Principles of data mining
Activity and Location Recognition Using Wearable Sensors
IEEE Pervasive Computing
Design and implementation of expressive footwear
IBM Systems Journal
An automatic segmentation technique in body sensor networks based on signal energy
BodyNets '09 Proceedings of the Fourth International Conference on Body Area Networks
Mixture modeling of DNA copy number amplification patterns in cancer
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Compact and understandable descriptions of mixtures of Bernoulli distributions
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
Tracking your steps on the track: body sensor recordings of a controlled walking experiment
Proceedings of the 3rd International Conference on PErvasive Technologies Related to Assistive Environments
Portable Activity Monitoring System for Temporal Parameters of Gait Cycles
Journal of Medical Systems
Bayesian Reasoning and Machine Learning
Bayesian Reasoning and Machine Learning
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Sensor data can be used for monitoring, modeling, and recognition of human activities during daily life or in special situations. In assistive environments, modeling of characteristic walking styles have been studied as well as preventing the falls of the elderly. In this paper, we pre-process and analyze a time series collection of sensor recordings which is publicly available. More specifically, we transform the raw pressure sensor data in the insoles of the shoes to yield binary pressure patterns to indicate contact between the shoe and the ground. We model the marginal probability distributions of the resulting 0-1 data with mixture models of multivariate Bernoulli distributions. We interpret the identified mixture model in terms of gait phases.