Thantip Sittiruk
Papers
2
Total Citations
9
H-Index
2
About
Thantip Sittiruk is a rising researcher at the intersection of biomedical engineering and human-robot interaction, whose work focuses on decoding human intent for advanced rehabilitation technologies. Her primary research areas include surface electromyography (sEMG) signal processing, force estimation, and machine learning for human-machine interfaces. Sittiruk’s major contribution lies in developing real-time, non-invasive systems that estimate human force from muscle activity, enabling more intuitive and responsive robotic assistance. In her 2024 work, she systematically evaluated nineteen regression models to continuously estimate force from sEMG signals across varied arm postures, achieving robust performance in two degrees of freedom. Her 2025 study advanced this further by implementing a Gaussian Process Regression-based system for real-time force estimation in rehabilitation contexts, demonstrating practical applicability for human-robot interaction. Though early in her career, with her most cited papers accumulating 5 and 4 citations respectively, Sittiruk’s work represents a promising step toward seamless, adaptive rehabilitation robots that can respond naturally to patient effort. Her systematic approach to modeling muscle dynamics across different postures addresses a critical challenge in making assistive technologies more effective and comfortable for users.
Research Focus
Key Achievements
Top Papers
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