A. Baez-miranda, B. Caffiau, S. Garbay, C. Portet, and F. , Task based model for récit generation from sensor data: an early experiment, 5th International Workshop on Computational Models of Narrative, pp.1-10, 2014.

M. B. Abidine and B. Fergani, A new multi-class WSVM classification to imbalanced human activity dataset, JCP, pp.1560-1565, 2014.

A. Aker and R. J. Gaizauskas, Generating image descriptions using dependency relational patterns, ACL 2010, Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp.1250-1258, 2010.

K. Altun and B. Barshan, Human activity recognition using inertial/magnetic sensor units, Human Behavior Understanding, First International Workshop, HBU 2010, pp.38-51, 2010.

P. M. Andersen, P. J. Hayes, A. K. Huettner, L. M. Schmandt, I. B. Nirenburg et al., Automatic extraction of facts from press releases to generate news stories, Proceedings of the Third Conference on Applied Natural Language Processing, ANLC '92, pp.170-177, 1992.

D. Anguita, A. Ghio, L. Oneto, X. Parra, and J. L. Reyes-ortiz, Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine, Proceedings of the 4th International Conference on Ambient Assisted Living and Home Care, IWAAL'12, pp.216-223, 2012.

D. Anguita, A. Ghio, L. Oneto, X. Parra, and J. L. Reyes-ortiz, Energy efficient smartphone-based activity recognition using fixed-point arithmetic, J. UCS, vol.19, issue.9, pp.1295-1314, 2013.

D. Arifoglu and A. Bouchachia, Activity recognition and abnormal behaviour detection with recurrent neural networks, 14th International Conference on Mobile Systems and Pervasive Computing (MobiSPC 2017) / 12th International Conference on Future Networks and Communications (FNC 2017) / Affiliated Workshops, pp.86-93, 2017.

N. Balasubramanian, S. Soderland, . Mausam, and O. Etzioni, Generating coherent event schemas at scale, Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp.1721-1731, 2013.

L. Bao and S. S. Intille, Activity recognition from user-annotated acceleration data, Pervasive Computing, Second International Conference, pp.1-17, 2004.

E. Batbaatar, M. Li, and K. H. Ryu, Semantic-emotion neural network for emotion recognition from text, IEEE Access, vol.7, pp.111866-111878, 2019.

A. Bayat, M. Pomplun, and D. A. Tran, A study on human activity recognition using accelerometer data from smartphones, The 9th International Conference on Future Networks and Communications (FNC'14)/The 11th International Conference on Mobile Systems and Pervasive Computing (MobiSPC'14)/Affiliated Workshops, vol.34, pp.450-457, 2014.

Y. Bengio, Deep learning of representations: Looking forward, Statistical Language and Speech Processing -First International Conference, SLSP 2013, pp.1-37, 2013.

M. Berchtold, M. Budde, D. Gordon, H. R. Schmidtke, and M. Beigl, Actiserv: Activity recognition service for mobile phones, 14th IEEE International Symposium on Wearable Computers (ISWC 2010), pp.1-8, 2010.

M. Berchtold, M. Budde, D. Gordon, H. R. Schmidtke, and M. Beigl, Actiserv: Activity recognition service for mobile phones, 14th IEEE International Symposium on Wearable Computers (ISWC 2010), pp.1-8, 2010.

S. Bethard, G. Savova, M. Palmer, and J. Pustejovsky, Semeval-2017 task 12: Clinical tempeval, Proceedings of the 11th International Workshop on Semantic Evaluation, pp.565-572, 2017.

A. Bevilacqua, K. Macdonald, A. Rangarej, V. Widjaya, B. Caulfield et al., Human activity recognition with convolutional neural networks, Machine Learning and Knowledge Discovery in Databases -European Conference, ECML PKDD 2018, pp.541-552, 2018.

A. Biswas and D. W. Jacobs, Active image clustering: Seeking constraints from humans to complement algorithms, CVPR, pp.2152-2159, 2012.

D. Blachon, D. Coskun, and F. Portet, On-line context aware physical activity recognition from the accelerometer and audio sensors of smartphones, Lecture Notes in Computer Science, vol.8850, pp.205-220, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01082580

D. Britz, M. Y. Guan, and M. Luong, Efficient attention using a fixed-size memory representation, Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp.392-400, 2017.

