Relative Hidden Markov Models for Video-Based Evaluation of Motion Skills in Surgical Training

A proper temporal model is essential to analysis tasks involving sequential data. In computer-assisted surgical training, which is the focus of this study, obtaining accurate temporal models is a key step towards automated skill-rating. Conventional learning approaches can have only limited success in this domain due to insufficient amount of data with accurate labels. We propose a novel formulation termed Relative Hidden Markov Model and develop algorithms for obtaining a solution under this formulation. The method requires only relative ranking between input pairs, which are readily available from training sessions in the target application, hence alleviating the requirement on data labeling. The proposed algorithm learns a model from the training data so that the attribute under consideration is linked to the likelihood of the input, hence supporting comparing new sequences. For evaluation, synthetic data are first used to assess the performance of the approach, and then we experiment with real videos from a widely-adopted surgical training platform. Experimental results suggest that the proposed approach provides a promising solution to video-based motion skill evaluation. To further illustrate the potential of generalizing the method to other applications of temporal analysis, we also report experiments on using our model on speech-based emotion recognition.