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Feature extraction is crucial for human activity recognition (HAR) using body-worn movement sensors.
Predictive coding–I
Peter Elias. 1955 · 1955
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014 · 1958
Earlier work this paper cites.
Adaptive predictive coding of speech signals
Bishnu S Atal and Manfred R Schroeder. 1970 · 1986
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
Earlier work this paper cites.
Detection of eating and drinking arm gestures using inertial body-worn sensors. In Ninth IEEE International Symposium on Wearable Computers (ISWC’05) . IEEE, 160–163
Oliver Amft, Holger Junker, and Gerhard Troster. 2005 · 2005
Earlier work this paper cites.
Analyzing features for activity recognition. In Proceedings of the 2005 joint conference on Smart objects and ambient intelligence: innovative context-aware services: usages and technologies . 159–163
Tâm Huynh and Bernt Schiele. 2005 · 2005
Earlier work this paper cites.
Wearable activity tracking in car manufacturing
T. Stiefmeier, D. Roggen, G. Ogris, P. Lukowicz, and G. Tröster. 2008 · 2008
Earlier work this paper cites.
Preprocessing techniques for context recognition from accelerometer data
Davide Figo, Pedro C Diniz, Diogo R Ferreira, and Joao MP Cardoso. 2010 · 2010
Earlier work this paper cites.
Noise-contrastive estimation: A new estimation principle for unnormalized statistical models. In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics . 297–304
Michael Gutmann and Aapo Hyvärinen. 2010 · 2010
Earlier work this paper cites.
Rectified linear units improve restricted boltzmann machines. In ICML
Vinod Nair and Geoffrey E Hinton. 2010 · 2010
Earlier work this paper cites.
Freezing of gait: moving forward on a mysterious clinical phenomenon
John G Nutt, Bastiaan R Bloem, Nir Giladi, Mark Hallett, Fay B Horak, and Alice Nieuwboer. 2011 · 2011
Earlier work this paper cites.
Feature learning for activity recognition in ubiquitous computing. In Twenty-second international joint conference on artificial intelligence
Thomas Plötz, Nils Y Hammerla, and Patrick L Olivier. 2011 · 2011
Earlier work this paper cites.
What next, Ubicomp? Celebrating an intellectual disappearing act
Gregory D Abowd. 2012 · 2012
Earlier work this paper cites.
Introducing a new benchmarked dataset for activity monitoring
A. Reiss and D. Stricker. 2012 · 2012
Earlier work this paper cites.
USC-HAD: a daily activity dataset for ubiquitous activity recognition using wearable sensors
M. Zhang and A. Sawchuk. 2012 · 2012
Earlier work this paper cites.
A public domain dataset for human activity recognition using smartphones.. In Esann
Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra, and Jorge Luis Reyes-Ortiz. 2013 · 2013
Earlier work this paper cites.
Speech recognition with deep recurrent neural networks. In 2013 IEEE international conference on acoustics, speech and signal processing . IEEE, 6645–6649
Alex Graves, Abdel-rahman Mohamed, and Geoffrey Hinton. 2013 · 2013
Earlier work this paper cites.
On preserving statistical characteristics of accelerometry data using their empirical cumulative distribution. In Proceedings of the 2013 international symposium on wearable computers . 65–68
Nils Y Hammerla, Reuben Kirkham, Peter Andras, and Thomas Ploetz. 2013 · 2013
Earlier work this paper cites.
Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems . 3111–3119
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013 · 2013
Earlier work this paper cites.
A tutorial on human activity recognition using body-worn inertial sensors
Andreas Bulling, Ulf Blanke, and Bernt Schiele. 2014 · 2014
Earlier work this paper cites.
Empirical evaluation of gated recurrent neural networks on sequence modeling
Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
Earlier work this paper cites.
Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) . 1532–1543
Jeffrey Pennington, Richard Socher, and Christopher D Manning. 2014 · 2014
Earlier work this paper cites.
StudentLife: assessing mental health, academic performance and behavioral trends of college students using smartphones. In Proceedings of the 2014 ACM international joint conference on pervasive and ubiquitous computing . 3–14
Rui Wang, Fanglin Chen, Zhenyu Chen, Tianxing Li, Gabriella Harari, Stefanie Tignor, Xia Zhou, Dror Ben-Zeev, and Andrew T Campbell. 2014 · 2014
Earlier work this paper cites.
Convolutional neural networks for human activity recognition using mobile sensors. In 6th International Conference on Mobile Computing, Applications and Services . IEEE, 197–205
Ming Zeng, Le T Nguyen, Bo Yu, Ole J Mengshoel, Jiang Zhu, Pang Wu, and Joy Zhang. 2014 · 2014
Earlier work this paper cites.
