Kaixuan Chen
Dalin Zhang
Lina Yao
Bin Guo
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The vast proliferation of sensor devices and Internet of Things enables the applications of sensor-based activity recognition. However, there exist substantial challenges that could influence the performance of the recognition system in practical scenarios. Recently, as deep learning has demonstrated its effectiveness in many areas, plenty of deep methods have been investigated to address the challenges in activity recognition. In this study, we present a survey of the state-of-the-art deep learning methods for sensor-based human activity recognition. We first introduce the multi-modality of the sensory data and provide information for public datasets that can be used for evaluation in different challenge tasks. We then propose a new taxonomy to structure the deep methods by challenges. Challenges and challenge-related deep methods are summarized and analyzed to form an overview of the current research progress. At the end of this work, we discuss the open issues and provide some insights for future directions.
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- Zahraa S. Abdallah, Mohamed Medhat Gaber, Bala Srinivasan, and Shonali Krishnaswamy. 2018. Activity recognition with evolving data streams: A review. Comput. Surv. 51, 4 (2018), 71.Google Scholar
Digital Library
- Alireza Abedin, Seyed Hamid Rezatofighi, Qinfeng Shi, and Damith Chinthana Ranasinghe. 2019. SparseSense: Human activity recognition from highly sparse sensor data-streams using set-based neural networks. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI’19). 5780–5786.Google ScholarCross Ref
- Ali Akbari and Roozbeh Jafari. 2019. Transferring activity recognition models for new wearable sensors with deep generative domain adaptation. In Proceedings of the 18th International Conference on Information Processing in Sensor Networks. ACM, 85–96.Google ScholarDigital Library
- Ali Akbari, Jian Wu, Reese Grimsley, and Roozbeh Jafari. 2018. Hierarchical signal segmentation and classification for accurate activity recognition. In Proceedings of the ACM International Joint Conference and International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers. ACM, 1596–1605.Google ScholarDigital Library
- Ali A. Alani, Georgina Cosma, and Aboozar Taherkhani. 2020. Classifying imbalanced multi-modal sensor data for human activity recognition in a smart home using deep learning. In Proceedings of the International Joint Conference on Neural Networks (IJCNN’20). IEEE, 1–8.Google ScholarCross Ref
- Hande Alemdar, Halil Ertan, Ozlem Durmaz Incel, and Cem Ersoy. 2013. ARAS human activity datasets in multiple homes with multiple residents. In Proceedings of the 7th International Conference on Pervasive Computing Technologies for Healthcare. ICST, 232–235.Google ScholarDigital Library
- Mohammad Abu Alsheikh, Ahmed Selim, Dusit Niyato, Linda Doyle, Shaowei Lin, and Hwee-Pink Tan. 2016. Deep activity recognition models with triaxial accelerometers. In Proceedings of the Workshops at the 30th AAAI Conference on Artificial Intelligence.Google Scholar
- Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra, and Jorge Luis Reyes-Ortiz. 2013. A public domain dataset for human activity recognition using smartphones. In Proceedings of the European Symposium on Artificial Neural Networks.Google Scholar
- Sina Mokhtarzadeh Azar, Mina Ghadimi Atigh, Ahmad Nickabadi, and Alexandre Alahi. 2019. Convolutional relational machine for group activity recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 7892–7901.Google ScholarCross Ref
- Moez Baccouche, Franck Mamalet, Christian Wolf, Christophe Garcia, and Atilla Baskurt. 2010. Action classification in soccer videos with long short-term memory recurrent neural networks. In Proceedings of the International Conference on Artificial Neural Networks. Springer, 154–159.Google ScholarCross Ref
- Marc Bachlin, Meir Plotnik, Daniel Roggen, Inbal Maidan, Jeffrey M. Hausdorff, Nir Giladi, and Gerhard Troster. 2010. Wearable assistant for Parkinson’s disease patients with the freezing of gait symptom. IEEE Trans. Inf. Technol. Biomed. 14, 2 (2010), 436–446.Google ScholarDigital Library
- Lei Bai, Lina Yao, Xianzhi Wang, Salil S. Kanhere, Bin Guo, and Zhiwen Yu. 2020. Adversarial multi-view networks for activity recognition. Proc. ACM Interact., Mob., Wear. Ubiq. Technol. 4, 2 (2020), 1–22.Google ScholarDigital Library
- Lei Bai, Lina Yao, Xianzhi Wang, Salil S. Kanhere, and Yang Xiao. 2020. Prototype similarity learning for activity recognition. In Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, 649–661.Google ScholarCross Ref
- Lu Bai, Chris Yeung, Christos Efstratiou, and Moyra Chikomo. 2019. Motion2Vector: Unsupervised learning in human activity recognition using wrist-sensing data. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the ACM International Symposium on Wearable Computers. ACM, 537–542.Google ScholarDigital Library
- Donald S. Baim, Wilson S. Colucci, E. Scott Monrad, Harton S. Smith, Richard F. Wright, Alyce Lanoue, Diane F. Gauthier, Bernard J. Ransil, William Grossman, and Eugene Braunwald. 1986. Survival of patients with severe congestive heart failure treated with oral milrinone. J. Amer. Coll. Cardiol. 7, 3 (1986), 661–670.Google ScholarCross Ref
- Oresti Banos, Rafael Garcia, Juan A. Holgado-Terriza, Miguel Damas, Hector Pomares, Ignacio Rojas, Alejandro Saez, and Claudia Villalonga. 2014. mHealthDroid: A novel framework for agile development of mobile health applications. In Proceedings of the International Workshop on Ambient Assisted Living. Springer, 91–98.Google ScholarCross Ref
- Billur Barshan and Murat Cihan Yüksek. 2014. Recognizing daily and sports activities in two open source machine learning environments using body-worn sensor units. Comput. J. 57, 11 (2014), 1649–1667.Google ScholarCross Ref
