RI: Small: Clustering, Classification and Alignment of Time Series for Human Sensing

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Time series analysis is central to the study of computer vision, signal processing, computer graphics, machine learning, and social sciences, among other fields. This project entails original contributions towards algorithms for unsupervised pattern discovery and temporal alignment of time series, and its applications to model human motion. In particular, the PI proposes three new methods: (1) a discriminative temporal clustering that factorizes a set of time series into segments belonging to one of k temporal clusters, (2) a method for discovering the set of most discriminative segments between two sets of time series, and (3) an unsupervised algorithm for temporally aligning multi-modal time series. The PI proposes an energy minimization framework to encompass these three problems. This framework should provide researchers with a thorough understanding of a large number of existing time series techniques, and it may serve as a tool for dealing with other problems in time series as they arise.Enabling computers to understand human behavior has the potential to revolutionize many areas that benefit society such as clinical diagnosis, human computer interaction, and social robotics. Advances in time series to model human actions and events from sensory data have been critical to the success of systems that can recognize and characterize human behavior. However, most existing algorithms have been supervised in nature. Supervised learning typically requires large amounts of human annotation, that is typically labor intensive and is difficult to standardize across coders. In this proposal the PI explores the use of unsupervised learning techniques for aligning and discovering patterns in time series of human motion that have been captured with accelerometers, video or motion capture technologies. The PI will show how the proposed algorithms outperform state-of-the-art techniques in several human sensing tasks such as temporal alignment of human motion, temporal clustering of human activities from video, learning motion primitives, and joint segmentation and classification of human behavior. In the educational aspect, the PI will continue to provide support to the Carnegie Science Center to demonstrate human sensing technologies, as well as incorporate a large number of undergraduates in his research. The research source code will be made available to the scientific community.