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         author = {Tommasi, Tatiana and Orabona, Francesco and Castellini, Claudio and Caputo, Barbara},
       projects = {Idiap, EMMA},
          title = {Improving Control of Dexterous Hand Prostheses Using Adaptive Learning},
           year = {2012},
           note = {Manuscript Number 11-0673},
            doi = {10.1109/TRO.2012.2226386},
       abstract = {At the time of writing, highly dexterous hand prostheses
are being manufactured and, to some extent, marketed.
This means wearable, implantable mechanical hands with many
independently controllable degrees of freedom, e.g. finger flexion
and thumb rotation. Still, control by the patient is an open issue,
and the most promising way ahead is probably machine learning
applied to surface electromyography (sEMG). Researchers have
mainly concentrated so far on improving the accuracy of sEMG
classification and/or regression; but in general, a finer control
implies longer and harder training times. A more natural form
of control might shorten the time a patient requires to learn
how to use the prosthesis, but the machine training time will
inevitably be longer.
In this work we propose a general method to re-use past
experience, in the form of models synthesised from previous users,
to boost the adaptivity of the prosthesis and dramatically shorten
the training time. Extensive tests on a database recorded from
10 healthy subjects in controlled and non-controlled conditions
reveal that the method is highly effective.},
            pdf = {https://publications.idiap.ch/attachments/papers/2012/Tommasi_IEEE-TRO_2012.pdf}