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About

Rongli Wang is a leading researcher in wearable robotics and rehabilitation engineering, with a focus on restoring mobility for stroke survivors. Her work centers on developing intelligent, sensor-driven systems that bridge human motion intent and robotic assistance. Wang’s most impactful contribution is her 2019 study on IMU-based gait phase recognition for stroke survivors, which has garnered 24 citations and demonstrates a practical, non-invasive method for detecting walking phases using inertial measurement units—a critical step for controlling wearable rehabilitation robots. She has further advanced the field with a 2024 study on a dual-mode, machine-learning-enhanced wearable sensing system for synergetic muscular activity monitoring, achieving 8 citations for its scalable, lightweight design that simultaneously captures muscular deformation and biopotential signals. Wang has also prioritized safety in rehabilitation, introducing a therapist-joined hybrid control method for ankle-foot systems in 2015, which combines zero torque control with proxy-based sliding mode to prevent injury. Her preliminary work on gait phase detection (2018) laid the groundwork for real-time, adaptive robotic assistance. Through these contributions, Wang is shaping safer, more effective wearable technologies that empower stroke survivors to regain independence.