AVSS '03 Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
Pervasive Body Sensor Network: An Approach to Monitoring the Post-operative Surgical Patient
BSN '06 Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks
Analysis of the Severity of Dyskinesia in Patients with Parkinson's Disease via Wearable Sensors
BSN '06 Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
An asymptotic analysis of generative, discriminative, and pseudolikelihood estimators
Proceedings of the 25th international conference on Machine learning
Context-Aware Computing Applications
WMCSA '94 Proceedings of the 1994 First Workshop on Mobile Computing Systems and Applications
IEEE Transactions on Information Technology in Biomedicine
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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 breakdown 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.