Gesture spotting with body-worn inertial sensors to detect user activities
Pattern Recognition
Gestures are strings: efficient online gesture spotting and classification using string matching
Proceedings of the ICST 2nd international conference on Body area networks
Multi Activity Recognition Based on Bodymodel-Derived Primitives
LoCA '09 Proceedings of the 4th International Symposium on Location and Context Awareness
Gesture-Controlled User Input to Complete Questionnaires on Wrist-Worn Watches
Proceedings of the 13th International Conference on Human-Computer Interaction. Part II: Novel Interaction Methods and Techniques
Extraction of multiple motion trajectories in human motion
SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis
Traffic analysis attacks on Skype VoIP calls
Computer Communications
Making gestural input from arm-worn inertial sensors more practical
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Connecting users to virtual worlds within MPEG-V standardization
Image Communication
Complex activity recognition using context-driven activity theory and activity signatures
ACM Transactions on Computer-Human Interaction (TOCHI)
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Gesture, as a 驴natural驴 mean, provides an alternative way for human-computer interaction. The recognition of continuous gestures suffers greatly from the existences of non-gesture hand motions. The given gestures can start at any moment in an input sequence. Hidden Markov Model (HMM) is used to tackle this problem. This paper proposes a novel method for the spotting and recognition of continuous spatio-temporal features. Without sliding the input temporal patterns past the trained models, the algorithm makes use of accumulation scores for evaluation. Therefore, it is an exhaustive evaluation method but only a sum operation is needed in each input frame. The method is demonstrated with real experiments on the recognition of some spatio-temporal trajectories. Results of the experiments show that the proposed method is very effective and fast in extracting given gestures from a continuous trajectory containing non-gestures.