Andrea Parri
Papers
18
Total Citations
720
H-Index
12
About
Andrea Parri is a leading researcher in wearable robotics and human-robot interaction, with a focus on developing intelligent, bioinspired control systems for lower-limb exoskeletons and prostheses. His work centers on real-time gait phase estimation, balance recovery, and metabolic efficiency in assistive devices. Parri’s most cited paper (137 citations) introduces an oscillator-based method for smooth, real-time gait phase estimation, a foundational contribution to wearable robotics. He also demonstrated that an ecologically-controlled exoskeleton can improve balance after slippage (126 citations), and showed that gait training with a robotic hip exoskeleton enhances metabolic efficiency in the elderly (97 citations). Parri has pioneered novel control strategies using noncontact capacitive sensors and electromyographic signals, enabling more intuitive and ergonomic human-robot interaction. His research on real-time locomotion mode recognition (75 citations) and bioinspired motor primitives (56 citations) has advanced the field’s understanding of cooperative exoskeleton control. Parri’s work is distinguished by its emphasis on practical, user-centered design, with applications in rehabilitation, active aging, and assistive technology.
Research Focus
Key Achievements
Top Papers
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- 4Real-Time Hybrid Locomotion Mode Recognition for Lower Limb Wearable Robots75 citations · 2017
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- 7Controlling a Robotic Hip Exoskeleton With Noncontact Capacitive Sensors37 citations · 2019
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- 9Whole Body Awareness for Controlling a Robotic Transfemoral Prosthesis28 citations · 2017
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