My research focuses on artificial intelligence, biomechanics, human factors and ergonomics, robotics, wearable sensing, biomedical signal processing, and time-series analysis with applications in healthcare and manufacturing systems. Broadly, my work aims to develop cost-effective, portable, and user-centered technologies that improve human safety, performance, accessibility, productivity, and quality of life.
My current research vision is centered on human-centered engineering solutions that support workers, patients, older adults, people with mobility limitations, frontline healthcare professionals, and manufacturing workforces. I am especially interested in how wearable sensors, AI, robotics, and digital technologies can be used to better understand human movement, reduce musculoskeletal risk, support safer human-robot collaboration, and improve decision-making in healthcare and industrial environments.
My research goals can be detailed under three strategic areas.
This research direction focuses on developing wearable-based sensing and AI tools for real-time ergonomic risk assessment in human-robot collaboration. By using signals such as electromyography, plantar pressure, and other biomechanical data, my goal is to estimate worker physical demand during collaborative tasks and support safety-aware robot adaptation. This work advances a human-centered vision for collaborative robotics, where robots can become safer and more responsive partners in manufacturing environments.
This area focuses on the development and evaluation of soft exoskeleton technologies to support mobility, reduce physical fatigue, and improve human performance. These systems have potential applications in healthcare, rehabilitation, workplace safety, and physically demanding tasks. My work in this area combines biomechanics, wearable sensing, human-subject data collection, and user-centered design to better understand how assistive technologies can support movement, comfort, and quality of life.
This research direction focuses on developing digital representations of manufacturing systems where humans, robots, and machines interact. These human-centered digital twins can combine robot motion, task layout, cycle time, quality metrics, and wearable-sensor data to better understand the relationship between productivity and ergonomic risk. This work supports Industry 4.0 and emerging Industry 5.0 directions in smart manufacturing, collaborative robotics, and human-centered automation.
Together, these research areas support my long-term goal of building interdisciplinary research that connects engineering, healthcare, robotics, data science, biomechanics, and manufacturing. Through this work, I aim to contribute to safer workplaces, more accessible assistive technologies, and smarter engineering systems that keep human needs at the center of design.