Abstract
In order to achieve the better online gesture recognition rate, a multi-sensor fusion method is proposed in this chapter. After the dimension reduction and quantization, we first measure the performance of every single sensor in training phase and use this prior knowledge to determine the weight vector; then we do the fusion of multiple sensors according to the weight vector which indicates each sensor’s importance in recognition. The core algorithm we use for online gesture recognition is WarpingLCSS, which is demonstrated to be an efficient template matching method for gesture spotting. We do the experiments on the OPPORTUNITY Activity Recognition Datasets, and the results show that the recognition rate of multi-sensor fusion method achieves 61 %, which outperforms the single sensor’s performance about 11 %. This demonstrates that our proposed multi-sensor fusion method is efficient in improving the performance of online gesture recognition.
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Chen, C., Shen, H. (2014). Improving Online Gesture Recognition with WarpingLCSS by Multi-Sensor Fusion. In: Wong, W.E., Zhu, T. (eds) Computer Engineering and Networking. Lecture Notes in Electrical Engineering, vol 277. Springer, Cham. https://doi.org/10.1007/978-3-319-01766-2_64
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DOI: https://doi.org/10.1007/978-3-319-01766-2_64
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