- Yoshua Bengio. 2012. Deep learning of representations for unsupervised and transfer learning. In Proceedings of ICML Workshop on Unsupervised and Transfer Learning. 17–36.Google ScholarDigital Library
- Asma Benmansour, Abdelhamid Bouchachia, and Mohammed Feham. 2015. Multioccupant activity recognition in pervasive smart home environments. Comput. Surv. 48, 3 (2015), 1–36.Google ScholarDigital Library
- Avrim Blum and Tom M. Mitchell. 1998. Combining labeled and unlabeled data with Co-Training. In Proceedings of the Eleventh Annual Conference on Computational Learning Theory, COLT 1998, Madison, Wisconsin, USA, July 24--26,1998. ACM, 92--100.Google ScholarDigital Library
- Henrik Blunck, Niels Olof Bouvin, Tobias Franke, Kaj Grønbæk, Mikkel B. Kjaergaard, Paul Lukowicz, and Markus Wüstenberg. 2013. On heterogeneity in mobile sensing applications aiming at representative data collection. In Proceedings of the ACM Conference on Pervasive and Ubiquitous Computing Adjunct Publication. ACM, 1087–1098.Google ScholarDigital Library
- Eoin Brophy, José Juan Dominguez Veiga, Zhengwei Wang, Alan F. Smeaton, and Tomas E. Ward. 2018. An interpretable machine vision approach to human activity recognition using photoplethysmograph sensor data. arXiv preprint arXiv:1812.00668 (2018).Google Scholar
- Andreas Bulling, Ulf Blanke, and Bernt Schiele. 2014. A tutorial on human activity recognition using body-worn inertial sensors. Comput. Surv. 46, 3 (2014), 33:1–33:33. DOI:https://doi.org/10.1145/2499621Google Scholar
- Ricardo Chavarriaga, Hesam Sagha, Alberto Calatroni, Sundara Tejaswi Digumarti, Gerhard Tröster, José del R. Millán, and Daniel Roggen. 2013. The opportunity challenge: A benchmark database for on-body sensor-based activity recognition. Pattern Recog. Lett. 34, 15 (2013), 2033–2042.Google ScholarDigital Library
- Chen Chen, Roozbeh Jafari, and Nasser Kehtarnavaz. 2015. UTD-MHAD: A multimodal dataset for human action recognition utilizing a depth camera and a wearable inertial sensor. In Proceedings of the IEEE International Conference on Image Processing (ICIP’15). IEEE, 168–172.Google ScholarDigital Library
- Kaixuan Chen, Lina Yao, Xianzhi Wang, Dalin Zhang, Tao Gu, Zhiwen Yu, and Zheng Yang. 2018. Interpretable parallel recurrent neural networks with convolutional attentions for multi-modality activity modeling. In Proceedings of the International Joint Conference on Neural Networks. IEEE, 1–8.Google ScholarCross Ref
- Kaixuan Chen, Lina Yao, Dalin Zhang, Xiaojun Chang, Guodong Long, and Sen Wang. 2019. Distributionally robust semi-supervised learning for people-centric sensing. In Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI’19). 3321–3328.Google ScholarCross Ref
- Kaixuan Chen, Lina Yao, Dalin Zhang, Bin Guo, and Zhiwen Yu. 2019. Multi-agent attentional activity recognition. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI’19). 1344–1350.Google ScholarDigital Library
- Kaixuan Chen, Lina Yao, Dalin Zhang, Xianzhi Wang, Xiaojun Chang, and Feiping Nie. 2020. A semisupervisedrecurrent convolutional attention model for human activity recognition. IEEE Trans. Neural Networks Learn. Syst. 31, 5 (2020), 1747--1756.Google ScholarCross Ref
- Ling Chen, Yi Zhang, and Liangying Peng. 2020. METIER: A deep multi-task learning based activity and user recognition model using wearable sensors. Proc. ACM Interact., Mob., Wear. Ubiq. Technol. 4, 1 (2020), 1–18.Google ScholarDigital Library
- Yiqiang Chen, Jindong Wang, Meiyu Huang, and Han Yu. 2019. Cross-position activity recognition with stratified transfer learning. Pervas. Mob. Comput. 57 (2019), 1–13.Google ScholarCross Ref
- Yuwen Chen, Kunhua Zhong, Ju Zhang, Qilong Sun, and Xueliang Zhao. 2016. LSTM networks for mobile human activity recognition. In Proceedings of the International Conference on Artificial Intelligence: Technologies and Applications. Atlantis Press.Google ScholarCross Ref
- Weihao Cheng, Sarah M. Erfani, Rui Zhang, and Ramamohanarao Kotagiri. 2018. Predicting complex activities from ongoing multivariate time series. In Proceedings of the 27th International Joint Conference on Artificial Intelligence. 3322–3328.Google ScholarCross Ref
- Belkacem Chikhaoui and Frank Gouineau. 2017. Towards automatic feature extraction for activity recognition from wearable sensors: A deep learning approach. In Proceedings of the IEEE 17th International Conference on Data Mining Workshops (ICDMW’17). IEEE, 693–702.Google ScholarCross Ref
- Jun-Ho Choi and Jong-Seok Lee. 2018. Confidence-based deep multimodal fusion for activity recognition. In Proceedings of the ACM International Joint Conference and International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers. ACM, 1548–1556.Google ScholarDigital Library
- Oscar Day and Taghi M. Khoshgoftaar. 2017. A survey on heterogeneous transfer learning. J. Big Data 4, 1 (2017), 29.Google ScholarCross Ref
- Sanorita Dey, Nirupam Roy, Wenyuan Xu, Romit Roy Choudhury, and Srihari Nelakuditi. 2014. AccelPrint: Imperfections of accelerometers make smartphones trackable. In Proceedings of the Network and Distributed System Security Symposium (NDSS’14).Google ScholarCross Ref
- Mingtao Dong, Jindong Han, Yuan He, and Xiaojun Jing. 2018. HAR-Net: Fusing deep representation and hand-crafted features for human activity recognition. In Proceedings of the International Conference on Signal and Information Processing, Networking and Computers. Springer, 32–40.Google Scholar
- Stefan Duffner, Samuel Berlemont, Grégoire Lefebvre, and Christophe Garcia. 2014. 3D gesture classification with convolutional neural networks. In Proceedings of the International Conference on Acoustics, Speech and Signal Processing. IEEE, 5432–5436.Google ScholarCross Ref
