Ms. Soree Hwang | Biomedical Engineering – Researcher at KIST | Korea Institute of Science and Technology, South Korea
So Ree Hwang is an emerging biomedical engineering researcher whose multidisciplinary expertise in mechanical engineering, design, and biomedical systems positions her at the forefront of AI-integrated healthcare innovation. Currently pursuing her Ph.D. in Biomedical Engineering at Korea University and conducting research at the Korea Institute of Science and Technology (KIST), she brings a unique fusion of engineering principles and data-driven health solutions. Her work centers on wearable sensor technologies, machine learning, and neurorehabilitation platforms, with a clear commitment to transforming clinical care through personalized, non-invasive, and remote monitoring systems. As a student researcher, her contributions are both technically rigorous and socially impactful, targeting populations with urgent healthcare needs such as stroke patients and frontline workers.
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Education:
Hwang’s educational journey reflects a strong foundation in engineering disciplines, which has evolved into highly specialized research in healthcare technologies. She holds a Bachelor’s degree in Mechanical Engineering, followed by a Master’s in Design and Engineering. These programs equipped her with deep technical knowledge and problem-solving capabilities, allowing her to apply advanced engineering concepts to human health systems. Currently pursuing a Ph.D. in Biomedical Engineering at Korea University, she is now applying data-driven methodologies to solve real-world clinical challenges. Her academic path shows a consistent upward progression from foundational mechanics to AI-assisted medical technology.
Experience:
Professionally, Hwang has gained extensive research experience through her role at KIST, a premier research institute known for advanced technological development. She has contributed to over 16 research projects, covering domains such as gait analysis, fatigue detection, and motor function evaluation. Her collaborative initiatives include working with Korea University Hospital and the Korea Institute of Machinery & Materials (KIMM) on technologies that promote neuroplasticity in stroke rehabilitation. She also collaborated on the development of an AI-powered stress monitoring system tailored for police officers. These projects illustrate her ability to engage in high-level, cross-disciplinary research that directly translates into real-world healthcare improvements.
Research Interest:
Hwang’s research interests include AI-based health monitoring, smart wearable technologies, neurorehabilitation, and digital healthcare applications for special populations. She is especially focused on developing systems that use inertial measurement units (IMUs), electromyography (sEMG), and machine learning algorithms to detect fatigue, classify abnormal gait, and assess stroke severity. Her goal is to enable remote, continuous health monitoring systems that assist clinicians in delivering customized rehabilitation and stress management therapies. By combining physiological signal analysis with deep learning architectures, she aims to improve both diagnostic accuracy and patient autonomy in the recovery process.
Award:
Though she is still in the early stages of her career, So Ree Hwang has demonstrated excellence in academic and applied research that makes her a strong candidate for the Best Researcher Award. She is actively involved in two patent applications and is a member of recognized professional bodies such as the Korean Society for Precision Engineering and the Korean Society of Medical & Biological Engineering. These affiliations, coupled with her multidisciplinary achievements and collaborative projects, establish her as a rising talent whose work is already contributing meaningfully to the biomedical field.
Publications:
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🧠📶 “A Multimodal Fatigue Detection System Using sEMG and IMU Signals with a Hybrid CNN-LSTM-Attention Model”, Sensors, 2025 – Main Author, Vol. 25, Article 3309.
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🦿📊 “Machine Learning-Based Abnormal Gait Classification with IMU Considering Joint Impairment”, Sensors, 2024 – Main Author, Vol. 24, Article 5571.
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🧬🚶 “Classification of Gait Phases Based on a Machine Learning Approach Using Muscle Synergy”, Frontiers in Human Neuroscience, 2023 – Co-Author, Vol. 17, Article 1201935.
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🔁🧠 “Classification of Stroke Severity Using Clinically Relevant Symmetric Gait Features Based on Recursive Feature Elimination with Cross-Validation”, IEEE Access, 2022 – Co-Author, Vol. 10, pp. 119437–119447.
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⛹️📐 “Prediction of Lower Extremity Multi-Joint Angles During Overground Walking Using a Single IMU with a Low Frequency Based on an LSTM Recurrent Neural Network”, Sensors, 2021 – Co-Author, Vol. 22, Article 53.
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🖨️🧪 “Property Analysis of Photo-Polymerization-Type 3D-Printed Structures Based on Multi-Composite Materials”, Applied Sciences, 2021 – Main Author, Vol. 11, Article 8545.
Conclusion:
So Ree Hwang stands out as a highly motivated and innovative researcher who is already making meaningful contributions to AI-driven healthcare systems. Her integration of engineering principles with clinical insights allows her to design impactful solutions for rehabilitation and stress monitoring. With a solid academic background, multiple high-quality publications, and successful collaborative research efforts, she exemplifies the qualities of a future leader in biomedical research. Recognizing her with the Best Researcher Award would not only validate her significant accomplishments but also inspire further innovations in digital health and neurorehabilitation technologies.