The problem of rigid motion segmentation of trajectory data under orthography has been long solved for non-degenerate motions in the absence of noise. But because real trajectory data often incorporates noise, outliers, motion degeneracies and motion dependencies, recently proposed motion segmentation methods resort to non-trivial representations to achieve state of the art segmentation accuracies, at the expense of a large computational cost. This paper proposes a method that dramatically reduces this cost (by two or three orders of magnitude) with minimal accuracy loss (from 98.8% achieved by the state of the art, to 96.2% achieved by our method on the standard Hopkins 155 dataset). Computational efficiency comes from the use of a simple but powerful representation of motion that explicitly incorporates mechanisms to deal with noise, outliers and motion degeneracies. Subsets of motion models with the best balance between prediction accuracy and model complexity are chosen from a pool of candidates, which are then used for segmentation.

We present MPTrain, a mobile phone based system that takes advantage of the influence of music in exercise performance, enabling users to more easily achieve their exercise goals. MPTrain is designed as a mobile and personal system (hardware and software) that users wear while exercising (walking, jogging or running). MPTrain's hardware includes a set of physiological sensors wirelessly connected to a mobile phone carried by the user. MPTrain's software allows the user to enter a desired exercise pattern (in terms of desired heart-rate over time) and assists the user in achieving his/her exercising goals by: (1) constantly monitoring the user's physiology (heart-rate in number of beats per minute) and movement (speed in number of steps per minute); and (2) selecting and playing music with specific features that will encourage the user to speed up, slow down or keep the pace to be on track with his/her exercise goals.We describe the hardware and software components of the MPTrain system, and present some preliminary results when using MPTrain while jogging.