- Marcus Edel and Enrico Köppe. 2016. Binarized-BLSTM-RNN based human activity recognition. In Proceedings of the International Conference on Indoor Positioning and Indoor Navigation (IPIN’16). IEEE, 1–7.Google ScholarCross Ref
- Dumitru Erhan, Yoshua Bengio, Aaron Courville, Pierre-Antoine Manzagol, Pascal Vincent, and Samy Bengio. 2010. Why does unsupervised pre-training help deep learning? J. Mach. Learn. Res. 11, Feb. (2010), 625–660.Google Scholar
- Xiaoyi Fan, Wei Gong, and Jiangchuan Liu. 2018. TagFree activity identification with RFIDs. Proc. ACM Interact., Mob., Wear. Ubiq. Technol. 2, 1 (2018), 7.Google ScholarDigital Library
- Martin Gjoreski, Stefan Kalabakov, Mitja Luštrek, and Hristijan Gjoreski. 2019. Cross-dataset deep transfer learning for activity recognition. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the ACM International Symposium on Wearable Computers. ACM, 714–718.Google ScholarDigital Library
- Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In Proceedings of the International Conference on Advances in Neural Information Processing Systems. 2672–2680.Google Scholar
- Klaus Greff, Rupesh K. Srivastava, Jan Koutník, Bas R. Steunebrink, and Jürgen Schmidhuber. 2016. LSTM: A search space odyssey. IEEE Trans. Neural Netw. Learn. Syst. 28, 10 (2016), 2222–2232.Google ScholarCross Ref
- Rene Grzeszick, Jan Marius Lenk, Fernando Moya Rueda, Gernot A. Fink, Sascha Feldhorst, and Michael ten Hompel. 2017. Deep neural network based human activity recognition for the order picking process. In Proceedings of the 4th International Workshop on Sensor-based Activity Recognition and Interaction. ACM, 14.Google ScholarDigital Library
- Fuqiang Gu, Kourosh Khoshelham, Shahrokh Valaee, Jianga Shang, and Rui Zhang. 2018. Locomotion activity recognition using stacked denoising autoencoders. IEEE Internet Things J. 5, 3 (2018), 2085–2093.Google ScholarCross Ref
- Yu Guan and Thomas Plötz. 2017. Ensembles of deep LSTM learners for activity recognition using wearables. Proc. ACM Interact., Mob., Wear. Ubiq. Technol. 1, 2 (2017), 11.Google ScholarDigital Library
- Gautham Krishna Gudur, Prahalathan Sundaramoorthy, and Venkatesh Umaashankar. 2019. ActiveHARNet: Towards on-device deep Bayesian active learning for human activity recognition. arXiv preprint arXiv:1906.00108 (2019).Google Scholar
- Abdu Gumaei, Mohammad Mehedi Hassan, Abdulhameed Alelaiwi, and Hussain Alsalman. 2019. A hybrid deep learning model for human activity recognition using multimodal body sensing data. IEEE Access 7 (2019), 99152–99160.Google ScholarCross Ref
- Haodong Guo, Ling Chen, Liangying Peng, and Gencai Chen. 2016. Wearable sensor based multimodal human activity recognition exploiting the diversity of classifier ensemble. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 1112–1123.Google ScholarDigital Library
- Quang-Do Ha and Minh-Triet Tran. 2017. Activity recognition from inertial sensors with convolutional neural networks. In Proceedings of the International Conference on Future Data and Security Engineering. Springer, 285–298.Google ScholarCross Ref
- Sojeong Ha and Seungjin Choi. 2016. Convolutional neural networks for human activity recognition using multiple accelerometer and gyroscope sensors. In Proceedings of the International Joint Conference on Neural Networks. IEEE, 381–388.Google ScholarCross Ref
- Sojeong Ha, Jeong-Min Yun, and Seungjin Choi. 2015. Multi-modal convolutional neural networks for activity recognition. In Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics. IEEE, 3017–3022.Google ScholarDigital Library
- Nils Yannick Hammerla, James Fisher, Peter Andras, Lynn Rochester, Richard Walker, and Thomas Plötz. 2015. PD disease state assessment in naturalistic environments using deep learning. In Proceedings of the 29th AAAI Conference on Artificial Intelligence.Google Scholar
- Nils Y. Hammerla, Shane Halloran, and Thomas Plötz. 2016. Deep, convolutional, and recurrent models for human activity recognition using wearables. In Proceedings of the 25th International Joint Conference on Artificial Intelligence. 1533–1540.Google Scholar
- H. M. Hossain, MD Al Haiz Khan, and Nirmalya Roy. 2018. DeActive: Scaling activity recognition with active deep learning. Proc. ACM Interact., Mob., Wear. Ubiq. Technol. 2, 2 (2018), 66.Google ScholarDigital Library
- H. M. Hossain and Nirmalya Roy. 2019. Active deep learning for activity recognition with context aware annotator selection. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 1862–1870.Google ScholarDigital Library
- Tâm Huynh, Mario Fritz, and Bernt Schiele. 2008. Discovery of activity patterns using topic models. In Proceedings of the Conference on Ubiquitous Computing (UbiComp’08), Vol. 8. 10–19.Google ScholarDigital Library
- Tâm Huynh and Bernt Schiele. 2005. Analyzing features for activity recognition. In Proceedings of the Joint Conference on Smart Objects and Ambient Intelligence: Innovative Context-aware Services: Usages and Technologies. ACM, 159–163.Google ScholarDigital Library
- Shoya Ishimaru, Kensuke Hoshika, Kai Kunze, Koichi Kise, and Andreas Dengel. 2017. Towards reading trackers in the wild: Detecting reading activities by EOG glasses and deep neural networks. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the ACM International Symposium on Wearable Computers. ACM, 704–711.Google ScholarDigital Library
- Chihiro Ito, Xin Cao, Masaki Shuzo, and Eisaku Maeda. 2018. Application of CNN for human activity recognition with FFT spectrogram of acceleration and gyro sensors. In Proceedings of the ACM International Joint Conference and International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers. ACM, 1503–1510.Google ScholarDigital Library
- Yusuke Iwasawa, Kotaro Nakayama, Ikuko Yairi, and Yutaka Matsuo. 2017. Privacy issues regarding the application of DNNs to activity-recognition using wearables and its countermeasures by use of adversarial training. In Proceedings of the 26th International Joint Conference on Artificial Intelligence. 1930–1936.Google ScholarCross Ref
