Yaning Li | Neurological | Best Researcher Award

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Ms. Yaning Li | Neurological | Best Researcher Award

Yaning Li at Shandong University of Traditional Chinese Medicine, China.

Li Yanning is a dedicated researcher specializing in rehabilitation medicine, with a strong background in neurological and musculoskeletal rehabilitation. She has actively contributed to clinical and academic research, focusing on innovative treatment approaches such as non-invasive brain stimulation, scoliosis-specific exercises, and rehabilitation technologies. With extensive experience in clinical practice and scientific research, she is committed to improving rehabilitation outcomes for patients through data-driven methodologies.

Professional Profile:

Scopus

Education Background

Li Yanning obtained her Master’s degree in Rehabilitation Medicine & Physiotherapy (2022-2025) from Shandong University of Traditional Chinese Medicine, where she focused on clinical rehabilitation research, neurophysiology, scientific research methodologies, and academic writing. She completed her Bachelor’s degree in Rehabilitation Therapy (2018-2022) at the same university, specializing in physical factor therapy, prosthetics and orthotics, musculoskeletal rehabilitation, cardiopulmonary rehabilitation, and pediatric rehabilitation.

Professional Development

Li Yanning has diverse experience in both academic and clinical settings. She worked in the Publicity Department of Shandong University of Traditional Chinese Medicine, where she managed broadcasting and promotional activities for major university events. She actively participated in volunteer teaching programs for migrant schools, coordinating student outreach and academic activities. Clinically, she completed an internship at a Military Special Convalescence Center, where she specialized in rehabilitation treatments using advanced physiotherapy equipment. Additionally, she contributed to research projects at the Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, focusing on adolescent scoliosis and rehabilitation therapies.

Research Focus

Her research interests include neurological rehabilitation, non-invasive brain stimulation, musculoskeletal rehabilitation, stroke recovery, scoliosis treatment, and rehabilitation technology. She has a strong focus on systematic reviews, meta-analyses, and evidence-based rehabilitation strategies, aiming to bridge the gap between clinical practice and innovative treatment methodologies.

Author Metrics:

Li Yanning has published multiple research papers in high-impact journals. She is the first author of Effect of Non-invasive Brain Stimulation on Conscious Disorder in Patients After Brain Injury: A Network Meta-analysis, published in Neurological Sciences (IF: 3.2, Q3). As a co-author, she has contributed to Physiotherapeutic Scoliosis-Specific Exercise for Adolescent Idiopathic Scoliosis: A Systematic Review and Network Meta-analysis in Am J Phys Med Rehabil (IF: 2.2, Q4), Efficacy of Robot-assisted Training on Upper Limb Motor Function After Stroke: A Systematic Review and Network Meta-analysis (Accepted, IF: 1.9, Q4), and Effect of Electrical Stimulation in Treating Foot Drop after Stroke: A Systematic Review and Network Meta-analysis (Under Review).

Honors & Awards

Li Yanning has been recognized for her contributions to rehabilitation research. She received the Third Prize in the Chinese Medical Association Science and Technology Award for her work on curriculum ideology-driven rehabilitation education reform. Her research on bilateral upper limb coordination music therapy for stroke rehabilitation was acknowledged for its integration of visual analysis, epidemiology, and clinical trials, further demonstrating her expertise in innovative rehabilitation methodologies.

Publication Top Notes

1.  Effect of Non-invasive Brain Stimulation on Conscious Disorder in Patients After Brain Injury: A Network Meta-analysis

  • Authors: Li Y, Li L, Huang H
  • Journal: Neurological Sciences
  • Year: 2023
  • Publication Status: Published
  • Research Focus: This study examines the effectiveness of non-invasive brain stimulation in improving consciousness disorders in post-brain injury patients. A network meta-analysis approach is used to compare different stimulation techniques and evaluate their clinical impact.

2.  Physiotherapeutic Scoliosis-Specific Exercise for the Treatment of Adolescent Idiopathic Scoliosis: A Systematic Review and Network Meta-analysis

  • Authors: Dong H, You M, Li Y, Wang B, Huang H
  • Journal: American Journal of Physical Medicine & Rehabilitation
  • Year: 2024
  • Publication Status: Published
  • Research Focus: This study assesses the efficacy of scoliosis-specific exercises for treating adolescent idiopathic scoliosis. Through a network meta-analysis, the study ranks different exercise interventions based on their effectiveness in spinal curvature correction and functional improvement.