P. Chahuara, A. Fleury, F. Portet, and M. Vacher, On-line Human Activity Recognition from Audio and Home Automation Sensors: comparison of sequential and non-sequential models in realistic Smart Homes, Journal of ambient intelligence and smart environments, vol.8, issue.4, pp.399-422, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01336552

N. Chambers and D. Jurafsky, Unsupervised learning of narrative event chains, ACL 2008, Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics, pp.789-797, 2008.

V. Chaparro, A. Gomez, A. Salgado, O. L. Quintero, N. López et al., Emotion recognition from EEG and facial expressions: a multimodal approach, p.40, 2018.

, Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp.530-533, 2018.

N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, Smote: Synthetic minority over-sampling technique, J. Artif. Int. Res, pp.321-357, 2002.

D. Chen and M. Q. Meng, Health status detection for patients in physiological monitoring, 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp.4921-4924, 2011.

G. Chen, A. Wang, S. Zhao, L. Liu, C. et al., Latent feature learning for activity recognition using simple sensors in smart homes, Multimedia Tools Appl, vol.77, issue.12, pp.15201-15219, 2018.

Y. Chen, J. Yang, S. Liou, G. Lee, W. et al., Online classifier construction algorithm for human activity detection using a tri-axial accelerometer, Applied Mathematics and Computation, vol.205, issue.2, pp.849-860, 2008.

Y. Chen, J. Yang, S. Liou, G. Lee, and J. Wang, Online classifier construction algorithm for human activity detection using a tri-axial accelerometer, 2008.

, Special Issue on Advanced Intelligent Computing Theory and Methodology in Applied Mathematics and Computation, vol.205, pp.849-860

R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu et al., Natural language processing (almost) from scratch, Journal of Machine Learning Research, vol.12, pp.2493-2537, 2011.

P. Dasigi and E. Hovy, Modeling newswire events using neural networks for anomaly detection, Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, pp.1414-1422, 2014.

O. Du?ek, J. Novikova, and V. Rieser, Findings of the E2E NLG Challenge, Proceedings of the 11th International Conference on Natural Language Generation, 2018.

M. Edel and E. Koppe, Binarized-blstm-rnn based human activity recognition, International Conference on Indoor Positioning and Indoor Navigation, pp.1-7, 2016.

S. Ertekin, Adaptive oversampling for imbalanced data classification, Information Sciences and Systems 2013 -Proceedings of the 28th International Symposium on Computer and Information Sciences, pp.261-269, 2013.

S. Ertekin, J. Huang, L. Bottou, G. , and L. , Learning on the border: Active learning in imbalanced data classification, Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management, CIKM '07, pp.127-136, 2007.

S. Ertekin, J. Huang, and C. L. Giles, Active learning for class imbalance problem, Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR '07, pp.823-824, 2007.

A. Farhadi, S. M. Hejrati, M. A. Sadeghi, P. Young, C. Rashtchian et al., Every picture tells a story: Generating sentences from images, Computer Vision -ECCV 2010, 11th European Conference on Computer Vision, pp.15-29, 2010.

Y. Feng and M. Lapata, How many words is a picture worth? automatic caption generation for news images, ACL 2010, Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp.1239-1249, 2010.

L. Frermann, I. Titov, and M. Pinkal, A hierarchical bayesian model for unsupervised induction of script knowledge, Proceedings of the 14th Conference of the European Chapter, pp.49-57, 2014.

M. Granroth-wilding and S. Clark, What happens next? event prediction using a compositional neural network model, Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, pp.2727-2733, 2016.

S. Guadarrama, N. Krishnamoorthy, G. Malkarnenkar, S. Venugopalan, R. J. Mooney et al., Youtube2text: Recognizing and describing arbitrary activities using semantic hierarchies and zero-shot recognition, IEEE International Conference on Computer Vision, ICCV 2013, pp.2712-2719, 2013.

S. Ha and S. Choi, Convolutional neural networks for human activity recognition using multiple accelerometer and gyroscope sensors, 2016 International Joint Conference on Neural Networks, IJCNN 2016, pp.381-388, 2016.

