About

Francesco Infarinato is a leading researcher in neurorehabilitation, specializing in the integration of robotic technologies and physiological monitoring to restore motor function after stroke. His work focuses on developing and validating innovative therapies for both upper and lower limb recovery, with a particular emphasis on translating these interventions into practical, home-based settings. Infarinato’s most influential contribution is his 2015 feasibility study on self-administered arm and hand training using a motivational gaming environment, a paper that has garnered 139 citations and demonstrated the potential for high-dose, engaging therapy outside the clinic. He has also pioneered the use of kinematic parameters from robotic devices to objectively track patient progress, as seen in his 2019 observational study (40 citations), and has advanced the understanding of functional gait recovery by combining overground robot-assisted training with electromyography (EMG) to analyze muscle activation patterns (21 citations). Further notable achievements include his work on grasp recognition for rehabilitation support and the use of electroencephalographic (EEG) markers to evaluate the neural effects of robot-aided therapy. By bridging robotics, biofeedback, and clinical assessment, Infarinato’s research is shaping the future of personalized, data-driven stroke rehabilitation.

Research Focus

Key Achievements

6
H-Index
6
Papers
249
Total Citations
42
Avg Citations/Paper
🏆 Most Cited Paper
Feasibility study into self-administered training at home using an arm and hand device with motivational gaming environment in chronic stroke
139 citations · 2015
📈 Most Prolific Year: 2015 (2 Papers)
🤝 Key Collaborators: 29
🏛 Institutions: Vita-Salute San Raffaele University, IRCCS Ospedale San Raffaele, Istituti di Ricovero e Cura a Carattere Scientifico, Istituto di Ricovero e Cura a Carattere Scientifico San Raffaele

Top Papers

  1. 1
  2. 2
  3. 3
  4. 4
  5. 5
  6. 6

Key Collaborators

Contact & Links

Available for collaboration
Content generated · 12 days ago