Francesca Cordella has received the Laurea degree in Electronic Engineering with the highest mark (110/110) from the University of Naples Federico II discussing the thesis “Technologies for the simulation of laparoscopic surgery”. Since January 2009 to October 2011 she has been a Ph.D. student in Computer and Automation Engineering at the University of Naples Federico II. Her Ph.D. thesis regarded “Grasping algorithms for antorpomorphic robotic hands inspired to human behavior”.
Her research interests include:
- the analysis of the grasping action performed by human beings;
- the analysis of the anatomy of the human hand and of its behavior in order to acquire a better knowledge of the hand kinematics;
- the development of human-like grasping algorithms with reduced computational cost;
- the design of new human-like robotic hands endowed with high dexterity;
- the design of innovative rehabilitation devices.
One of the main applications of her work is in assistive robotics, with special reference to upper limb prostheses and rehabilitation devices. Building robotic devices able to replicate the human behavior guarantees obtaining motor recovery, functional substitution and human-robot interaction as human-like as possible. Bio-inspiration, at the basis of her work, enables to design rehabilitation robotic devices assuring a therapy for motor recovery more similar to that effected by a human therapist. It makes also possible substituting the functionality of a missing limb with robotic prostheses exhibiting physical and functional features as much as possible similar to the human ones, reducing the complexity of the control that ensures the stability of grasping and realizing human-robot interaction as safe and natural as possible. In order to guarantee the safety in human-robot interaction, her research interests regard also the implementation of tracking algorithms for human pose estimation in order to study the human behavior and to guarantee the safety of human-robot interaction. The so obtained data can be used for detecting and avoiding possible collisions between humans and robots.