Detailed monitoring of training sessions of elite athletes is an important component of their training. In this paper we describe an application that performs a precise segmentation and labeling of swimming sessions. This allows a comprehensive break-down of the training session, including lap times, detailed statistics of strokes, and turns. To this end we use semi-Markov models (SMM), a formalism for labeling and segmenting sequential data, trained in a max-margin setting. To reduce the computational complexity of the task and at the same time enforce sensible output, we introduce a grammar into the SMM framework. Using the trained model on test swimming sessions of different swimmers provides highly accurate segmentation as well as perfect labeling of individual segments. The results are significantly better than those achieved by discriminative hidden Markov models.
Abstract.We introduce the first online kernelized version of SARSA(λ) to permit sparsification for arbitrary λ for 0 ≤ λ ≤ 1; this is possible via a novel kernelization of the eligibility trace that is maintained separately from the kernelized value function. This separation is crucial for preserving the functional structure of the eligibility trace when using sparse kernel projection techniques that are essential for memory efficiency and capacity control. The result is a simple and practical Kernel-SARSA(λ) algorithm for general 0 ≤ λ ≤ 1 that is memory-efficient in comparison to standard SARSA(λ) (using various basis functions) on a range of domains including a real robotics task running on a Willow Garage PR2 robot.
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