- Wenjun Jiang, Chenglin Miao, Fenglong Ma, Shuochao Yao, Yaqing Wang, Ye Yuan, Hongfei Xue, Chen Song, Xin Ma, Dimitrios Koutsonikolas, et al. 2018. Towards environment independent device free human activity recognition. In Proceedings of the 24th International Conference on Mobile Computing and Networking. ACM, 289–304.Google ScholarDigital Library
- Wenchao Jiang and Zhaozheng Yin. 2015. Human activity recognition using wearable sensors by deep convolutional neural networks. In Proceedings of the 23rd ACM International Conference on Multimedia. ACM, 1307–1310.Google ScholarDigital Library
- Artur Jordao, Antonio C. Nazare Jr, Jessica Sena, and William Robson Schwartz. 2018. Human activity recognition based on wearable sensor data: A standardization of the state-of-the-art. arXiv preprint arXiv:1806.05226 (2018).Google Scholar
- Andrej Karpathy, Justin Johnson, and Li Fei-Fei. 2016. Visualizing and understanding recurrent networks. In Proceedings of the 4th International Conference on Learning Representations Workshop.Google Scholar
- Md Abdullah Al Hafiz Khan, Nirmalya Roy, and Archan Misra. 2018. Scaling human activity recognition via deep learning-based domain adaptation. In Proceedings of the International Conference on Pervasive Computing and Communications. IEEE, 1–9.Google Scholar
- Shehroz S. Khan and Babak Taati. 2017. Detecting unseen falls from wearable devices using channel-wise ensemble of autoencoders. Exp. Syst. Applic. 87 (2017), 280–290.Google ScholarDigital Library
- Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2012. Imagenet classification with deep convolutional neural networks. In Proceedings of the International Conference on Advances in Neural Information Processing Systems. 1097–1105.Google Scholar
- Jennifer R. Kwapisz, Gary M. Weiss, and Samuel A. Moore. 2011. Activity recognition using cell phone accelerometers. ACM SIGKDD Explor. Newslett. 12, 2 (2011), 74–82.Google ScholarDigital Library
- Yongjin Kwon, Kyuchang Kang, and Changseok Bae. 2015. Analysis and evaluation of smartphone-based human activity recognition using a neural network approach. In Proceedings of the International Joint Conference on Neural Networks. IEEE, 1–5.Google ScholarCross Ref
- Nicholas D. Lane and Petko Georgiev. 2015. Can deep learning revolutionize mobile sensing? In Proceedings of the 16th International Workshop on Mobile Computing Systems and Applications. ACM, 117–122.Google Scholar
- Gierad Laput and Chris Harrison. 2019. Sensing fine-grained hand activity with smartwatches. In Proceedings of the CHI Conference on Human Factors in Computing Systems. ACM, 338.Google ScholarDigital Library
- Oscar D. Lara and Miguel A. Labrador. 2013. A survey on human activity recognition using wearable sensors. IEEE Commun. Surv. Tutor. 15, 3 (2013), 1192–1209.Google ScholarCross Ref
- Song-Mi Lee, Sang Min Yoon, and Heeryon Cho. 2017. Human activity recognition from accelerometer data using Convolutional Neural Network. In Proceedings of the IEEE International Conference on Big Data and Smart Computing (BigComp’17). IEEE, 131–134.Google Scholar
- Fei Li and Schahram Dustdar. 2011. Incorporating unsupervised learning in activity recognition. In Proceedings of the Workshops at the 25th AAAI Conference on Artificial Intelligence.Google Scholar
- Xinyu Li, Yuan He, and Xiaojun Jing. 2019. A survey of deep learning-based human activity recognition in radar. Remote Sens. 11, 9 (2019), 1068.Google ScholarCross Ref
- Xinyu Li, Yanyi Zhang, Mengzhu Li, Ivan Marsic, JaeWon Yang, and Randall S. Burd. 2016. Deep neural network for RFID-based activity recognition. In Proceedings of the 8th Wireless of the Students, by the Students, and for the Students Workshop (S3@MobiCom’16). ACM, 24–26.Google Scholar
- Xinyu Li, Yanyi Zhang, Ivan Marsic, Aleksandra Sarcevic, and Randall S. Burd. 2016. Deep learning for RFID-based activity recognition. In Proceedings of the 14th ACM Conference on Embedded Network Sensor Systems CD-ROM. ACM, 164–175.Google Scholar
- Xinyu Li, Yanyi Zhang, Jianyu Zhang, Shuhong Chen, Ivan Marsic, Richard A. Farneth, and Randall S. Burd. 2017. Concurrent activity recognition with multimodal CNN-LSTM structure. arXiv preprint arXiv:1702.01638 (2017).Google Scholar
- Jessica Lin, Eamonn Keogh, Stefano Lonardi, and Bill Chiu. 2003. A symbolic representation of time series, with implications for streaming algorithms. In Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery. ACM, 2–11.Google ScholarDigital Library
- Jonathan Long, Evan Shelhamer, and Trevor Darrell. 2015. Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 3431–3440.Google ScholarCross Ref
- Lingjuan Lyu, Xuanli He, Yee Wei Law, and Marimuthu Palaniswami. 2017. Privacy-preserving collaborative deep learning with application to human activity recognition. In Proceedings of the ACM on Conference on Information and Knowledge Management. ACM, 1219–1228.Google ScholarDigital Library
- Haojie Ma, Wenzhong Li, Xiao Zhang, Songcheng Gao, and Sanglu Lu. 2019. AttnSense: Multi-level attention mechanism for multimodal human activity recognition. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI’19). 3109–3115.Google ScholarCross Ref
- Yuchao Ma and Hassan Ghasemzadeh. 2019. LabelForest: Non-parametric semi-supervised learning for activity recognition. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 4520–4527.Google ScholarDigital Library
- Mohammad Malekzadeh, Richard G. Clegg, Andrea Cavallaro, and Hamed Haddadi. 2018. Protecting sensory data against sensitive inferences. In Proceedings of the 1st Workshop on Privacy by Design in Distributed Systems. ACM, 2.Google ScholarDigital Library
- Mohammad Malekzadeh, Richard G. Clegg, Andrea Cavallaro, and Hamed Haddadi. 2019. Mobile sensor data anonymization. In Proceedings of the International Conference on Internet of Things Design and Implementation. 49–58.Google ScholarDigital Library
