Intelligent Data Collection Platforms
1. EasyTrack: A Scalable and General Purpose Platform for Reliable Data Collection in mHealth Studies
EasyTrack is a meticulously crafted software solution that addresses the increasing demand for large-scale longitudinal data collection in mHealth applications. It serves the needs of the academic and medical communities by offering a versatile platform capable of accommodating diverse participant populations and their associated sensor data. EasyTrack stands out for its general-purpose functionality, making it adaptable for a wide range of mHealth studies. Additionally, it incorporates a robust Data Quality (DQ) monitoring mechanism, featuring a user-friendly dashboard and automated problem-detection routines that promptly alert researchers to potential data discrepancies. Furthermore, EasyTrack ensures seamless interoperability with existing data collection platforms.
2. Campus Open Energy Platform
Within the context of the KENTECH microgrid roadmap, the Campus Open Energy Data Platform emerges as a transformative force. This roadmap paves the way for sustainable energy solutions, and the platform is poised to revolutionize the way we view, analyze, and act on energy data. This state-of-the-art platform not only collects and stores vast quantities of energy-related big data, but it also offers effective data processing applications encompassing time series analysis, load forecasting, fault diagnosis, and more. With its intuitive dashboard, users can visualize diverse datasets, enabling informed decision-making. Undergraduate students can directly engage with real-time power consumption patterns, enhancing their understanding of energy management, while graduate research is bolstered with crucial data for R&D in cutting-edge energy sectors. Further, the platform is a catalyst for innovation, offering budding tech entrepreneurs the tools they need for novel energy-related ventures. Through its comprehensive offerings, the platform not only advances educational and research endeavors but also propels the broader energy sector into the future.
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Publications
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MobiSys
- EasyTrack-Orchestrating Large-scale Mobile User Experimental Studies (poster)
- Kobiljon Toshnazarov, Hamza Baazizi, Nematjon Narziev, Youngtae Noh, Uichin Lee
- n Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services,
Research Participants
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- Integrated PhD Student
- Hyesung Lee
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Research Interest
- Personalized Stress Detection
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- Master’s Student
- Kondoro Malengo Alfred
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Research Interest
- Smart Healthcare system
- Engagement Detection
- Large Language Models (LLMs)
- 다음글 : 다음글이 없습니다.
- 이전글 : Digital Healthcare