3.  Efficacy of Robot-assisted Training on Upper Limb Motor Function After Stroke: A Systematic Review and Network Meta-analysis

  • Authors: H. Wang, X. Wu, Y. Li (Yaning Li), S. Yu (Shaohong Yu)
  • Journal: Archives of Rehabilitation Research and Clinical Translation
  • Year: 2024
  • Publication Status: Published
  • Research Focus: This study systematically reviews and analyzes the effectiveness of robot-assisted training in improving upper limb motor function in post-stroke patients. It uses network meta-analysis to compare various robotic rehabilitation interventions and determine their efficacy.

4.  Effect of Electrical Stimulation in the Treatment of Patients with Foot Drop after Stroke: A Systematic Review and Network Meta-analysis

  • Authors: [Names not provided]
  • Journal: [Under Review]
  • Year: [Pending]
  • Publication Status: Under Review
  • Research Focus: This study investigates the effectiveness of electrical stimulation therapy in treating foot drop after a stroke. A systematic review and network meta-analysis are used to evaluate the impact of different stimulation protocols on functional recovery.

5.  Network Meta-analysis of the Rehabilitation Effects of Traditional Chinese Exercises on Motor Function Recovery in Post-stroke Patients

  • Authors: Li Yanning, Huang Hailiang, Ding Liang
  • Journal: Proceedings of the 2023 China Health Qigong Science Forum
  • Year: 2023
  • Publication Status: Published
  • Research Focus: This study explores the rehabilitation benefits of traditional Chinese exercises for stroke patients. A network meta-analysis is used to compare different exercise interventions and determine their effectiveness in motor function recovery.

6.  Research Progress on the Pharmacological Mechanism of Panax Notoginseng in Treating Fractures

  • Authors: Wang Bingjie, Huang Hailiang, Dong Huanrun, Li Yanning
  • Journal: Global Traditional Chinese Medicine
  • Year: 2025
  • Publication Status: Accepted (Pending Publication)
  • Research Focus: This study reviews the pharmacological mechanisms of Panax Notoginseng in fracture healing. It examines its bioactive compounds and their therapeutic effects in promoting bone regeneration and reducing inflammation.

Conclusion

Ms. Yaning Li is a highly qualified and deserving candidate for the Best Researcher Award in Neurological Rehabilitation. Her strong academic record, impactful publications, innovative research, and clinical contributions make her an excellent nominee. With further development in international collaborations, grant acquisitions, and emerging rehabilitation technologies, she has the potential to become a leading global researcher in neurological rehabilitation and physiotherapy.

Shurun Wang | Brain function connectivity analysis | Best Researcher Award

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Dr. Shurun Wang | Brain function connectivity analysis | Best Researcher Award

Postdoctoral researcher at  University of Science and Technology, China📝

Dr. Shurun Wang is a postdoctoral researcher at the School of Information Science and Technology, University of Science and Technology of China (USTC), specializing in biomedical signal analysis and brain function connectivity analysis. He holds a Ph.D. from the School of Electrical Engineering and Automation, Hefei University of Technology, where he also earned his MSc and BSc degrees. Dr. Wang has actively contributed to academic research and is passionate about advancing understanding in brain connectivity and biomedical systems through his work. He has received several prestigious awards, including the National Scholarship for Doctoral Students and the Outstanding Doctoral Dissertation Award from the Anhui Province Robotics Society.

Profile    

Orcid Profile

Google Scholar Profile 

Education 🎓

Dr. Shurun Wang has a robust academic background in Electrical Engineering and Automation. He completed his Ph.D. at the School of Electrical Engineering and Automation, Hefei University of Technology, in Hefei, China, from 2019 to 2024, where he specialized in biomedical signal analysis and brain function connectivity. During his doctoral studies, he also had the opportunity to enhance his research through a one-year visiting student program at the Graduate School of Medicine, Juntendo University, Tokyo, Japan, from April 2023 to April 2024. Prior to his Ph.D., Dr. Wang earned both his M.Sc. (2016–2019) and B.Sc. (2012–2016) degrees in Electrical Engineering and Automation, also from Hefei University of Technology, where he gained a strong foundation in electrical systems and automation technologies.

Professional Experience 💼

Dr. Shurun Wang is currently a Postdoctoral Researcher at the School of Information Science and Technology, University of Science and Technology of China (USTC), where he conducts cutting-edge research in biomedical signal analysis and brain function connectivity analysis. Prior to his postdoctoral role, he completed his Ph.D. at the School of Electrical Engineering and Automation, Hefei University of Technology. During his doctoral studies, Dr. Wang also undertook a one-year research stint as a visiting student at the Graduate School of Medicine, Juntendo University, Tokyo, Japan.