S. Ha and S. Choi, Convolutional neural networks for human activity recognition using multiple accelerometer and gyroscope sensors, 2016 International Joint Conference on Neural Networks, IJCNN 2016, pp.381-388, 2016.

N. Y. Hammerla, J. Fisher, P. Andras, L. Rochester, R. Walker et al., , 2015.

, PD disease state assessment in naturalistic environments using deep learning, Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, pp.1742-1748, 2015.

N. Y. Hammerla, S. Halloran, and T. Plötz, Deep, convolutional, and recurrent models for human activity recognition using wearables, Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI 2016, pp.1533-1540, 2016.

H. Han, W. Wang, and B. Mao, Borderline-smote: A new over-sampling method in imbalanced data sets learning, Proceedings of the 2005 International Conference on Advances in Intelligent Computing -Volume Part I, ICIC'05, pp.878-887, 2005.

Y. Hanai, Y. Hori, J. Nishimura, and T. Kuroda, A versatile recognition processor employing haar-like feature and cascaded classifier, IEEE International Solid-State Circuits Conference, pp.148-149, 2009.

B. Harrison, S. Banerjee, and M. O. Riedl, Learning from stories: using natural communication to train believable agents, IJCAI 2016 Workshop on Interactive Machine Learning, 2016.

H. He and E. A. Garcia, Learning from imbalanced data, IEEE Trans. on Knowl. and Data Eng, vol.21, issue.9, pp.1263-1284, 2009.

Z. He and L. Jin, Activity recognition from acceleration data based on discrete consine transform and SVM, Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, pp.5041-5044, 2009.

G. E. Hinton, S. Osindero, and Y. W. Teh, A fast learning algorithm for deep belief nets, Neural Computation, vol.18, issue.7, pp.1527-1554, 2006.

S. Hochreiter and J. Schmidhuber, Long short-term memory, Neural Computation, vol.9, issue.8, pp.1735-1780, 1997.

L. Hu, J. Li, L. Nie, X. Li, and C. Shao, What happens next? future subevent prediction using contextual hierarchical LSTM, Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, pp.3450-3456, 2017.

J. Hunter, Y. Freer, A. Gatt, E. Reiter, S. Sripada et al., Automatic generation of natural language nursing shift summaries in neonatal intensive care: Btnurse, Artificial Intelligence in Medicine, vol.56, issue.3, pp.157-172, 2012.

A. Ignatov, Real-time human activity recognition from accelerometer data using convolutional neural networks, Appl. Soft Comput, vol.62, pp.915-922, 2018.

S. Inoue, N. Ueda, Y. Nohara, and N. Nakashima, Mobile activity recognition for a whole day: recognizing real nursing activities with big dataset, Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp.1269-1280, 2015.

P. S. Jacobs and L. F. Rau, Scisor: Extracting information from on-line news, Commun. ACM, vol.33, issue.11, pp.88-97, 1990.

B. Jans, S. Bethard, I. Vulic, and M. Moens, Skip n-grams and ranking functions for predicting script events, EACL 2012, 13th Conference of the European Chapter of the Association for Computational Linguistics, pp.336-344, 2012.

Y. Jia, Diatetic and exercise therapy against diabetes mellitus, Second International Conference on Intelligent Networks and Intelligent Systems, pp.693-696, 2009.

W. Jiang and Z. Yin, Human activity recognition using wearable sensors by deep convolutional neural networks, Proceedings of the 23rd ACM International Conference on Multimedia, MM '15, pp.1307-1310, 2015.

W. Jiang and Z. Yin, Human activity recognition using wearable sensors by deep convolutional neural networks, Proceedings of the 23rd Annual ACM Conference on Multimedia Conference, MM '15, pp.1307-1310, 2015.

Y. Kashimoto, T. Morita, M. Fujimoto, Y. Arakawa, H. Suwa et al., Sensing activities and locations of senior citizens toward automatic daycare report generation, 31st IEEE International Conference on Advanced Information Networking and Applications, pp.174-181, 2017.

A. M. Khan, Y. Lee, S. Y. Lee, and T. Kim, A triaxial accelerometer-based physical-activity recognition via augmented-signal features and a hierarchical recognizer, 2010.

