Towards Dexterous and Adaptable Prosthetic Hands

Daily life of hand amputees can be poor compared to what it was before the amputation. The state of the art in hand prosthetics, at the time of writing, does not offer more than 2-3 degrees of freedom, which translates to a maximum of 12 hand postures in controlled settings. Patients interface with the prosthesis via surface electromyography (sEMG), recorded using surface electrodes. Learning how to control the device through many input sEMG channels is a long and difficult process for most patients, that therefore settles for limited and very simplified movements (open/close). The goal of this project is to pave the way to the next generation of dexterous and easy to control prosthetic hands. Specifically, we will act in three complementary directions: (1) we will push the current state of the art in prosthetic hand posture classification from handling a maximum of 12 postures up to 40, (2) we will use learning algorithms for a better interpretation the sEMG signals acquired from the patients, with the ultimate goal of boosting the learning process necessary for the patient to effectively use the prosthesis; (3) we will develop a reproducible protocol to acquire large data sets from healthy patients, and acquire a database of at least 100 subjects to validate our findings. All the work, especially the definition of the acquisition protocol for the database, will be carried out in close collaboration with the SUVA rehabilitation clinic in Sion. This will ensure that the research will be guided by the needs of patients.
Machine Learning
Idiap Research Institute
Hasler Stiftung (Hasler Foundation)
Mar 01, 2011
Sep 30, 2011