Machine Learning
On interpolating between probability distributions
Applied Mathematics and Computation
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
A Wireless Body Area Sensor Network for Posture Detection
ISCC '06 Proceedings of the 11th IEEE Symposium on Computers and Communications
Body posture identification using hidden Markov model with a wearable sensor network
BodyNets '08 Proceedings of the ICST 3rd international conference on Body area networks
A Low-delay Protocol for Multihop Wireless Body Area Networks
MOBIQUITOUS '07 Proceedings of the 2007 Fourth Annual International Conference on Mobile and Ubiquitous Systems: Networking&Services (MobiQuitous)
Robust and distributed stochastic localization in sensor networks: Theory and experimental results
ACM Transactions on Sensor Networks (TOSN)
Adaptive Body Posture Analysis for Elderly-Falling Detection with Multisensors
IEEE Intelligent Systems
System architecture of a wireless body area sensor network for ubiquitous health monitoring
Journal of Mobile Multimedia
Which is the best multiclass SVM method? an empirical study
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Statistical location detection with sensor networks
IEEE Transactions on Information Theory
When is the generalized likelihood ratio test optimal?
IEEE Transactions on Information Theory
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Body posture detection is extremely useful in health monitoring and rehabilitation. We develop a method to detect body posture that uses signal strength measurements from sensor nodes forming a Wireless Body Area Network (WBAN). We assume that postures (formations) take values in a discrete set and develop a composite hypothesis testing approach which uses a Generalized Likelihood Test (GLT) decision rule. The GLT rule distinguishes between a set of probability density function (pdf) families constructed using a custom pdf interpolation technique. The GLT is compared with the simple Likelihood Test (LT). We also adapt one prevalent supervised learning approach, Multiple Support Vector Machine (MSVM), to compare with our probabilistic methods. Due to the highly variant measurements from the WBAN, and these methods' different adaptability to multiple observations, our analysis and experimental results suggest that GLT is more accurate and suitable for posture/formation detection. Even for very similar postures in our experiments, GLT demonstrates high detection accuracy (around 97% with multiple observations). Besides the body area networks, the formation detection problem has interesting applications in autonomous robot systems.