Research Interests 🔬

Dr. Wang’s primary research interests lie in biomedical signal analysis, particularly focusing on brain function connectivity. His work aims to develop advanced computational techniques to enhance the understanding of neural systems and brain activity patterns. This research is vital for applications in medical diagnostics, neuroengineering, and cognitive neuroscience, with potential contributions to improving treatments for neurological disorders and enhancing brain-machine interfaces.

Author Metrics 🏆

Dr. Wang has published extensively in top-tier journals and conferences, contributing to the fields of biomedical signal processing and neural networks. His work has gained recognition for its innovation and impact on both theoretical advancements and practical applications.

Awards and Recognition 🏆

  • National Scholarship for Doctoral Students
  • Outstanding Doctoral Dissertation Award from Anhui Province Robotics Society
  • Xplore New Automation Award 2018 of PHOENIX CONTACT

Academic Service

Dr. Wang has contributed significantly to the academic community by reviewing for over 10 reputable journals, including:

  • IEEE Transactions on Instrumentation and Measurement
  • IEEE Transactions on Neural Networks and Learning Systems
  • Scientific Reports
  • Applied Artificial Intelligence

Publications Top Notes 📚

  1. Title: A novel approach to detecting muscle fatigue based on sEMG by using neural architecture search framework
    Authors: S Wang, H Tang, B Wang, J Mo
    Journal: IEEE Transactions on Neural Networks and Learning Systems
    Volume: 34, Issue: 8, Pages: 4932-4943
    Year: 2021
    Citations: 28
    Summary: This paper proposes a novel method for detecting muscle fatigue from surface electromyographic (sEMG) signals by employing a neural architecture search (NAS) framework. The study demonstrates that using NAS can efficiently identify optimal deep learning architectures for accurate and real-time fatigue detection, making it a significant contribution to health monitoring technologies.
  2. Title: Analysis of fatigue in the biceps brachii by using rapid refined composite multiscale sample entropy
    Authors: S Wang, H Tang, B Wang, J Mo
    Journal: Biomedical Signal Processing and Control
    Volume: 67, Article Number: 102510
    Year: 2021
    Citations: 24
    Summary: This study focuses on the analysis of muscle fatigue in the biceps brachii using rapid refined composite multiscale sample entropy (rRCMSE), a novel method to quantify the complexity of sEMG signals during fatigue. The research provides a reliable approach for muscle fatigue assessment in clinical and rehabilitation settings.
  3. Title: A double threshold adaptive method for robust detection of muscle activation intervals from surface electromyographic signals
    Authors: H Tang, S Wang, Q Tan, B Wang
    Journal: IEEE Transactions on Instrumentation and Measurement
    Volume: 71, Article Number: 1-12
    Year: 2022
    Citations: 5
    Summary: This paper introduces a double-threshold adaptive method to improve the robustness of detecting muscle activation intervals from sEMG signals. The method enhances the reliability and accuracy of muscle activation detection, which is crucial for fatigue monitoring and rehabilitation applications.
  4. Title: Continuous estimation of human joint angles from sEMG using a multi-feature temporal convolutional attention-based network
    Authors: S Wang, H Tang, L Gao, Q Tan
    Journal: IEEE Journal of Biomedical and Health Informatics
    Volume: 26, Issue: 11, Pages: 5461-5472
    Year: 2022
    Citations: 4
    Summary: This paper proposes a deep learning-based model that estimates human joint angles continuously from sEMG signals. The model uses a temporal convolutional attention mechanism to process multiple features, enabling precise real-time joint angle estimation for applications in rehabilitation and prosthetics.
  5. Title: Optimizing graph neural network architectures for schizophrenia spectrum disorder prediction using evolutionary algorithms
    Authors: S Wang, H Tang, R Himeno, J Solé-Casals, CF Caiafa, S Han, S Aoki, …
    Journal: Computer Methods and Programs in Biomedicine
    Volume: 257, Article Number: 108419
    Year: 2022
    Citations: Not specified
    Summary: This paper focuses on optimizing graph neural network (GNN) architectures for predicting schizophrenia spectrum disorder. By using evolutionary algorithms, the study improves model accuracy, highlighting the potential of AI in mental health diagnosis and prognosis.

Conclusion

Dr. Shurun Wang is an outstanding candidate for the Best Researcher Award, owing to his remarkable contributions to biomedical signal analysis, brain function connectivity, and innovative health technologies. His research in detecting muscle fatigue, improving neurodiagnostic systems, and exploring neural systems for mental health prediction has the potential to revolutionize the field. With his continued dedication to advancing computational techniques in health sciences, Dr. Wang is poised to make even greater strides in improving medical diagnostics, neuroengineering, and treatment methodologies for neurological disorders.