Bilateral and monolateral hand amputees suffer strong functional deficits due to their impairment. Surface
Electromyography (sEMG) currently gives some control capabilities but these are limited, often not natural and usually require long training times. The application of modern machine learning techniques to analyze sEMG activity related to natural movements seems promising but it is far from practice due to two main aspects: first, the effects of the amputation on the nervous system of the subjects are not fully clear; second, there is a strong lack of accuracy in movement classification and a few wrong movements can have important negative effects.
The goal of our research is to strongly improve robotic prosthesis control possibilities by hand amputated subjects. We will develop a coherent framework of learning algorithms able to significantly advance the state of the art in sEMG controlled prostheses in terms of control, stability and dexterity. The prosthesis controllers will be enriched by autonomous decision making that will operate through the analysis of multimodal data, such as sEMG, accelerometry, vision, haptics and so forth. Algorithms and approaches will be machine learning based, exploring deep learning as well as statistical learning techniques within integrated systems. A special focus will be on adaptive learning algorithms, i.e. algorithms able to leverage over prior information, acquired by other users or across modalities, in order to increase stability and shorten significantly the time needed by amputees for learning how to control and interact with the device.
The candidate should have a strong technical and theoretical background, with a M. Sc. in Computer science, Physics, Electrical and/or biomedical engineer or similar, and a record of research on machine learning applied to sEMG signals or similar.
Prior experience in learning methods applied to prostheses control, documented by a publication record in the field, will be a plus.
The successful candidate will work starting from October 2017 in the newly established Visual and Multimodal Applied Learning Laboratory (VANDAL), led by Prof. Caputo, in Milan, with high end computing and robotics facilities.
Application procedure (deadline is August 15, 2017): please submit your applications, including a detailed curriculum vitae, 2 representative publications and 1 page of research statement in PDF format to firstname.lastname@example.org quoting “Fellow position CB 74244” in the subject line.
Please note that this position is pending transfer FNSNF budget to IIT (FNSNF Fonds National Suisse de la Recherche Scientifique - MeganePro - Décision CRSII2_160837/1).
IIT was established in 2003 and successfully created the large scale infrastructure in Genova, a network of 10 state of the art laboratories countrywide, recruited an international staff of about 1100 people from more than 50 countries. IIT's research endeavour focuses on high-tech and innovation, representing the forefront of technology with possible application from medicine to industry, computer science, robotics, life sciences and nanobiotechnologies.
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