PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Latent space segmentation for mobile gait analysis
Aris Valtazanos, DK Arvind and Subramanian Ramamoorthy
ACM Transactions on Embedded Computing Systems 2012.


An unsupervised learning algorithm is presented for segmentation and evaluation of motion data from the on-body Orient wireless motion capture system for mobile gait analysis. The algorithm is model-free and operates on the latent space of the motion, by first aggregating all the sensor data into a single vector, and then modelling them on a low-dimensional manifold to perform segmentation. The proposed approach is contrasted to a basic, model-based algorithm, which operates directly on the joint angles computed by the Orient sensor devices. The latent space algorithm is shown to be capable of retrieving qualitative features of the motion even in the face of noisy or incomplete sensor readings.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:8900
Deposited By:Subramanian Ramamoorthy
Deposited On:21 February 2012