The present paper proposes pattern recognition techniques for the evaluation of exercises performed on weight training machines equipped with a load cell and rotary encoder for the measurement of essential force and weight displacement characteristics during training. The latter parameters can be used for the implementation of intelligent modeling methods like artificial neural networks in order to assess the exercise technique automatically and provide the athlete with appropriate feedback. First results of the developed classifiers indicate good performance values and high classification rates, demonstrating a significant potential of machine learning routines for the autonomous evaluation of performances on weight machines.
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