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About

Tianyi Wang is a robotics and rehabilitation engineering researcher whose work centers on human-robot interaction, assistive technology, and the biomechanical evaluation of robotic support systems. His research has made meaningful contributions to the development and quality assessment of nursing care and self-reliance support robots, with a particular focus on sit-to-stand (STS) movement assistance — a critical yet physically demanding transition for elderly and mobility-impaired individuals. Wang's most influential contribution lies in bridging psychological and biomechanical dimensions of robotic assistance. His 2017 work on fault classification using discriminant analysis (30 citations) was among the first to formally define and detect "fault" states during robot-assisted STS movement, identifying negative psychological changes in users that had been largely overlooked in prior research. Complementing this, his evaluation framework using relative phase analysis (22 citations) introduced a quantitative method for assessing service robot performance by examining upper-lower body coordination. His subsequent 2018 study further refined fault classification using center-of-mass metrics, deepening the analytical toolkit available to researchers in assistive robotics. Collectively, Wang's work has helped establish rigorous, user-centered evaluation standards for rehabilitation robots, offering valuable guidance for safer and more responsive robotic care systems.