Abstract
Activity recognition based on mobile device is an important aspect in developing human-centric pervasive applications like gaming, industrial maintenance and health monitoring. However, the data distribution of accelerometer is heavily affected by varying device locations and orientations, which will degrade the performance of recognition model. To solve this problem, we propose a fast, robust and device displacement free activity recognition model in this paper, which integrates principal component analysis (PCA) and extreme learning machine (ELM) to realize location-adaptive activity recognition. On the one hand, PCA is employed to reduce the dimensionality of feature space and extract robust features for recognition. On the other hand, in the online phase ELM is applied to classify the activity and adapt the recognition model to new device locations based on high confident recognition results in real time. Experimental results show that, with robust features and fast adaptation capability, the proposed model can adapt the classifier to new device locations quickly and obtain good recognition performance.
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Notes
“XSens Technologies B.V.”, http://www.xsens.com.
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Acknowledgments
This work is supported in part by the Natural Science Foundation of China (61070110, 90820303), and Beijing Natural Science Foundation (4112056), and the National Basic Research Program of China (973 Program, 2011CB302803).
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Chen, Y., Zhao, Z., Wang, S. et al. Extreme learning machine-based device displacement free activity recognition model. Soft Comput 16, 1617–1625 (2012). https://doi.org/10.1007/s00500-012-0822-8
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DOI: https://doi.org/10.1007/s00500-012-0822-8