Unsupervised visual representation learning by context prediction. In Proceedings of the IEEE international conference on computer vision . 1422–1430
Carl Doersch, Abhinav Gupta, and Alexei A Efros. 2015 · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy. 2015 · 2015
Earlier work this paper cites.
Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015 · 2015
Earlier work this paper cites.
A Practical Approach for Recognizing Eating Moments with Wrist-Mounted Inertial Sensing. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (Osaka, Japan) (UbiComp ’15) . Association for Computing Machinery, New York, NY, USA, 1029–1040
Edison Thomaz, Irfan Essa, and Gregory D. Abowd. 2015 · 2015
Earlier work this paper cites.
Deep convolutional neural networks on multichannel time series for human activity recognition.. In Ijcai , Vol. 15. Citeseer, 3995–4001
Jianbo Yang, Minh Nhut Nguyen, Phyo Phyo San, Xiaoli Li, and Shonali Krishnaswamy. 2015 · 2015
Cited alongside, same era.
Deep speech 2: End-to-end speech recognition in english and mandarin. In International conference on machine learning . 173–182
Dario Amodei, Sundaram Ananthanarayanan, Rishita Anubhai, Jingliang Bai, Eric Battenberg, Carl Case, Jared Casper, Bryan Catanzaro, Qiang Cheng, Guoliang Chen, et al · 2016
Cited alongside, same era.
Human daily activity and fall recognition using a smartphone’s acceleration sensor. In International Conference on Information and Communication Technologies for Ageing Well and e-Health . Springer, 100–118
Charikleia Chatzaki, Matthew Pediaditis, George Vavoulas, and Manolis Tsiknakis. 2016 · 2016
Cited alongside, same era.
Unsupervised home monitoring of Parkinson’s disease motor symptoms using body-worn accelerometers
James M Fisher, Nils Y Hammerla, Thomas Ploetz, Peter Andras, Lynn Rochester, and Richard W Walker. 2016 · 2016
Cited alongside, same era.
Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2018 · 2018
Later among the works it cites.
Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018 · 2018
Later among the works it cites.
A multi-sensor setting activity recognition simulation tool. In Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers . 1444–1448
Shingo Takeda, Tsuyoshi Okita, Paula Lago, and Sozo Inoue. 2018 · 2018
Later among the works it cites.
Deep auto-set: A deep auto-encoder-set network for activity recognition using wearables. In Proceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services . 246–253
Alireza Abedin Varamin, Ehsan Abbasnejad, Qinfeng Shi, Damith C Ranasinghe, and Hamid Rezatofighi. 2018 · 2018
Later among the works it cites.
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Nils Y Hammerla, Shane Halloran, and Thomas Plötz. 2016 · 2016
Cited alongside, same era.
Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition . 770–778
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016 · 2016
Cited alongside, same era.
Unsupervised feature extraction by time-contrastive learning and nonlinear ICA. In Advances in Neural Information Processing Systems . 3765–3773
Aapo Hyvarinen and Hiroshi Morioka. 2016 · 2016
Cited alongside, same era.
Shuffle and learn: unsupervised learning using temporal order verification. In European Conference on Computer Vision . Springer, 527–544
Ishan Misra, C Lawrence Zitnick, and Martial Hebert. 2016 · 2016
Cited alongside, same era.
Deep convolutional and lstm recurrent neural networks for multimodal wearable activity recognition
Francisco Javier Ordóñez and Daniel Roggen. 2016 · 2016
Cited alongside, same era.
Context encoders: Feature learning by inpainting. In Proceedings of the IEEE conference on computer vision and pattern recognition . 2536–2544
Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, and Alexei A Efros. 2016 · 2016
Cited alongside, same era.
Whoosh: non-voice acoustics for low-cost, hands-free, and rapid input on smartwatches. In Proceedings of the 2016 ACM International Symposium on Wearable Computers . 120–127
Gabriel Reyes, Dingtian Zhang, Sarthak Ghosh, Pratik Shah, Jason Wu, Aman Parnami, Bailey Bercik, Thad Starner, Gregory D Abowd, and W Keith Edwards. 2016 · 2016
Cited alongside, same era.
Tapskin: Recognizing on-skin input for smartwatches. In Proceedings of the 2016 ACM International Conference on Interactive Surfaces and Spaces . 13–22
Cheng Zhang, AbdelKareem Bedri, Gabriel Reyes, Bailey Bercik, Omer T Inan, Thad E Starner, and Gregory D Abowd. 2016a · 2016
Cited alongside, same era.
Learning and using the arrow of time. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 8052–8060
Donglai Wei, Joseph J Lim, Andrew Zisserman, and William T Freeman. 2018 · 2018
Later among the works it cites.
Understanding and improving recurrent networks for human activity recognition by continuous attention. In Proceedings of the 2018 ACM International Symposium on Wearable Computers . 56–63
Ming Zeng, Haoxiang Gao, Tong Yu, Ole J Mengshoel, Helge Langseth, Ian Lane, and Xiaobing Liu. 2018 · 2018
Later among the works it cites.
