Adversarial Unsupervised Representation Learning for Activity Time-Series
Open accessAbstract
Sufficient physical activity and restful sleep play a major role in the prevention and cure of many chronic conditions. Being able to proactively screen and monitor such chronic conditions would be a big step forward for overall health. The rapid increase in the popularity of wearable devices pro-vides a significant new source, making it possible to track the user’s lifestyle real-time. In this paper, we propose a novel unsupervised representation learning technique called activ-ity2vecthat learns and “summarizes” the discrete-valued ac-tivity time-series. It learns the representations with three com-ponents: (i) the co-occurrence and magnitude of the activ-ity levels in a time-segment, (ii) neighboring context of the time-segment, and (iii) promoting subject-invariance with ad-versarial training. We evaluate our method on four disorder prediction tasks using linear classifiers. Empirical evaluation demonstrates that our proposed method scales and performs better than many strong baselines. The adversarial regime helps improve the generalizability of our representations by promoting subject invariant features. We also show that using the representations at the level of a day works the best since human activity is structured in terms of daily routines.
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Activity2vec uses adversarial-based training to model target invariance and enhance the representation ability of the model in different behavior stages.
“In modeling behavior data, Activity2vec [169] uses adversarial-based training to model target invariance and enhance the representation ability of the model in different behavior stages.”
From Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects (2024) · cites this paper as [169]
The Modeling Subject Invariance section describes adversarial training that makes activity2vec representations less dependent on the person who generated the data. Results and Discussion reports improved prediction performance and more generalizable representations. The paper does not describe the adversarial objective as target invariance or establish improved representation across different behavior stages. (LLM, conf=0.86)
Experimental — accuracy not yet measuredBased on adversarial training, recent works performed subject-invariant learning, making the system less sensitive to personal and environmental factors.
“Based on adversarial training process, some of the most recent works performed subject-invariant learning, which makes the system less sensitive to personal and environmental factors112,121.”
From The future of sleep health: a data-driven revolution in sleep science and medicine (2020) · cites this paper as [112]
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Arjovsky, M., Chintala, S., & Bottou, L. (2017) · arXiv · doi:10.48550/arxiv.1701.07875 · Cited by 2,225 · see citing works
The role and validity of actigraphy in sleep medicine: an update
Sadeh, A. (2011) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2010.10.001 · Cited by 1,415 · see citing works
Bagnall, A., Lines, J., Bostrom, A., Large, J., & Keogh, E. (2017) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-016-0483-9 · Cited by 1,377 · see citing works
Impact of Physical Activity on Sleep:A Deep Learning Based ExplorationPreprint
Sathyanarayana, A., Joty, S., Fernández-Luque, L., Ofli, F., Srivastava, J., Elmagarmid, A. K., Taheri, S., & Arora, T. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1607.07034 · Cited by 73 · see citing works
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Research works printed but not linked: 24
- Alsheikh, M. A.; Selim, A.; Niyato, D.; Doyle, L.; Lin, S.; and Tan, H.-P. 2016. Deep activity recognition models with triaxial accelerometers. In AAAI Workshops.
- Bengio, Y.; Courville, A.; and Vincent, P. 2013. Representation learning: A review and new perspectives. TPAMI 35(8):1798- 1828.
- Berndt, D. J., and Clifford, J. 1994. Using dynamic time warping to find patterns in time series. In KDD workshop, volume 10, 359- 370.
- Bild, D. E.; Bluemke, D. A.; Burke, G. L.; Detrano, R.; Diez Roux, A. V.; Folsom, A. R.; Greenland, P.; JacobsJr, D. R.; Kronmal, R.; Liu, K.; et al. 2002. Multi-ethnic study of atherosclerosis: objec- tives and design. American journal of epidemiology 156(9):871- 881.
- Bulling, A.; Blanke, U.; and Schiele, B. 2014. A tutorial on human activity recognition using body-worn inertial sensors. ACM CSUR 46(3):33.
- Collobert, R.; Weston, J.; Bottou, L.; Karlen, M.; Kavukcuoglu, K.; and Kuksa, P. 2011. Natural language processing (almost) from scratch. JMLR 12(Aug):2493-2537.
