NinaPro - Non-Invasive Adaptive Hand Prosthetics
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 and a very coarse control of the force,
as there is no haptic feedback. Patients interface with the prothesis
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). This contrasts
with recent advances in mechatronics, thanks to which mechanical hands
gifted with many degrees-of-freedom and force control are being built.
There is a need for prosthetic hands able to naturally reproduce a wide
amount of movements and forces, while at the same time requiring a lower
effort in learning how to control hand postures. This goes beyond
mechatronic dexterity: the real challenge is how to provide patients
with a cheap, easy and natural way of controlling the prosthesis.
The goal of this project is to develop a family of algorithms able to significantly augment the dexterity, and reduce the training time, for sEMG controlled prosthesis. By testing our findings on a very large collection of data, this project will pave the way for a new generation of prosthetic hands. The work will be organized along the following four themes.
Theme 1: Data Acquisition and Analysis. The goal of this theme is to develop a reproducible protocol to acquire large data sets for healthy patients performing certain movements and amputated patients also making complex movements, while analyzing and assessing the data as they become avaliable. The data acquisition includes the acquisition of signal data and the calibration of the sensors to limit the noise in the data. Relevant clinical data will be acquired at the same time such as age, gender, height, weight and for amputated patients also the exact place of the amputation and the time between amputation and tests performed. The data acquisition and analysis will proceed in close connection with the other themes.
Theme 2: Augmented Dexterity: Posture Classification. The objective of this theme is to push the current state of the art in prosthetic hand posture classification from handling a maximum of 12 postures up to 40-50. We will design and implement state of the art machine learning algorithms within the multi kernel learning framework, using the sEMG signals separated instead of concatenated, as it is the mainstream practice today. We will then proceed to extend the algorithm so to exploit the intrinsic hierarchical structure of hand postures. The outcome of this theme will offer patients a much wider dexterity compared to the current state of the art.
Theme 3: Augmented Dexterity: Natural Control. This research theme is about pushing the envelope of sEMG control: extending it to a quasi-perfect prediction of force, by independently modeling and controlling single degrees-of-motion. The overall aim is then to augment the dexterity that an sEMG-controlled prosthesis could potentially achieve mimicking the way a human hand works. Results in this theme will be periodically benchmarked against those achieved in Theme 2.
Theme 4: Adaptive Learning. The goal of this theme is to develop learning algorithms to better interpret 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. We will build pre-trained models of various data postures, on the data acquired in theme 1, and we will adapt these general models to the needs of individual users as new data will became available using adaptive online learning methods.