- Akhil Mathur, Tianlin Zhang, Sourav Bhattacharya, Petar Veličković, Leonid Joffe, Nicholas D. Lane, Fahim Kawsar, and Pietro Lió. 2018. Using deep data augmentation training to address software and hardware heterogeneities in wearable and smartphone sensing devices. In Proceedings of the 17th ACM/IEEE International Conference on Information Processing in Sensor Networks. IEEE Press, 200–211.Google ScholarDigital Library
- Shinya Matsui, Nakamasa Inoue, Yuko Akagi, Goshu Nagino, and Koichi Shinoda. 2017. User adaptation of convolutional neural network for human activity recognition. In Proceedings of the 25th European Signal Processing Conference. IEEE, 753–757.Google ScholarCross Ref
- Taylor Mauldin, Marc Canby, Vangelis Metsis, Anne Ngu, and Coralys Rivera. 2018. SmartFall: A smartwatch-based fall detection system using deep learning. Sensors 18, 10 (2018), 3363.Google ScholarCross Ref
- Tomáš Mikolov, Martin Karafiát, Lukáš Burget, Jan Černockỳ, and Sanjeev Khudanpur. 2010. Recurrent neural network based language model. In Proceedings of the 11th Conference of the International Speech Communication Association.Google ScholarCross Ref
- Abdel-rahman Mohamed, George E. Dahl, and Geoffrey Hinton. 2011. Acoustic modeling using deep belief networks. IEEE Trans. Audio, Speech, Lang. Proc. 20, 1 (2011), 14–22.Google Scholar
- Francisco Javier Ordóñez Morales and Daniel Roggen. 2016. Deep convolutional feature transfer across mobile activity recognition domains, sensor modalities and locations. In Proceedings of the ACM International Symposium on Wearable Computers. ACM, 92–99.Google Scholar
- Sebastian Münzner, Philip Schmidt, Attila Reiss, Michael Hanselmann, Rainer Stiefelhagen, and Robert Dürichen. 2017. CNN-based sensor fusion techniques for multimodal human activity recognition. In Proceedings of the ACM International Symposium on Wearable Computers. ACM, 158–165.Google ScholarDigital Library
- Vishvak S. Murahari and Thomas Plötz. 2018. On attention models for human activity recognition. In Proceedings of the ACM International Symposium on Wearable Computers. ACM, 100–103.Google Scholar
- Harideep Nair, Cathy Tan, Ming Zeng, Ole J. Mengshoel, and John Paul Shen. 2019. AttriNet: Learning mid-level features for human activity recognition with deep belief networks. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the ACM International Symposium on Wearable Computers. ACM, 510–517.Google ScholarDigital Library
- Mark Nutter, Catherine H. Crawford, and Jorge Ortiz. 2018. Design of novel deep learning models for real-time human activity recognition with mobile phones. In Proceedings of the International Joint Conference on Neural Networks. IEEE, 1–8.Google ScholarCross Ref
- Henry Friday Nweke, Ying Wah Teh, Mohammed Ali Al-Garadi, and Uzoma Rita Alo. 2018. Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges. Exp. Syst. Applic. 105 (2018), 233–261.Google ScholarCross Ref
- Tsuyoshi Okita and Sozo Inoue. 2017. Recognition of multiple overlapping activities using compositional CNN-LSTM model. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the ACM International Symposium on Wearable Computers. ACM, 165–168.Google ScholarDigital Library
- Francisco Ordóñez and Daniel Roggen. 2016. Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition. Sensors 16, 1 (2016), 115.Google ScholarCross Ref
- Sinno Jialin Pan and Qiang Yang. 2009. A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22, 10 (2009), 1345–1359.Google ScholarDigital Library
- Liangying Peng, Ling Chen, Zhenan Ye, and Yi Zhang. 2018. AROMA: A deep multi-task learning based simple and complex human activity recognition method using wearable sensors. Proc. ACM Interact., Mob., Wear. Ubiq. Technol. 2, 2 (2018), 74.Google ScholarDigital Library
- Cuong Pham and Patrick Olivier. 2009. Slice&dice: Recognizing food preparation activities using embedded accelerometers. In Proceedings of the European Conference on Ambient Intelligence. Springer, 34–43.Google ScholarDigital Library
- NhatHai Phan, Yue Wang, Xintao Wu, and Dejing Dou. 2016. Differential privacy preservation for deep auto-encoders: An application of human behavior prediction. In Proceedings of the 30th AAAI Conference on Artificial Intelligence.Google Scholar
- Ivan Miguel Pires, Nuno Pombo, Nuno M. Garcia, and Francisco Flórez-Revuelta. 2018. Multi-sensor mobile platform for the recognition of activities of daily living and their environments based on artificial neural networks. In Proceedings of the 27th International Joint Conference on Artificial Intelligence. 5850–5852.Google ScholarCross Ref
- Thomas Plötz, Nils Y. Hammerla, and Patrick L. Olivier. 2011. Feature learning for activity recognition in ubiquitous computing. In Proceedings of the 22nd International Joint Conference on Artificial Intelligence.Google Scholar
- Samira Pouyanfar, Saad Sadiq, Yilin Yan, Haiman Tian, Yudong Tao, Maria Presa Reyes, Mei-Ling Shyu, Shu-Ching Chen, and S. S. Iyengar. 2018. A survey on deep learning: Algorithms, techniques, and applications. Comput. Surv. 51, 5 (2018), 92.Google Scholar
- Ofir Press, Amir Bar, Ben Bogin, Jonathan Berant, and Lior Wolf. 2017. Language generation with recurrent generative adversarial networks without pre-training. arXiv preprint arXiv:1706.01399 (2017).Google Scholar
- Hangwei Qian, Sinno Pan, and Chunyan Miao. 2018. Sensor-based activity recognition via learning from distributions. In Proceedings of the AAAI Conference on Artificial Intelligence.Google Scholar
- Hangwei Qian, Sinno Jialin Pan, Bingshui Da, and Chunyan Miao. 2019. A novel distribution-embedded neural network for sensor-based activity recognition. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI’19). 5614–5620.Google ScholarCross Ref
