Michele Francesco Penna
Papers
4
Total Citations
16
H-Index
3
About
Michele Francesco Penna is a rising researcher at the intersection of wearable robotics, human movement decoding, and human-robot interaction. His work focuses on developing intelligent control systems for robotic prostheses and exoskeletons that can seamlessly interpret a user’s intent. Penna’s key contribution lies in applying **Adaptive Dynamic Movement Primitives (DMPs)** to decode locomotion modes and continuous gait phases, enabling wearable robots to synchronize with volitional user movements in real time. His most-cited paper (2023, 5 citations) introduces a locomotion mode recognition algorithm using adaptive DMPs, while a companion paper (also 5 citations) advances continuous phase estimation for rhythmic tasks. Penna has extended this framework to upper-limb exoskeletons for reaching tasks (4 citations) and, most recently, to enhancing motor synchrony in dyadic human-robot-human interactions (2025). By bridging intent decoding with collaborative robotics, Penna’s work promises more intuitive, responsive assistive devices for individuals with motor impairments.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4