- Fulcher, B. D., and Jones, N. S. 2014. Highly comparative feature- based time-series classification. TKDE 26(12):3026-3037.
- Ganin, Y.; Ustinova, E.; Ajakan, H.; Germain, P.; Larochelle, H.; Laviolette, F.; Marchand, M.; and Lempitsky, V. 2016. Domain- adversarial training of neural networks. JMLR 17(1):2096-2030.
- Goodfellow, I.; Bengio, Y.; and Courville, A. 2016. Deep Learning. MIT Press.
- Grover, A., and Leskovec, J. 2016. node2vec: Scalable feature learning for networks. In ACM SIGKDD, 855-864.
- Gutmann, M. U., and Hyvärinen, A. 2012. Noise-contrastive es- timation of unnormalized statistical models, with applications to natural image statistics. JMLR 2012 13(Feb):307-361.
- Hinton, G.; Deng, L.; Yu, D.; Dahl, G. E.; Mohamed, A.-r.; Jaitly, N.; Senior, A.; Vanhoucke, V.; Nguyen, P.; Sainath, T. N.; et al. 2012. Deep neural networks for acoustic modeling in speech recog- nition: The shared views of four research groups. IEEE Signal Pro- cessing Magazine 29(6):82-97.
- Le, Q., and Mikolov, T. 2014. Distributed representations of sen- tences and documents. In ICML, 1188-1196.
- Lin, J.; Keogh, E.; Wei, L.; and Lonardi, S. 2007. Experiencing sax: a novel symbolic representation of time series. DMKD 15(2):107- 144.
- Maaten, L. v. d., and Hinton, G. 2008. Visualizing data using t-sne. JMLR 9(Nov):2579-2605.
- Mikolov, T.; Sutskever, I.; Chen, K.; Corrado, G. S.; and Dean, J. 2013. Distributed representations of words and phrases and their compositionality. In NIPS, 3111-3119.
- Saha, T. K.; Joty, S.; Hassan, N.; and Hasan, M. A. 2017. Regu- larized and retrofitted models for learning sentence representation with context. In CIKM, 547-556. ACM.
- Schäfer, P. 2015. The boss is concerned with time series classifi- cation in the presence of noise. DMKD 29(6):1505-1530.
- Schäfer, P. 2016. Scalable time series classification. DMKD 30(5):1273-1298.
- Senin, P., and Malinchik, S. 2013. Sax-vsm: Interpretable time series classification using sax and vector space model. In ICDM, 1175-1180. IEEE.
- Sorlie, P. D.; Avilés-Santa, L. M.; Wassertheil-Smoller, S.; Kaplan, R. C.; Daviglus, M. L.; Giachello, A. L.; Schneiderman, N.; Raij, L.; Talavera, G.; Allison, M.; et al. 2010. Design and implementa- tion of the hispanic community health study/study of latinos. An- nals of epidemiology 20(8):629-641.
- Storm, F. A.; Heller, B. W.; and Mazzà, C. 2015. Step detection and activity recognition accuracy of seven physical activity monitors. PloS one 10(3):e0118723.
- Sundermeyer, M.; Schlüter, R.; and Ney, H. 2012. Lstm neural networks for language modeling. In INTERSPEECH.
- Warburton, D. E.; Nicol, C. W.; and Bredin, S. S. 2006. Health benefits of physical activity: the evidence. Canadian medical as- sociation journal 174(6):801-809.
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- Authors
- Aggarwal, K., Joty, S., Fernandez-Luque, L., & Srivastava, J.
- Affiliations
- University of Minnesota (US)
- Nanyang Technological University (SG)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2019-07-17
- Volume
- 33
- Issue
- 01
- Pages
- 834-841
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2901370885
- Cited by
- 0
- References
- 4
- FWCI
- 0.00
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- www.aaai.org/ (unspecified)
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Health Sciences › Medicine › Physiology › Physical Activity and Health
- Keywords
- unsupervised feature learning, adversarial training, wearable devices, physical activity monitoring, sleep, chronic disease prediction, daily routine
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