Papers
3
Total Citations
1,003
H-Index
3
About
Giorgio Giatsidis is a leading researcher in rehabilitation robotics and biomedical engineering, whose work focuses on restoring hand functionality for amputees through non-invasive, naturally-controlled prosthetic systems. His most influential contribution, the 2014 paper "Electromyography data for non-invasive naturally-controlled robotic hand prostheses," has garnered an impressive 931 citations, establishing a foundational dataset and methodology for the field. Giatsidis pioneered the application of machine learning to surface electromyography (sEMG) signals, enabling more intuitive and precise control of myoelectric prosthetic hands. His 2016 study on the effect of clinical parameters (66 citations) systematically examined how factors like electrode placement and signal variability impact control performance, providing critical guidelines for real-world implementation. Additionally, his 2015 work on the effects of long-term prosthesis use (6 citations) explored how user adaptation influences control capabilities. Collectively, Giatsidis’s research bridges the gap between laboratory prototypes and practical, user-friendly prostheses, addressing key challenges in signal processing and human-machine interaction. His work remains essential reading for students and researchers developing next-generation assistive technologies.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3