Prof. Xiaoying Li | Design | Best Researcher Award

Posted on by Global Sensor Awards

Prof. Xiaoying Li | Design | Best Researcher Award 

Prof. Xiaoying Li | Design | Hubei University of Technology | China

Prof. Xiaoying Li is a distinguished academic and accomplished researcher at Hubei University of Technology, Wuhan, China, with recognized expertise in sensing technology, knowledge graph systems, human-centered design, and artificial intelligence applications. Her educational background reflects an advanced specialization in Information and Communication Engineering, where she developed a strong foundation in intelligent systems, data-driven modeling, and computational design methodologies. Through her academic and professional journey, Prof. Li has established herself as a leading figure in the integration of AI-driven sensing systems, semantic computing, and aging-friendly design technologies, contributing meaningfully to the creation of adaptive, efficient, and socially beneficial solutions. Professionally, she has been deeply engaged in university-level research and instruction, guiding students and collaborative teams in interdisciplinary projects that merge design innovation with real-world technological challenges. Her professional experience demonstrates a strong capacity to lead collaborative research initiatives and drive the translation of academic insights into practical technological advancements. Prof. Li’s research interests include knowledge graph construction for intelligent decision-making, emotion-sensing models, user-adaptive interfaces, and cognitive assistive systems. Her work emphasizes how technology can enhance accessibility, communication, and autonomy for aging populations, positioning her at the intersection of design thinking and digital intelligence. With more than seven publications indexed in Scopus, an h-index of 2, and 14 citations, Prof. Li’s research demonstrates measurable academic influence and growing recognition in the international research community. Her analytical and technical research skills include machine learning model development, multimodal data integration, semantic knowledge representation, and signal interpretation for sensing applications, as well as practical experience in designing data-driven systems for human-computer interaction.

Professional Profile: Scopus

Selected Publications

  1. Li, X. (2025). Aging-friendly design research: Knowledge graph construction for elderly advantage applications. Applied Sciences, 15(20), 11287. [Cited by 1]

Mr. Haoyu Zhang | Design Awards | Best Researcher Award

Posted on by Global Sensor Awards

Mr. Haoyu Zhang | Design Awards | Best Researcher Award

Mr. Haoyu Zhang | Design Awards | Hubei University of Technology | China

Mr. Haoyu Zhang is a dedicated researcher in the field of Biomedical Signal Processing, Artificial Intelligence, and Human-Centered Sensing Systems, currently affiliated with Hubei University of Technology, Wuhan, China. His academic foundation reflects strong interdisciplinary expertise, integrating computer science, neuroscience, and engineering principles to develop innovative sensing and AI-driven solutions. Educated in information technology and biomedical engineering, Mr. Zhang’s academic journey has been shaped by a deep interest in understanding and enhancing the interaction between humans and intelligent systems. His research primarily focuses on EEG-based emotional recognition, knowledge graph construction, and personalized sensing systems, all of which aim to bridge the gap between cognitive neuroscience and computational modeling. Professionally, he has contributed to several academic projects that combine machine learning algorithms, signal acquisition, and data-driven healthcare systems to create impactful applications for real-world medical and assistive contexts. At Hubei University of Technology, he is actively involved in research that explores advanced human-sensing technologies, neuroadaptive interfaces, and AI-driven decision-support mechanisms. Mr. Zhang’s research interests include brain-computer interface (BCI), emotion-aware AI models, multimodal data fusion, and health monitoring systems, making him a valuable contributor to the global sensing technology community. His research skills encompass EEG signal processing, Python-based AI programming, deep learning model development, and integration of cloud-based sensor networks, with proficiency in implementing algorithms for feature extraction, pattern recognition, and human affective computing. Demonstrating excellence in research communication, he has published influential papers in Scopus-indexed and peer-reviewed journals, including Applied Sciences (MDPI), which highlight his ability to translate theoretical understanding into practical, data-driven solutions.

Professional Profile: ORCID

Selected Publications

  1. Zhang, H., & Li, X. (2025). A family emotional support system for MCS patients based on an EEG-to-visual translation mechanism: Design, implementation, and preliminary validation. Applied Sciences, 15(20), 11149. [Cited by 4]