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The continuing drive for better rehabilitative healthcare hinges on the availability of sensor data which can be shared and analysed. This leverages on the widespread communications network to provide an integrated health management environment. For this paper, we delineate our current work in sensorizing rehabilitative tests of upper limb movements. Where previously we applied data driven analysis,...
Sparsity is known to be very beneficial in blind source separation (BSS). Even if data is not sparse in its current domain, it can be modelled as sparse linear combinations of atoms of a chosen dictionary. The choice of dictionary that sparsifies the data is very important. In this paper the dictionary is partly pre-specified based on chirplet modelling of various kinds of real epileptic discharges,...
In this paper, a new tensor factorization method based on k-SCA [1] approach is developed to solve the under-determined blind identification (UBI) problem where k sources are active in each signal segment. Similar to k-SCA methods we assume our k is equal to the number of sensors minus one. This approach improves the general upper bound for maximum possible number of sources in a second order underdetermined...
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