Abstract
Activity recognition is an important step towards automatically measuring the functional health of individuals in smart home settings. Since the inherent nature of human activities is characterized by a high degree of complexity and uncertainty, it poses a great challenge to build a robust activity recognition model. This study aims to exploit deep learning techniques to learn high-level features from the binary sensor data under the assumption that there exist discriminant latent patterns inherent in the low-level features. Specifically, we first adopt a stacked autoencoder to extract high-level features, and then integrate feature extraction and classifier training into a unified framework to obtain a jointly optimized activity recognizer. We use three benchmark datasets to evaluate our method, and investigate two different original sensor data representations. Experimental results show that the proposed method achieves better recognition rate and generalizes better across different original feature representations compared with other four competing methods.
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Acknowledgments
This work was supported by the Natural Science Foundation of China (No. 61472057) and China Postdoctoral Science Foundation (No. 2016M592046).
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Wang, A., Chen, G., Shang, C., Zhang, M., Liu, L. (2016). Human Activity Recognition in a Smart Home Environment with Stacked Denoising Autoencoders. In: Song, S., Tong, Y. (eds) Web-Age Information Management. WAIM 2016. Lecture Notes in Computer Science(), vol 9998. Springer, Cham. https://doi.org/10.1007/978-3-319-47121-1_3
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DOI: https://doi.org/10.1007/978-3-319-47121-1_3
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