 78 ms system latency, and 98% sensor data consistency based on cross-sensor agreement. The system also enabled timely detection of environmental anomalies, leading to measurable improvements in air quality and lighting conditions. Unlike prior digital twin systems, the proposed framework delivers a fully browser-native, lightweight architecture that integrates real-time IoT sensing, adaptive Web3D visualization, and structured ESG monitoring within a single deployable system. This approach provides a practical solution with potential for broader deployment in real-time sustainability monitoring for smart buildings.

As stroke survivors often manifest not only physical dysfunction but also cognitive impairment, interventions that address both physical and cognitive aspects may be beneficial for individuals with chronic stroke. However, a randomized clinical trial (RCT) examining the effects of physical–cognitive telerehabilitation exergaming across physical and cognitive domains in chronic stroke survivors remains scarce. Therefore, this study aims to investigate the effectiveness of the web-based physical–cognitive telerehabilitation exergaming on the physical and cognitive performance of chronic stroke survivors as compared to control. A two-arm RCT with a blinded assessor will be conducted. Fifty-two participants will be randomly assigned to either the intervention or the control group. Participants in the intervention group will receive a 12-week training program, whereas those in the control group will continue with usual care. Motor function, gait speed, balance, fall risk, muscle power, weight distribution, body composition, global cognition, executive function, attention, memory, as well as enjoyment will be assessed. Existing evidence has established the health benefits of physical–cognitive exergaming. It is expected that a physical–cognitive exergaming program may further exhibit positive effects on both physical function and cognition beyond the results of usual care. In addition, incorporating physical–cognitive training into a web-based telerehabilitation program may improve adherence and provide a feasible and enjoyable rehabilitation option for individuals with chronic stroke.