- Hangwei Qian, Sinno Jialin Pan, and Chunyan Miao. 2019. Distribution-based semi-supervised learning for activity recognition. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 7699–7706.Google ScholarCross Ref
- Valentin Radu, Nicholas D. Lane, Sourav Bhattacharya, Cecilia Mascolo, Mahesh K. Marina, and Fahim Kawsar. 2016. Towards multimodal deep learning for activity recognition on mobile devices. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct. ACM, 185–188.Google ScholarDigital Library
- Valentin Radu, Catherine Tong, Sourav Bhattacharya, Nicholas D. Lane, Cecilia Mascolo, Mahesh K. Marina, and Fahim Kawsar. 2018. Multimodal deep learning for activity and context recognition. Proc. ACM Interact., Mob., Wear. Ubiq. Technol. 1, 4 (2018), 157.Google ScholarDigital Library
- Daniele Ravi, Charence Wong, Benny Lo, and Guang-Zhong Yang. 2016. A deep learning approach to on-node sensor data analytics for mobile or wearable devices. IEEE J. Biomed. Health Inform. 21, 1 (2016), 56–64.Google ScholarCross Ref
- Daniele Ravi, Charence Wong, Benny Lo, and Guang-Zhong Yang. 2016. Deep learning for human activity recognition: A resource efficient implementation on low-power devices. In Proceedings of the IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN’16). IEEE, 71–76.Google ScholarCross Ref
- Attila Reiss and Didier Stricker. 2012. Introducing a new benchmarked dataset for activity monitoring. In Proceedings of the 16th International Symposium on Wearable Computers. IEEE, 108–109.Google ScholarDigital Library
- Jorge-L. Reyes-Ortiz, Luca Oneto, Albert Samà, Xavier Parra, and Davide Anguita. 2016. Transition-aware human activity recognition using smartphones. Neurocomputing 171 (2016), 754–767.Google ScholarDigital Library
- Daniele Riboni, Linda Pareschi, Laura Radaelli, and Claudio Bettini. 2011. Is ontology-based activity recognition really effective? In Proceedings of the IEEE International Conference on Pervasive Computing and Communications Workshops. IEEE, 427–431.Google ScholarCross Ref
- Daniel Roggen, Alberto Calatroni, Mirco Rossi, Thomas Holleczek, Kilian Förster, Gerhard Tröster, Paul Lukowicz, David Bannach, Gerald Pirkl, Alois Ferscha, et al. 2010. Collecting complex activity datasets in highly rich networked sensor environments. In Proceedings of the 7th International Conference on Networked Sensing Systems (INSS’10). IEEE, 233–240.Google ScholarCross Ref
- Seyed Ali Rokni, Marjan Nourollahi, and Hassan Ghasemzadeh. 2018. Personalized human activity recognition using convolutional neural networks. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence.Google Scholar
- Charissa Ann Ronao and Sung-Bae Cho. 2015. Deep convolutional neural networks for human activity recognition with smartphone sensors. In Proceedings of the International Conference on Neural Information Processing. Springer, 46–53.Google ScholarCross Ref
- Charissa Ann Ronao and Sung-Bae Cho. 2016. Human activity recognition with smartphone sensors using deep learning neural networks. Exp. Syst. Applic. 59 (2016), 235–244.Google ScholarDigital Library
- Silvia Rossi, Roberto Capasso, Giovanni Acampora, and Mariacarla Staffa. 2018. A multimodal deep learning network for group activity recognition. In Proceedings of the International Joint Conference on Neural Networks. IEEE, 1–6.Google ScholarCross Ref
- Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al. 2015. Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115, 3 (2015), 211–252.Google ScholarDigital Library
- Ramyar Saeedi, Skyler Norgaard, and Assefaw H. Gebremedhin. 2017. A closed-loop deep learning architecture for robust activity recognition using wearable sensors. In Proceedings of the IEEE International Conference on Big Data. IEEE, 473–479.Google Scholar
- Jeffrey C. Schlimmer and Richard H. Granger. 1986. Incremental learning from noisy data. Mach. Learn. 1, 3 (1986), 317–354.Google ScholarCross Ref
- Sofia Serrano and Noah A. Smith. 2019. Is attention interpretable? In Proceedings of the 57th Conference of the Association for Computational Linguistics (ACL’19). 2931–2951.Google Scholar
- Yu-Han Shen, Ke-Xin He, and Wei-Qiang Zhang. 2018. SAM-GCNN: A gated convolutional neural network with segment-level attention mechanism for home activity monitoring. In Proceedings of the IEEE International Symposium on Signal Processing and Information Technology (ISSPIT’18). IEEE, 679–684.Google ScholarCross Ref
- Muhammad Shoaib, Stephan Bosch, Ozlem Incel, Hans Scholten, and Paul Havinga. 2014. Fusion of smartphone motion sensors for physical activity recognition. Sensors 14, 6 (2014), 10146–10176.Google ScholarCross Ref
- Geetika Singla, Diane J. Cook, and Maureen Schmitter-Edgecombe. 2010. Recognizing independent and joint activities among multiple residents in smart environments. J. Amb. Intell. Human. Comput. 1, 1 (2010), 57–63.Google ScholarCross Ref
- Elnaz Soleimani and Ehsan Nazerfard. 2019. Cross-subject transfer learning in human activity recognition systems using generative adversarial networks. arXiv preprint arXiv:1903.12489 (2019).Google Scholar
- Maja Stikic, Kristof Van Laerhoven, and Bernt Schiele. 2008. Exploring semi-supervised and active learning for activity recognition. In Proceedings of the 12th IEEE International Symposium on Wearable Computers. IEEE, 81–88.Google ScholarDigital Library
- Allan Stisen, Henrik Blunck, Sourav Bhattacharya, Thor Siiger Prentow, Mikkel Baun Kjærgaard, Anind Dey, Tobias Sonne, and Mads Møller Jensen. 2015. Smart devices are different: Assessing and mitigating mobile sensing heterogeneities for activity recognition. In Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems. ACM, 127–140.Google ScholarDigital Library
- Yujin Tang, Jianfeng Xu, Kazunori Matsumoto, and Chihiro Ono. 2016. Sequence-to-sequence model with attention for time series classification. In Proceedings of the 16th International Conference on Data Mining Workshops. IEEE, 503–510.Google ScholarCross Ref
