Mattia Orlandi
Papers
1
Total Citations
5
H-Index
1
About
Mattia Orlandi is a researcher at the forefront of human-machine interaction, specializing in gesture recognition through surface electromyography (sEMG) and low-power embedded systems. His work focuses on reconstructing neural spikes from sEMG signals to enable intuitive, real-time control of devices—a critical step toward seamless prosthetics and wearable interfaces. His most-cited paper, "sEMG Neural Spikes Reconstruction for Gesture Recognition on a Low-Power Multicore Processor" (2022), demonstrates how to efficiently process complex biological signals on energy-constrained hardware, achieving accurate hand movement classification while minimizing power consumption. This contribution bridges the gap between biomedical signal processing and edge computing, making advanced gesture recognition viable for portable applications. With a growing citation impact, Orlandi’s research is shaping the future of non-invasive, responsive human-machine interfaces. His work holds promise for assistive technologies, virtual reality, and next-generation control systems, positioning him as an emerging leader in the field of biosignal-driven interaction.
Research Focus
Key Achievements
Top Papers
- 1