Trans-radially amputated persons who own a myoelectric prosthesis have currently some control via surface electromyography (sEMG). However, the control systems are still limited (as they include very few movements) and not always natural (as the subject has to learn to associate movements of the muscles with the movements of the prosthesis). The Ninapro project tries helping the scientific community to overcome these limits through the creation of electromyography data sources to test machine learning algorithms. In this paper the results gained from first tests made on an amputated subject with the Ninapro acquisition protocol are detailed. In agreement with neurological studies on cortical plasticity and on the anatomy of the forearm, the amputee produced stable signals for each movement in the test. Using a k-NN classification algorithm, we obtain an average classification rate of 61.5% on all 53 movements. Successively, we simplify the task reducing the number of movements to 13, resulting in no misclassified movements. This shows that for fewer movements a very high classification accuracy is possible without the subject having to learn the movements specifically.
This paper describes a generic layout analysis system for historical documents. It presents the architecture of a pyramidal approach using three analysis levels. Each level consists of a classifier using machine learning techniques where the output of the upper level is used as a feature in the lower level. The current implementation uses a so called Dynamic Multi-Layer perceptron (DMLP), which is a natural extension of MLP classifiers. The system is evaluated on medieval documents for which a multi-layer model is used to discriminate among 10 classes organized hierarchically.
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