- Dapeng Tao, Yonggang Wen, and Richang Hong. 2016. Multicolumn bidirectional long short-term memory for mobile devices-based human activity recognition. IEEE Internet Things J. 3, 6 (2016), 1124–1134.Google ScholarCross Ref
- Luan Tran, Xi Yin, and Xiaoming Liu. 2017. Disentangled representation learning GAN for pose-invariant face recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1415–1424.Google ScholarCross Ref
- Son N. Tran, Qing Zhang, Vanessa Smallbon, and Mohan Karunanithi. 2018. Multi-resident activity monitoring in smart homes: A case study. In Proceedings of the IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops’18). IEEE, 698–703.Google ScholarCross Ref
- Tim L. M. van Kasteren, Gwenn Englebienne, and Ben J. A. Kröse. 2011. Human activity recognition from wireless sensor network data: Benchmark and software. In Activity Recognition in Pervasive Intelligent Environments. Springer, 165–186.Google Scholar
- Alireza Abedin Varamin, Ehsan Abbasnejad, Qinfeng Shi, Damith C. Ranasinghe, and Hamid Rezatofighi. 2018. 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. ACM, 246–253.Google ScholarDigital Library
- George Vavoulas, Charikleia Chatzaki, Thodoris Malliotakis, Matthew Pediaditis, and Manolis Tsiknakis. 2016. The MobiAct dataset: Recognition of activities of daily living using smartphones. In Proceedings of the International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AgeingWell’16). 143–151.Google ScholarCross Ref
- Praneeth Vepakomma, Debraj De, Sajal K. Das, and Shekhar Bhansali. 2015. A-Wristocracy: Deep learning on wrist-worn sensing for recognition of user complex activities. In Proceedings of the IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN’15). 1–6.Google ScholarCross Ref
- Toan H. Vu, An Dang, Le Dung, and Jia-Ching Wang. 2017. Self-gated recurrent neural networks for human activity recognition on wearable devices. In Proceedings of the Thematic Workshops of ACM Multimedia. ACM, 179–185.Google ScholarDigital Library
- Jiwei Wang, Yiqiang Chen, Yang Gu, Yunlong Xiao, and Haonan Pan. 2018. SensoryGANs: An effective generative adversarial framework for sensor-based human activity recognition. In Proceedings of the International Joint Conference on Neural Networks. IEEE, 1–8.Google ScholarCross Ref
- Jindong Wang, Yiqiang Chen, Shuji Hao, Xiaohui Peng, and Lisha Hu. 2019. Deep learning for sensor-based activity recognition: A survey. Pattern Recog. Lett. 119 (2019), 3–11.Google ScholarDigital Library
- Jindong Wang, Vincent W. Zheng, Yiqiang Chen, and Meiyu Huang. 2018. Deep transfer learning for cross-domain activity recognition. In Proceedings of the 3rd International Conference on Crowd Science and Engineering. ACM, 16.Google ScholarDigital Library
- Yanwen Wang, Jiaxing Shen, and Yuanqing Zheng. 2020. Push the limit of acoustic gesture recognition. In 39th IEEE Conference on Computer Communications (INFOCOM’20). IEEE, 566--575.Google ScholarDigital Library
- Sungpil Woo, Jaewook Byun, Seonghoon Kim, Hoang Minh Nguyen, Janggwan Im, and Daeyoung Kim. 2016. RNN-based personalized activity recognition in multi-person environment using RFID. In Proceedings of the IEEE International Conference on Computer and Information Technology (CIT’16). IEEE, 708–715.Google ScholarCross Ref
- Rui Xi, Mengshu Hou, Mingsheng Fu, Hong Qu, and Daibo Liu. 2018. Deep dilated convolution on multimodality time series for human activity recognition. In Proceedings of the International Joint Conference on Neural Networks. IEEE, 1–8.Google ScholarCross Ref
- Rui Xi, Ming Li, Mengshu Hou, Mingsheng Fu, Hong Qu, Daibo Liu, and Charles R. Haruna. 2018. Deep dilation on multimodality time series for human activity recognition. IEEE Access 6 (2018), 53381–53396.Google ScholarCross Ref
- Cheng Xu, Duo Chai, Jie He, Xiaotong Zhang, and Shihong Duan. 2019. InnoHAR: A deep neural network for complex human activity recognition. IEEE Access 7 (2019), 9893–9902.Google ScholarCross Ref
- Li Xue, Si Xiandong, Nie Lanshun, Li Jiazhen, Ding Renjie, Zhan Dechen, and Chu Dianhui. 2018. Understanding and improving deep neural network for activity recognition. arXiv preprint arXiv:1805.07020 (2018).Google Scholar
- Jianbo Yang, Minh Nhut Nguyen, Phyo Phyo San, Xiao Li Li, and Shonali Krishnaswamy. 2015. Deep convolutional neural networks on multichannel time series for human activity recognition. In Proceedings of the 24th International Joint Conference on Artificial Intelligence.Google ScholarDigital Library
- Yang Yang, Chunping Hou, Yue Lang, Dai Guan, Danyang Huang, and Jinchen Xu. 2019. Open-set human activity recognition based on micro-Doppler signatures. Pattern Recog. 85 (2019), 60–69.Google ScholarCross Ref
- Zhan Yang, Osolo Ian Raymond, Chengyuan Zhang, Ying Wan, and Jun Long. 2018. DFTerNet: Towards 2-bit dynamic fusion networks for accurate human activity recognition. IEEE Access 6 (2018), 56750–56764.Google ScholarCross Ref
- Lina Yao, Feiping Nie, Quan Z. Sheng, Tao Gu, Xue Li, and Sen Wang. 2016. Learning from less for better: Semi-supervised activity recognition via shared structure discovery. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing. 13–24.Google ScholarDigital Library
- Rui Yao, Guosheng Lin, Qinfeng Shi, and Damith C. Ranasinghe. 2018. Efficient dense labelling of human activity sequences from wearables using fully convolutional networks. Pattern Recog. 78 (2018), 252–266.Google ScholarCross Ref
- Shuochao Yao, Shaohan Hu, Yiran Zhao, Aston Zhang, and Tarek Abdelzaher. 2017. Deepsense: A unified deep learning framework for time-series mobile sensing data processing. In Proceedings of the 26th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 351–360.Google ScholarDigital Library
