Gestures are strings: efficient online gesture spotting and classification using string matching

@inproceedings{Stiefmeier2007GesturesAS,
  title={Gestures are strings: efficient online gesture spotting and classification using string matching},
  author={T. Stiefmeier and D. Roggen and G. Tr{\"o}ster},
  booktitle={BODYNETS},
  year={2007}
}
  • T. Stiefmeier, D. Roggen, G. Tröster
  • Published in BODYNETS 2007
  • Computer Science
  • Context awareness is one mechanism that allows wearable computers to provide information proactively, unobtrusively and with minimal user disturbance. Gestures and activities are an important aspect of the user's context. Detection and classification of gestures may be computationally expensive for low-power, miniaturized wearable platforms, such as those that may be integrated into garments. In this paper we introduce a novel method for online and real-time spotting and classification of… CONTINUE READING
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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 33 REFERENCES
    An HMM-based approach for gesture segmentation and recognition
    • 58
    A State-Based Approach to the Representation and Recognition of Gesture
    • 325
    • Highly Influential
    • PDF
    An HMM-Based Threshold Model Approach for Gesture Recognition
    • 676
    • PDF
    Hierarchical recognition of intentional human gestures for sports video annotation
    • 95
    • Highly Influential
    • PDF
    Parametric Hidden Markov Models for Gesture Recognition
    • 654
    • Highly Influential
    • PDF
    Recognition and reproduction of gestures using a probabilistic framework combining PCA, ICA and HMM
    • 119
    • PDF
    Real-Time American Sign Language Recognition Using Desk and Wearable Computer Based Video
    • 1,262
    • PDF
    Discovering Characteristic Actions from On-Body Sensor Data
    • 143
    • PDF