A Distributed Hidden Markov Model for Fine-grained Annotation in Body Sensor Networks

Authors:
Eric Guenterberg;Hassan Ghasemzadeh;Roozbeh Jafari
Affiliations:
-;-;-
Venue:
BSN '09 Proceedings of the 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks
Year:
2009

Citing 0
Cited 6

Collaborative signal processing for action recognition in body sensor networks: a distributed classification algorithm using motion transcripts

Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks
Comparative study of segmentation of periodic motion data for mobile gait analysis

WH '10 Wireless Health 2010
Rejection of irrelevant human actions in real-time hidden Markov model based recognition systems for wearable computers

Proceedings of the 2nd Conference on Wireless Health
Latent space segmentation for mobile gait analysis

ACM Transactions on Embedded Computing Systems (TECS) - Special Section on Wireless Health Systems, On-Chip and Off-Chip Network Architectures
A method for cricket bowling action classification and analysis using a system of inertial sensors

ICCSA'13 Proceedings of the 13th international conference on Computational Science and Its Applications - Volume 1
A hidden Markov model for detection and classification of arm action in cricket using wearable sensors

Journal of Mobile Multimedia

Quantified Score

Hi-index	0.00

Visualization

Abstract

Human movement models often divide movements into parts. In walking the stride can be segmented into four different parts, and in golf and other sports, the swing is divided into section based on the primary direction of motion. When analyzing a movement, it is important to correctly locate the key events dividing portions. There exist methods for dividing certain actions using data from speciﬁc sensors. We introduce a generalized method for event annotation based on Hidden Markov Models. Genetic algorithms are used for feature selection and model parameterization. Further, collaborative techniques are explored. We validate this method on a walking dataset using inertial sensors placed on various locations on a human body. Our technique is computationally simple to allow it to run on resource constrained sensor nodes.