An HMM-Based Threshold Model Approach for Gesture Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
An On-Line Signature Verification System Using Hidden Markov Model in Polar Space
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
Gaussian Mixture Models for on-line signature verification
WBMA '03 Proceedings of the 2003 ACM SIGMM workshop on Biometrics methods and applications
Hidden Markov Model Based Continuous Online Gesture Recognition
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
On-Line Signature Verification With Two-Stage Statistical Models
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Rejection of Non-meaningful Activities
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Speaker background models for connected digit password speaker verification
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 01
Background model design for flexible and portable speaker verification systems
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 02
Action coverage formulation for power optimization in body sensor networks
Proceedings of the 2008 Asia and South Pacific Design Automation Conference
An activity recognition system for mobile phones
Mobile Networks and Applications
Distributed Continuous Action Recognition Using a Hidden Markov Model in Body Sensor Networks
DCOSS '09 Proceedings of the 5th IEEE International Conference on Distributed Computing in Sensor Systems
A Distributed Hidden Markov Model for Fine-grained Annotation in Body Sensor Networks
BSN '09 Proceedings of the 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks
Wearable coach for sport training: A quantitative model to evaluate wrist-rotation in golf
Journal of Ambient Intelligence and Smart Environments
Ergodic HMM-UBM system for on-line signature verification
BioID_MultiComm'09 Proceedings of the 2009 joint COST 2101 and 2102 international conference on Biometric ID management and multimodal communication
A survey on wearable sensor-based systems for health monitoring and prognosis
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Information Technology in Biomedicine
Towards power optimized kalman filter for gait assessment using wearable sensors
WH '10 Wireless Health 2010
A survey on wireless body area networks
Wireless Networks
RTAS '11 Proceedings of the 2011 17th IEEE Real-Time and Embedded Technology and Applications Symposium
Feature selection and activity recognition from wearable sensors
UCS'06 Proceedings of the Third international conference on Ubiquitous Computing Systems
Target dependent score normalization techniques and their application to signature verification
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Information Technology in Biomedicine
IEEE Transactions on Information Technology in Biomedicine
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Hidden Markov Model (HMM) is a well established technique for detecting patterns in a stream of observations. It performs well when the observation sequence does not contain unseen patterns that were not part of the training set. In an unconstrained environment, the observation sequence might contain new patterns that the HMM model is not familiar with. In such cases, HMM will match the test pattern to some trained pattern, which is most similar to the test pattern. This increases the false positives in the system. In this paper, we are describing a threshold based technique to detect such irrelevant patterns in a continuous stream of observations, and classify them as unwanted or bad patterns. The novelty of our approach is that it allows early detection of unwanted patterns. Test patterns are validated on a fixed length substring of observation sequence, rather than on the whole observation sequence corresponding to the test pattern. The substrings are validated based on its similarity with a valid pattern using a threshold value. This reduces the latency of detection of unwanted movement, and makes the detection process independent of duration of the various pattern classes. We evaluated this technique in the context of body sensor networks based human action recognition, and have achieved about 93 percent accuracy in detecting unwanted actions, while maintaining a 94 percent accuracy of recognizing valid actions.