Prof. Shing-Hong Liu | Biomedical Award | Best Researcher Award

Posted on by Global Sensor Awards

Prof. Shing-Hong Liu | Biomedical Award | Best Researcher Award 

Prof. Shing-Hong Liu, Chaoyang University of Technology, Taiwan

Shing-Hong Liu is an esteemed academic and researcher in the field of biomedical engineering and computer science. He obtained his B.S. degree in Electronic Engineering from Feng-Jia University, Taiwan, in 1990, followed by an M.S. degree in Biomedical Engineering from National Cheng-Kung University in 1992. In 2002, he earned his Ph.D. from the Department of Electrical and Control Engineering at National Chiao-Tung University, Taiwan. Since August 1994, Dr. Liu has been actively involved in academia, initially as a Lecturer in the Department of Biomedical Engineering at Yuanpei University, Taiwan. He progressed to become an Associate Professor from 2002 to 2008. Currently, he holds the position of Distinguished Professor in the Department of Computer Science and Information Engineering at Chaoyang University of Technology. Dr. Liu’s research focuses on biomedical signal processing, artificial intelligence applications in mobile health (mHealth), and the design of biomedical instruments. He has been recognized for his contributions, being named one of the World’s Top 2% Scientists in 2020. His research projects have received substantial funding, totaling NT$36,329,914, and he has authored 59 papers in SCI journals.

 

Professional Profile:

ORCID

 

Education:

  • B.S. in Electronic Engineering
    • Feng-Jia University, Taizhong, Taiwan, R.O.C.
    • Year of Completion: 1990
  • M.S. in Biomedical Engineering
    • National Cheng-Kung University, Tainan, Taiwan, R.O.C.
    • Year of Completion: 1992
  • Ph.D. in Electrical and Control Engineering
    • National Chiao-Tung University, Hsinchu, Taiwan, R.O.C.
    • Year of Completion: 2002

Work Experience:

  • Lecturer
    • Department of Biomedical Engineering, Yuanpei University, Hsinchu, Taiwan, R.O.C.
    • August 1994 – 2002
  • Associate Professor
    • Department of Biomedical Engineering, Yuanpei University, Hsinchu, Taiwan, R.O.C.
    • 2002 – 2008
  • Distinguished Professor
    • Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taiwan, R.O.C.
    • 2020 – Present

Achievements:

Shing-Hong Liu has been recognized as one of the World’s Top 2% Scientists in 2020. His research interests focus on biomedical signal processing, artificial intelligence for mHealth applications, and the design of biomedical instruments. He has successfully led projects with a total budget of NT 36,329,914 and has published 59 papers in SCI journals.

Publication top Notes:

Predicting Gait Parameters of Leg Movement with sEMG and Accelerometer Using CatBoost Machine Learning

Human Activity Recognition Based on Deep Learning and Micro-Doppler Radar Data

Estimation of Gait Parameters for Adults with Surface Electromyogram Based on Machine Learning Models

A Wearable Assistant Device for the Hearing Impaired to Recognize Emergency Vehicle Sirens with Edge Computing

A Wearable Assistant Device for Hearing Impaired to Recognize Emergency Vehicle Sirens with Edge Computing

Best Medical Sensing Technology

Posted on by Global Sensor Awards

Introduction Best Medical Sensing Technology

The Best Medical Sensing Technology Award recognizes groundbreaking innovations that revolutionize medical sensing, enabling accurate, non-invasive, and real-time monitoring of patient health parameters. This prestigious award celebrates advancements that have the potential to significantly improve healthcare outcomes globally.

About the Award:
The Best Medical Sensing Technology Award is open to individuals, research teams, and companies worldwide that have developed cutting-edge technologies in medical sensing. Applicants must demonstrate exceptional creativity, innovation, and impact in the field of medical sensing.

Eligibility:
There are no age limits for applicants. The award is open to researchers, engineers, inventors, and entrepreneurs who have made significant contributions to the field of medical sensing. Applicants must have a proven track record of excellence in their respective fields.

Qualifications:
Applicants must have developed a medical sensing technology that demonstrates exceptional innovation, effectiveness, and potential for improving healthcare outcomes. The technology should be supported by strong scientific evidence and have the potential for widespread adoption in the medical field.

Publications:
Applicants are encouraged to submit any relevant publications, research papers, or patents that support their application. These publications should demonstrate the novelty and impact of the medical sensing technology.

Evaluation Criteria:
Applications will be evaluated based on the following criteria:

  • Innovation and creativity of the medical sensing technology
  • Impact on healthcare outcomes
  • Scientific rigor and validity of the technology
  • Potential for widespread adoption
  • Overall quality and clarity of the application

Submission Guidelines:
Applicants must submit a detailed description of their medical sensing technology, along with any supporting documents, such as publications, patents, or videos. The submission should clearly demonstrate the innovation, effectiveness, and potential impact of the technology.

Recognition:
The winner of the Best Medical Sensing Technology Award will receive a prestigious award certificate, recognition on our website and social media channels, and an opportunity to present their technology at a major medical conference.

Community Impact:
The award-winning medical sensing technology should demonstrate a positive impact on the healthcare community, improving patient outcomes, reducing healthcare costs, or advancing medical research.

Biography:
Applicants should provide a brief biography highlighting their relevant experience and achievements in the field of medical sensing.

Abstract:
A concise abstract summarizing the key features and benefits of the medical sensing technology should be included in the application.

Supporting Files:
Applicants may include any additional supporting files, such as videos, images, or technical specifications, to enhance their application.