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About

Sruthi Ramadurai is a rising researcher at the intersection of wearable robotics and human-robot collaboration, whose work is shaping how machines can better assist and coordinate with people. Her research focuses on three key areas: exoskeleton assistance for reducing physical effort, physiological metrics for evaluating human-robot teaming, and machine learning for predicting human energy expenditure. In her most cited work (29 citations), she demonstrated how hip abduction assistance from wearable robots can reduce metabolic cost during walking while maintaining balance, a critical insight for designing assistive devices for mobility-impaired populations. Her studies on human-robot collaboration for recycling (14 citations) revealed how different levels of human involvement affect work performance and fluency, while her innovative use of physiological indicators like heart rate variability has provided new ways to measure team fluency during collaborative tasks. Notably, she pioneered a machine learning approach that uses foot pressure features to predict metabolic cost during exoskeleton-assisted squatting (7 citations), enabling real-time optimization of assistive devices without cumbersome metabolic measurement equipment. Her work bridges engineering and human factors, offering practical pathways toward more intuitive and effective human-robot systems.