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Advances in deep learning for human activity recognition have been relatively limited due to the lack of large labelled datasets.
Dataset shift in machine learning
Quiñonero-Candela, J., Sugiyama, M., Schwaighofer, A., and Lawrence, N. D. (2008) · 2008
Earlier work this paper cites.
Time–frequency analysis of biosignals
Addison, P. S., Walker, J., and Guido, R. C. (2009) · 2009
Earlier work this paper cites.
Collecting complex activity datasets in highly rich networked sensor environments
Roggen, D., Calatroni, A., Rossi, M., Holleczek, T., Förster, K., Tröster, G., Lukowicz, P., Bannach, D., Pirkl, G., Ferscha, A., et al. (2010) · 2010
Earlier work this paper cites.
Self-supervised wearable-based activity recognition by learning to forecast motion
Taghanaki, S. R. and Etemad, A. (2020) · 2010
Earlier work this paper cites.
Validation of the genea accelerometer
Esliger, D., Rowlands, A. V., Hurst, T. L., Catt, M., Murray, P., and Eston, R. G. (2011) · 2011
Earlier work this paper cites.
Exploring contrastive learning in human activity recognition for healthcare
Tang, C. I., Perez-Pozuelo, I., Spathis, D., and Mascolo, C. (2020) · 2011
Earlier work this paper cites.
Contrastive predictive coding for human activity recognition
Haresamudram, H., Essa, I., and Ploetz, T. (2020b) · 2012
Earlier work this paper cites.
Introducing a new benchmarked dataset for activity monitoring
Reiss, A. and Stricker, D. (2012) · 2012
Earlier work this paper cites.
Physical activity classification using the genea wrist-worn accelerometer
Zhang, S., Rowlands, A. V., Murray, P., Hurst, T. L., et al. (2012) · 2012
Earlier work this paper cites.
Analysis of human behavior recognition algorithms based on acceleration data
Bruno, B., Mastrogiovanni, F., Sgorbissa, A., Vernazza, T., and Zaccaria, R. (2013) · 2013
Earlier work this paper cites.
On preserving statistical characteristics of accelerometry data using their empirical cumulative distribution
Hammerla, N. Y., Kirkham, R., Andras, P., and Ploetz, T. (2013) · 2013
Earlier work this paper cites.
Activity recognition using a single accelerometer placed at the wrist or ankle
Mannini, A., Intille, S. S., Rosenberger, M., Sabatini, A. M., and Haskell, W. (2013) · 2013
Earlier work this paper cites.
Efficient estimation of word representations in vector space
Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013) · 2013
Earlier work this paper cites.
Deep inside convolutional networks: Visualising image classification models and saliency maps
Simonyan, K., Vedaldi, A., and Zisserman, A. (2013) · 2013
Earlier work this paper cites.
A tutorial on human activity recognition using body-worn inertial sensors
Bulling, A., Blanke, U., and Schiele, B. (2014) · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J. (2014) · 2014
Earlier work this paper cites.
Striving for simplicity: The all convolutional net
Springenberg, J. T., Dosovitskiy, A., Brox, T., and Riedmiller, M. (2014) · 2014
Earlier work this paper cites.
Unsupervised visual representation learning by context prediction
Doersch, C., Gupta, A., and Efros, A. A. (2015) · 2015
Earlier work this paper cites.
Deep convolutional neural networks on multichannel time series for human activity recognition
Yang, J., Nguyen, M. N., San, P. P., Li, X., and Krishnaswamy, S. (2015) · 2015
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Hip and wrist accelerometer algorithms for free-living behavior classification
Ellis, K., Kerr, J., Godbole, S., Staudenmayer, J., and Lanckriet, G. (2016) · 2016
Cited alongside, same era.
Deep, convolutional, and recurrent models for human activity recognition using wearables
Hammerla, N. Y., Halloran, S., and Plötz, T. (2016) · 2016
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Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., and Sun, J. (2016) · 2016
Cited alongside, same era.
Unsupervised learning of visual representations by solving jigsaw puzzles
Noroozi, M. and Favaro, P. (2016) · 2016
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Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 uk biobank participants
Willetts, M., Hollowell, S., Aslett, L., Holmes, C., and Doherty, A. (2018) · 2018
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On the role of features in human activity recognition
Haresamudram, H., Anderson, D. V., and Plötz, T. (2019) · 2019
Later among the works it cites.
Layer-wise relevance propagation: an overview
Montavon, G., Binder, A., Lapuschkin, S., Samek, W., and Müller, K.-R. (2019) · 2019
Later among the works it cites.
Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al. (2019) · 2019
Later among the works it cites.
Multi-task self-supervised learning for human activity detection
Saeed, A., Ozcelebi, T., and Lukkien, J. (2019) · 2019
Later among the works it cites.
Smartphone and smartwatch-based biometrics using activities of daily living
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On-body localization of wearable devices: An investigation of position-aware activity recognition
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Colorful image colorization
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Large scale population assessment of physical activity using wrist worn accelerometers: The uk biobank study
Doherty, A., Jackson, D., Hammerla, N., Plötz, T., Olivier, P., Granat, M. H., White, T., Van Hees, V. T., Trenell, M. I., Owen, C. G., et al. (2017) · 2017
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Axiomatic attribution for deep networks
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Data augmentation of wearable sensor data for parkinson’s disease monitoring using convolutional neural networks
Um, T. T., Pfister, F. M. J., Pichler, D., Endo, S., Lang, M., Hirche, S., Fietzek, U., and Kulić, D. (2017) · 2017
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Weiss, G. M., Yoneda, K., and Hayajneh, T. (2019) · 2019
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A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G. (2020) · 2020
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Creagh, A. P., Simillion, C., Bourke, A., Scotland, A., Lipsmeier, F., Bernasconi, C., Beek, J. v., Baker, M., Gossens, C., Lindemann, M., and Vos, M. D. (2020) · 2020
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Masked reconstruction based self-supervision for human activity recognition
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Momentum contrast for unsupervised visual representation learning
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Towards best practice in explaining neural network decisions with lrp
Kohlbrenner, M., Bauer, A., Nakajima, S., Binder, A., Samek, W., and Lapuschkin, S. (2020) · 2020
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Imutube: Automatic extraction of virtual on-body accelerometry from video for human activity recognition
Kwon, H., Tong, C., Haresamudram, H., Gao, Y., Abowd, G. D., Lane, N. D., and Ploetz, T. (2020) · 2020
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Us population-referenced percentiles for wrist-worn accelerometer-derived activity
Belcher, B. R., Wolff-Hughes, D. L., Dooley, E. E., Staudenmayer, J., Berrigan, D., Eberhardt, M. S., and Troiano, R. P. (2021) · 2021
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On the opportunities and risks of foundation models
Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al. (2021) · 2021
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Interpretable deep learning for the remote characterisation of ambulation in multiple sclerosis using smartphones
Creagh, A. P., Lipsmeier, F., Lindemann, M., and Vos, M. D. (2021) · 2021
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Explaining deep neural networks and beyond: A review of methods and applications
Samek, W., Montavon, G., Lapuschkin, S., Anders, C. J., and Müller, K.-R. (2021) · 2021
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Selfhar: Improving human activity recognition through self-training with unlabeled data
Tang, C. I., Perez-Pozuelo, I., Spathis, D., Brage, S., Wareham, N., and Mascolo, C. (2021) · 2021
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Breaking away from labels: The promise of self-supervised machine learning in intelligent health
Spathis, D., Perez-Pozuelo, I., Marques-Fernandez, L., and Mascolo, C. (2022) · 2022
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