Fetching the paper…
Reading the bibliography…
Wearable devices such as smartwatches are becoming increasingly popular tools for objectively monitoring physical activity in free-living conditions.
Machine Learning for Scent: Learning Generalizable Perceptual Representations of Small Molecules
Sanchez-Lengeling, B.; Wei, J. N.; Lee, B. K.; Gerkin, R. C.; Aspuru-Guzik, A.; and Wiltschko, A. B. 2019 · 1910
Earlier work this paper cites.
Self-supervised Learning for ECG-based Emotion Recognition
Sarkar, P.; and Etemad, A. 2019 · 1910
Earlier work this paper cites.
The effect of endurance training on parameters of aerobic fitness
Jones, A. M.; and Carter, H. 2000 · 2000
Earlier work this paper cites.
Age-predicted maximal heart rate revisited
Tanaka, H.; Monahan, K. D.; and Seals, D. R. 2001 · 2001
Earlier work this paper cites.
Deep Transfer Learning for Physiological Signals
Chen, H.; Lundberg, S.; Erion, G.; Kim, J. H.; and Lee, S.-I. 2020 · 2002
Earlier work this paper cites.
Representation Learning on Variable Length and Incomplete Wearable-Sensory Time Series
Wu, X.; Huang, C.; Roblesgranda, P.; and Chawla, N. 2020 · 2002
Earlier work this paper cites.
Branched equation modeling of simultaneous accelerometry and heart rate monitoring improves estimate of directly measured physical activity energy expenditure
Brage, S.; Brage, N.; Franks, P. W.; Ekelund, U.; Wong, M.-Y.; Andersen, L. B.; Froberg, K.; and Wareham, N. J. 2004 · 2004
Earlier work this paper cites.
Metabolic syndrome, obesity, and mortality: impact of cardiorespiratory fitness
Katzmarzyk, P. T.; Church, T. S.; Janssen, I.; Ross, R.; and Blair, S. N. 2005 · 2005
Earlier work this paper cites.
Daily activity energy expenditure and mortality among older adults
Manini, T. M.; Everhart, J. E.; Patel, K. V.; Schoeller, D. A.; Colbert, L. H.; Visser, M.; Tylavsky, F.; Bauer, D. C.; Goodpaster, B. H.; and Harris, T. B. 2006 · 2006
Earlier work this paper cites.
Heart rate response during exercise test and cardiovascular mortality in middle-aged men
Savonen, K. P.; Lakka, T. A.; Laukkanen, J. A.; Halonen, P. M.; Rauramaa, T. H.; Salonen, J. T.; and Rauramaa, R. 2006 · 2006
Earlier work this paper cites.
Resting heart rate in cardiovascular disease
Fox, K.; Borer, J. S.; Camm, A. J.; Danchin, N.; Ferrari, R.; Sendon, J. L. L.; Steg, P. G.; Tardif, J.-C.; Tavazzi, L.; Tendera, M.; et al. 2007 · 2007
Earlier work this paper cites.
Visualizing data using t-SNE
Maaten, L. v. d.; and Hinton, G. 2008 · 2008
Earlier work this paper cites.
Multimodal deep learning
Ngiam, J.; Khosla, A.; Kim, M.; Nam, J.; Lee, H.; and Ng, A. Y. 2011 · 2011
Earlier work this paper cites.
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 · 2011
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.
Learning phrase representations using RNN encoder-decoder for statistical machine translation
Cho, K.; Van Merriënboer, B.; Gulcehre, C.; Bahdanau, D.; Bougares, F.; Schwenk, H.; and Bengio, Y. 2014 · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P.; and Ba, J. 2014 · 2014
Cited alongside, same era.
Distributed representations of sentences and documents
Le, Q.; and Mikolov, T. 2014 · 2014
Cited alongside, same era.
Deep activity recognition models with triaxial accelerometers
Alsheikh, M. A.; Selim, A.; Niyato, D.; Doyle, L.; Lin, S.; and Tan, H.-P. 2015 · 2015
Cited alongside, same era.
Distilling the knowledge in a neural network
Hinton, G.; Vinyals, O.; and Dean, J. 2015 · 2015
Cited alongside, same era.
