Papers

6

Total Citations

325

H-Index

6

About

Benedetta Cesqui is a leading researcher in neurorehabilitation engineering, specializing in robot-mediated therapy for upper limb recovery following stroke and neurological injury. Her work focuses on integrating electromyographic (EMG) signals with robotic systems to enhance motor rehabilitation. In her landmark 2013 study, cited over 170 times, she demonstrated the feasibility of using EMG-based pattern recognition to predict patient intentions during post-stroke robot-aided therapy, paving the way for more intuitive, patient-driven rehabilitation protocols. Cesqui has also made significant contributions to understanding spasticity reduction, showing in a 2010 study with 43 citations that active robot-mediated training can effectively decrease upper limb spasticity in chronic hemiparesis patients. Her innovative exploration of divergent force fields—robotic training that enhances errors to stimulate motor learning—has opened new avenues for rehabilitation strategies. With over 325 cumulative citations across her most influential works, Cesqui’s research bridges engineering and clinical practice, offering evidence-based approaches to improve motor recovery and quality of life for patients with chronic neurological conditions.

Research Focus

Key Achievements

6
H-Index
6
Papers
325
Total Citations
54
Avg Citations/Paper
🏆 Most Cited Paper
EMG-based pattern recognition approach in post stroke robot-aided rehabilitation: a feasibility study
170 citations · 2013
📈 Most Prolific Year: 2013 (2 Papers)
🤝 Key Collaborators: 11
🏛 Institutions: Scuola Superiore Sant'Anna, Fondazione Santa Lucia, IMT School for Advanced Studies Lucca

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 12 days ago