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1 March 1991 Neural networks for the recognition of skilled arm and hand movements
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In this paper we are focusing on the discrimination and recognition of arm movements described in terms of the evolution of the joint angles during the trajectory of the movement. Considering this as a classification problem, we could use a three layered feedforward network trained by the backpropagation learning algorithm to discriminate between arm movements described in the representation proposed by Marr and Vaina (1982) and Vaina and Bennour (1985). We discuss the contribution of the sampling rate for the value of the joint angles in the input layers, the number of angles necessary for a good recognition of the movement. We introduce a new method for determining the size of the network at input stage, which eliminates the unnecessary data in the training set. The effect of changing the number of frames used to train the network for each movement and the properties of the backpropagation algorithm in this application are discussed. We demonstrate that a description of movements at multiple scales of resolution organized from general to particular is conducive to an efficient learning scheme in the network. The main result obtained is that the discrimination between different arm movements is most efficiently obtained at the appropriate and most meaningful scale of resolution, as predicted by the Marr and Vaina's model.
© (1991) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Lucia M. Vaina and Temel Engin Tuncer "Neural networks for the recognition of skilled arm and hand movements", Proc. SPIE 1468, Applications of Artificial Intelligence IX, (1 March 1991);

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