An unsupervised autoregressive model for speech representation learning
Yu-An Chung, Wei-Ning Hsu, Hao Tang, and James Glass. 2019 · 2019
Later among the works it cites.
On the role of features in human activity recognition. In Proceedings of the 23rd International Symposium on Wearable Computers . 78–88
Harish Haresamudram, David V Anderson, and Thomas Plötz. 2019 · 2019
Later among the works it cites.
Data-efficient image recognition with contrastive predictive coding
Olivier J Hénaff, Aravind Srinivas, Jeffrey De Fauw, Ali Razavi, Carl Doersch, SM Eslami, and Aaron van den Oord. 2019 · 2019
Later among the works it cites.
Prediction of mood instability with passive sensing
Mehrab Bin Morshed, Koustuv Saha, Richard Li, Sidney K D’Mello, Munmun De Choudhury, Gregory D Abowd, and Thomas Plötz. 2019 · 2019
Later among the works it cites.
Pytorch: An imperative style, high-performance deep learning library. In Advances in neural information processing systems . 8026–8037
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
Later among the works it cites.
Multi-task Self-Supervised Learning for Human Activity Detection
Aaqib Saeed, Tanir Ozcelebi, and Johan Lukkien. 2019 · 2019
Later among the works it cites.
wav2vec 2.0: A framework for self-supervised learning of speech representations
Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, and Michael Auli. 2020 · 2020
Closest in time.
A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020 · 2020
Closest in time.
Debiased contrastive learning
Ching-Yao Chuang, Joshua Robinson, Yen-Chen Lin, Antonio Torralba, and Stefanie Jegelka. 2020 · 2020
Closest in time.
Generative pre-training for speech with autoregressive predictive coding. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 3497–3501
Yu-An Chung and James Glass. 2020a · 2020
Closest in time.
Improved speech representations with multi-target autoregressive predictive coding
Yu-An Chung and James Glass. 2020b · 2020
Closest in time.
Masked reconstruction based self-supervision for human activity recognition. In Proceedings of the 2020 International Symposium on Wearable Computers . 45–49
Harish Haresamudram, Apoorva Beedu, Varun Agrawal, Patrick L Grady, Irfan Essa, Judy Hoffman, and Thomas Plötz. 2020 · 2020
Closest in time.
Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 9729–9738
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. 2020 · 2020
Closest in time.
Hyeokhyen Kwon, Catherine Tong, Harish Haresamudram, Yan Gao, Gregory D Abowd, Nicholas D Lane, and Thomas Ploetz. 2020 · 2020
Closest in time.
Tera: Self-supervised learning of transformer encoder representation for speech
Andy T Liu, Shang-Wen Li, and Hung-yi Lee. 2020a · 2020
Closest in time.
Mockingjay: Unsupervised speech representation learning with deep bidirectional transformer encoders. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 6419–6423
Andy T Liu, Shu-wen Yang, Po-Han Chi, Po-chun Hsu, and Hung-yi Lee. 2020b · 2020
Closest in time.
Human Activity Recognition from Wearable Sensor Data Using Self-Attention
Saif Mahmud, M Tonmoy, Kishor Kumar Bhaumik, AKM Rahman, M Ashraful Amin, Mohammad Shoyaib, Muhammad Asif Hossain Khan, and Amin Ahsan Ali. 2020 · 2020
Closest in time.
Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation
David MW Powers. 2020 · 2020
Closest in time.
Unsupervised pretraining transfers well across languages. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 7414–7418
Morgane Rivière, Armand Joulin, Pierre-Emmanuel Mazaré, and Emmanuel Dupoux. 2020 · 2020
Closest in time.
Unsupervised pre-training of bidirectional speech encoders via masked reconstruction. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 6889–6893
Weiran Wang, Qingming Tang, and Karen Livescu. 2020 · 2020
Closest in time.
NeckSense: A Multi-Sensor Necklace for Detecting Eating Activities in Free-Living Conditions
Shibo Zhang, Yuqi Zhao, Dzung Tri Nguyen, Runsheng Xu, Sougata Sen, Josiah Hester, and Nabil Alshurafa. 2020 · 2020
Closest in time.
MusiCoder: A Universal Music-Acoustic Encoder Based on Transformers
Yilun Zhao, Xinda Wu, Yuqing Ye, Jia Guo, and Kejun Zhang. 2020 · 2020
Closest in time.
The Opportunity challenge: A benchmark database for on-body sensor-based activity recognition
R. Chavarriaga, H. Sagha, A. Calatroni, S. T. Digumarti, G. Tröster, J. R. Millán, and D. Roggen. 2013 · 2042
Closest in time.