- Yuta Yuki, Junto Nozaki, Kei Hiroi, Katsuhiko Kaji, and Nobuo Kawaguchi. 2018. Activity recognition using dual-ConvLSTM extracting local and global features for SHL recognition challenge. In Proceedings of the ACM International Joint Conference and International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers. ACM, 1643–1651.Google ScholarDigital Library
- Piero Zappi, Clemens Lombriser, Thomas Stiefmeier, Elisabetta Farella, Daniel Roggen, Luca Benini, and Gerhard Tröster. 2008. Activity recognition from on-body sensors: Accuracy-power trade-off by dynamic sensor selection. In Proceedings of the European Conference on Wireless Sensor Networks. Springer, 17–33.Google ScholarCross Ref
- Tahmina Zebin, Patricia J. Scully, and Krikor B. Ozanyan. 2016. Human activity recognition with inertial sensors using a deep learning approach. In Proceedings of the IEEE Conference on Sensors (SENSORS’16). IEEE, 1–3.Google Scholar
- Ming Zeng, Haoxiang Gao, Tong Yu, Ole J. Mengshoel, Helge Langseth, Ian Lane, and Xiaobing Liu. 2018. Understanding and improving recurrent networks for human activity recognition by continuous attention. In Proceedings of the ACM International Symposium on Wearable Computers. ACM, 56–63.Google ScholarDigital Library
- Ming Zeng, Le T. Nguyen, Bo Yu, Ole J. Mengshoel, Jiang Zhu, Pang Wu, and Joy Zhang. 2014. Convolutional neural networks for human activity recognition using mobile sensors. In Proceedings of the 6th International Conference on Mobile Computing, Applications and Services. IEEE, 197–205.Google ScholarCross Ref
- Ming Zeng, Tong Yu, Xiao Wang, Le T. Nguyen, Ole J. Mengshoel, and Ian Lane. 2017. Semi-supervised convolutional neural networks for human activity recognition. In Proceedings of the IEEE International Conference on Big Data. IEEE, 522–529.Google ScholarCross Ref
- Dalin Zhang, Kaixuan Chen, Debao Jian, and Lina Yao. 2020. Motor imagery classification via temporal attention cues of graph embedded EEG signals. IEEE J. Biomed. Health Inform. 24, 9 (2020), 2570–2579.Google ScholarCross Ref
- Dalin Zhang, Lina Yao, Kaixuan Chen, Guodong Long, and Sen Wang. 2019. Collective protection: Preventing sensitive inferences via integrative transformation. In Proceedings of the 19th IEEE International Conference on Data Mining. IEEE, 1–6.Google ScholarCross Ref
- Dalin Zhang, Lina Yao, Kaixuan Chen, and Jessica Monaghan. 2019. A convolutional recurrent attention model for subject-independent eeg signal analysis. IEEE Sig. Proc. Lett. 26, 5 (2019), 715–719.Google ScholarCross Ref
- Dalin Zhang, Lina Yao, Kaixuan Chen, and Sen Wang. 2018. Ready for use: Subject-independent movement intention recognition via a convolutional attention model. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. ACM, 1763–1766.Google ScholarDigital Library
- Dalin Zhang, Lina Yao, Kaixuan Chen, Sen Wang, Xiaojun Chang, and Yunhao Liu. 2019. Making sense of spatio-temporal preserving representations for EEG-based human intention recognition. IEEE Trans. Cyber. 50, 7 (2019), 3033–3044.Google ScholarCross Ref
- Dalin Zhang, Lina Yao, Kaixuan Chen, Sen Wang, Pari Delir Haghighi, and Caley Sullivan. 2019. A graph-based hierarchical attention model for movement intention detection from EEG signals. IEEE Trans. Neural Syst. Rehab. Eng. 27, 11 (2019), 2247–2253.Google ScholarCross Ref
- Dalin Zhang, Lina Yao, Xiang Zhang, Sen Wang, Weitong Chen, Robert Boots, and Boualem Benatallah. 2018. Cascade and parallel convolutional recurrent neural networks on EEG-based intention recognition for brain computer interface. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 32.Google ScholarCross Ref
- Mi Zhang and Alexander A. Sawchuk. 2012. USC-HAD: A daily activity dataset for ubiquitous activity recognition using wearable sensors. In Proceedings of the ACM Conference on Ubiquitous Computing. ACM, 1036–1043.Google Scholar
- Xiang Zhang, Lina Yao, Chaoran Huang, Sen Wang, Mingkui Tan, Guodong Long, and Can Wang. 2018. Multi-modality sensor data classification with selective attention. In Proceedings of the 27th International Joint Conference on Artificial Intelligence.Google ScholarDigital Library
- Xiang Zhang, Lina Yao, and Feng Yuan. 2019. Adversarial variational embedding for robust semi-supervised learning. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 139–147.Google ScholarDigital Library
- Yanyi Zhang, Xinyu Li, Jianyu Zhang, Shuhong Chen, Moliang Zhou, Richard A. Farneth, Ivan Marsic, and Randall S. Burd. 2017. Car—A deep learning structure for concurrent activity recognition. In Proceedings of the 16th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN’17). IEEE, 299–300.Google Scholar
- Yong Zhang, Yu Zhang, Zhao Zhang, Jie Bao, and Yunpeng Song. 2018. Human activity recognition based on time series analysis using U-Net. arXiv preprint arXiv:1809.08113 (2018).Google Scholar
- Yi Zheng, Qi Liu, Enhong Chen, Yong Ge, and J. Leon Zhao. 2014. Time series classification using multi-channels deep convolutional neural networks. In Proceedings of the International Conference on Web-age Information Management. Springer, 298–310.Google Scholar
- Yue Zheng, Yi Zhang, Kun Qian, Guidong Zhang, Yunhao Liu, Chenshu Wu, and Zheng Yang. 2019. Zero-effort cross-domain gesture recognition with Wi-Fi. In Proceedings of the 17th International Conference on Mobile Systems, Applications, and Services. ACM, 313–325.Google ScholarDigital Library
- Jun-Yan Zhu and Jim Foley. 2019. Learning to synthesize and manipulate natural images. IEEE Comput. Graph. Applic. 39, 2 (2019), 14–23.Google ScholarCross Ref
- Han Zou, Yuxun Zhou, Jianfei Yang, Hao Jiang, Lihua Xie, and Costas J. Spanos. 2018. DeepSense: Device-free human activity recognition via autoencoder long-term recurrent convolutional network. In Proceedings of the International Conference on Communications (ICC’18). IEEE, 1–6.Google Scholar
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