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Various health-care applications such as assisted living, fall detection etc., require modeling of user behavior through Human Activity Recognition (HAR).
A mathematical theory of communication
Shannon, C. E · 1948
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Dropout: A simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R · 1958
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Elementary Applied Statistics: For Students in Behavioral Science
Freeman, L. C · 1965
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Human activity recognition in pervasive health-care: Supporting efficient remote collaboration
Osmani, V., Balasubramaniam, S., and Botvich, D · 2008
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Bayesian active learning for classification and preference learning
Houlsby, N., Huszár, F., Ghahramani, Z., and Lengyel, M · 2011
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Fall detection with wearable sensors–safe (smart fall detection)
Ojetola, O., Gaura, E. I., and Brusey, J · 2011
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Active learning
Settles, B · 2012
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Real-time crowd labeling for deployable activity recognition
Lasecki, W. S., Song, Y. C., Kautz, H., and Bigham, J. P · 2013
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Using unlabeled data in a sparse-coding framework for human activity recognition
Bhattacharya, S., Nurmi, P., Hammerla, N., and Plötz, T · 2014
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An early resource characterization of deep learning on wearables, smartphones and internet-of-things devices
Lane, N. D., Bhattacharya, S., Georgiev, P., Forlivesi, C., and Kawsar, F · 2015
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Smart devices are different: Assessing and mitigating mobile sensing heterogeneities for activity recognition
Stisen, A., Blunck, H., Bhattacharya, S., Prentow, T. S., Kjærgaard, M. B., Dey, A., Sonne, T., and Jensen, M. M · 2015
Cited alongside, same era.
From smart to deep: Robust activity recognition on smartwatches using deep learning
Bhattacharya, S., and Lane, N. D · 2016
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Human daily activity recognition for healthcare using wearable and visual sensing data
Liu, X., Liu, L., Simske, S. J., and Liu, J · 2016
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Deep bayesian active learning with image data
Gal, Y., Islam, R., and Ghahramani, Z · 2017
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Deep active learning for named entity recognition
Shen, Y., Yun, H., Lipton, Z., Kronrod, Y., and Anandkumar, A · 2017
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Deepsense: A unified deep learning framework for time-series mobile sensing data processing
Yao, S., Hu, S., Zhao, Y., Zhang, A., and Abdelzaher, T · 2017
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Smartfall: A smartwatch-based fall detection system using deep learning
Mauldin, T. R., Canby, M. E., Metsis, V., Ngu, A. H. H., and Rivera, C. C · 2018
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Harnet: Towards on-device incremental learning using deep ensembles on constrained devices
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y., and Ghahramani, Z · 2016
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Active learning enabled activity recognition
Hossain, H. M. S., Roy, N., and Khan, M. A. A. H · 2016
Cited alongside, same era.
Sundaramoorthy, P., Gudur, G. K., Moorthy, M. R., Bhandari, R. N., and Vijayaraghavan, V · 2018
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Deep learning for sensor-based activity recognition: A survey
Wang, J., Chen, Y., Hao, S., Peng, X., and Hu, L · 2019
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