Deep convolutional neural networks on multichannel time series for human activity recognition
Yang, J.; Nguyen, M. N.; San, P. P.; Li, X. L.; and Krishnaswamy, S. 2015 · 2015
Cited alongside, same era.
Learning Individualized Cardiovascular Responses from Large-scale Wearable Sensors Data
Hallgrímsson, H. T.; Jankovic, F.; Althoff, T.; and Foschini, L. 2018 · 2018
Later among the works it cites.
Self-supervised feature learning by learning to spot artifacts
Jenni, S.; and Favaro, P. 2018 · 2018
Later among the works it cites.
Insights from the long-tail: Learning latent representations of online user behavior in the presence of skew and sparsity
Krishnan, A.; Sharma, A.; and Sundaram, H. 2018 · 2018
Later among the works it cites.
Association of cardiorespiratory fitness with long-term mortality among adults undergoing exercise treadmill testing
Mandsager, K.; Harb, S.; Cremer, P.; Phelan, D.; Nissen, S. E.; and Jaber, W. 2018 · 2018
Later among the works it cites.
Online heart rate prediction using acceleration from a wrist worn wearable
McConville, R.; Archer, G.; Craddock, I.; ter Horst, H.; Piechocki, R.; Pope, J.; and Santos-Rodriguez, R. 2018 · 2018
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Extract: Strong examples from weakly-labeled sensor data
Blalock, D. W.; and Guttag, J. V. 2016 · 2016
Cited alongside, same era.
Xgboost: A scalable tree boosting system
Chen, T.; and Guestrin, C. 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
Cited alongside, same era.
Training and evaluating multimodal word embeddings with large-scale web annotated images
Mao, J.; Xu, J.; Jing, K.; and Yuille, A. L. 2016 · 2016
Cited alongside, same era.
Ambient sound provides supervision for visual learning
Owens, A.; Wu, J.; McDermott, J. H.; Freeman, W. T.; and Torralba, A. 2016 · 2016
Cited alongside, same era.
Large-scale physical activity data reveal worldwide activity inequality
Althoff, T.; Hicks, J. L.; King, A. C.; Delp, S. L.; Leskovec, J.; et al. 2017 · 2017
Cited alongside, same era.
Large scale population assessment of physical activity using wrist worn accelerometers: the UK biobank study
Doherty, A.; Jackson, D.; et al. 2017 · 2017
Cited alongside, same era.
Later among the works it cites.
Multimodal deep learning for activity and context recognition
Radu, V.; Tong, C.; Bhattacharya, S.; Lane, N. D.; Mascolo, C.; Marina, M. K.; and Kawsar, F. 2018 · 2018
Later among the works it cites.
Beyond expectation: Deep joint mean and quantile regression for spatio-temporal problems
Rodrigues, F.; and Pereira, F. C. 2018 · 2018
Later among the works it cites.
Adversarial unsupervised representation learning for activity time-series
Aggarwal, K.; Joty, S.; Fernandez-Luque, L.; and Srivastava, J. 2019 · 2019
Later among the works it cites.
Advanced machine learning techniques for building performance simulation: a comparative analysis
Chakraborty, D.; and Elzarka, H. 2019 · 2019
Later among the works it cites.
Developing measures of cognitive impairment in the real world from consumer-grade multimodal sensor streams
Chen, R.; Jankovic, F.; Marinsek, N.; Foschini, L.; Kourtis, L.; Signorini, A.; Pugh, M.; Shen, J.; Yaari, R.; Maljkovic, V.; et al. 2019 · 2019
Later among the works it cites.
AttnSense: multi-level attention mechanism for multimodal human activity recognition
Ma, H.; Li, W.; Zhang, X.; Gao, S.; and Lu, S. 2019 · 2019
Later among the works it cites.
Modeling Heart Rate and Activity Data for Personalized Fitness Recommendation
Ni, J.; Muhlstein, L.; and McAuley, J. 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.
PhoneMD: Learning to diagnose Parkinson’s disease from smartphone data
Schwab, P.; and Karlen, W. 2019 · 2019
Later among the works it cites.
Albert: A lite bert for self-supervised learning of language representations
Lan, Z.; Chen, M.; Goodman, S.; Gimpel, K.; Sharma, P.; and Soricut, R. 2020 · 2020
Closest in time.