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Relative Hidden Markov Models for Evaluating Motion Skills

机译:用于评估运动技能的相对隐藏的马尔可夫模型

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This paper is concerned with a novel problem: learning temporal models using only relative information. Such a problem arises naturally in many applications involving motion or video data. Our focus in this paper is on video-based surgical training, in which a key task is to rate the performance of a trainee based on a video capturing his motion. Compared with the conventional method of relying on ratings from senior surgeons, an automatic approach to this problem is desirable for its potential lower cost, better objectiveness, and real-time availability. To this end, we propose a novel formulation termed Relative Hidden Markov Model and develop an algorithm for obtaining a solution under this model. The proposed method utilizes only a relative ranking (based on an attribute of interest) between pairs of the inputs, which is easier to obtain and often more consistent, especially for the chosen application domain. The proposed algorithm effectively learns a model from the training data so that the attribute under consideration is linked to the likelihood of the inputs under the learned model. Hence the model can be used to compare new sequences. Synthetic data is first used to systematically evaluate the model and the algorithm, and then we experiment with real data from a surgical training system. The experimental results suggest that the proposed approach provides a promising solution to the real-world problem of motion skill evaluation from video.
机译:本文涉及一种新问题:仅使用相对信息学习时间模型。在涉及运动或视频数据的许多应用中,这种问题自然出现。我们对本文的重点是基于视频的外科培训,其中一个关键任务是根据捕获他的运动来评估学员的性能。与依赖于高级外科医生的额定值的传统方法相比,这种问题的自动方法对于其潜在的较低成本,更好的客观性和实时可用性是期望的。为此,我们提出了一种新的制剂,称为相对隐藏的马尔可夫模型,并开发一种用于在该模型下获得解决方案的算法。所提出的方法在对输入的对之间仅利用相对排名(基于感兴趣的属性),这更容易获得和往往更加一致,特别是对于所选择的应用程序域。所提出的算法有效地从训练数据中学习模型,以便所考虑的属性与学习模型下的输入的可能性相关联。因此,该模型可用于比较新序列。合成数据首先用于系统地评估模型和算法,然后我们从外科训练系统进行实验。实验结果表明,该方法为来自视频的运动技能评估的现实世界问题提供了有希望的解决方案。

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