, Trans. Info. Tech. Biomed, vol.14, issue.5, pp.1166-1172

A. M. Khan, A. Tufail, A. M. Khattak, and T. H. Laine, Activity recognition on smartphones via sensor-fusion and kda-based svms, p.10, 2014.

Y. Kim and B. Toomajian, Hand gesture recognition using micro-doppler signatures with convolutional neural network, IEEE Access, vol.4, pp.7125-7130, 2016.

D. P. Kingma and J. Ba, Adam: A method for stochastic optimization, 3rd International Conference on Learning Representations, 2015.

G. Kulkarni, V. Premraj, S. Dhar, S. Li, Y. Choi et al., Baby talk: Understanding and generating simple image descriptions, The 24th IEEE Conference on Computer Vision and Pattern Recognition, pp.1601-1608, 2011.

P. Kuznetsova, V. Ordonez, A. C. Berg, T. L. Berg, and Y. Choi, Collective generation of natural image descriptions, The 50th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, vol.1, pp.359-368, 2012.

J. R. Kwapisz, G. M. Weiss, and S. A. Moore, Activity recognition using cell phone accelerometers, SIGKDD Explor. Newsl, vol.12, issue.2, pp.74-82, 2011.

O. D. Lara and M. A. Labrador, A mobile human activity recognition system, 2012 IEEE Consumer Communications and Networking Conference (CCNC), pp.38-39, 2012.

O. D. Lara and M. A. Labrador, A Survey on Human Activity Recognition using Wearable Sensors, IEEE Communications Surveys & Tutorials, vol.15, issue.3, pp.1192-1209, 2013.

Ó. D. Lara, A. J. Pérez, M. A. Labrador, and J. D. Posada, Centinela: A human activity recognition system based on acceleration and vital sign data, Pervasive and Mobile Computing, vol.8, issue.5, pp.717-729, 2012.

S. Lee and K. Mase, Activity and location recognition using wearable sensors, IEEE Pervasive Computing, vol.1, issue.3, pp.24-32, 2002.

B. Li and M. O. Riedl, Scheherazade: Crowd-powered interactive narrative generation, Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, pp.4305-4306, 2015.

Y. Li, D. Shi, B. Ding, and D. Liu, Unsupervised feature learning for human activity recognition using smartphone sensors, Mining Intelligence and Knowledge Exploration -Second International Conference, pp.99-107, 2014.

C. Liu, L. Zhang, Z. Liu, K. Liu, X. Li et al., Lasagna: towards deep hierarchical understanding and searching over mobile sensing data, Proceedings of the 22nd Annual International Conference on Mobile Computing and Networking, pp.334-347, 2016.

M. Ma, H. Fan, and K. M. Kitani, Going deeper into first-person activity recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp.1894-1903, 2016.

S. T. Mai, I. Assent, and M. Storgaard, AnyDBC: An Efficient Anytime Densitybased Clustering Algorithm for Very Large Complex Datasets, KDD, pp.1025-1034, 2016.

L. J. Martin, P. Ammanabrolu, X. Wang, W. Hancock, S. Singh et al., Event representations for automated story generation with deep neural nets, AAAI, 2018.

D. Micucci, M. Mobilio, and P. Napoletano, Unimib shar: a new dataset for human activity recognition using acceleration data from smartphones, Applied Sciences, vol.7, 2017.

T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean, Distributed representations of words and phrases and their compositionality, Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held, pp.3111-3119, 2013.

B. B. Miranda, Génération de récits à partir de données ambiantes. (Generating stories from ambient data), 2018.

T. Miyanishi, J. Hirayama, T. Maekawa, and M. Kawanabe, Generating an event timeline about daily activities from a semantic concept stream, Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), pp.142-150, 2018.

A. Modi, Event embeddings for semantic script modeling, Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning, pp.75-83, 2016.

A. Modi, T. Anikina, S. Ostermann, and M. Pinkal, Inscript: Narrative texts annotated with script information, Proceedings of the Tenth International Conference on Language Resources and Evaluation LREC, 2016.

A. Modi and I. Titov, Inducing neural models of script knowledge, Proceedings of the Eighteenth Conference on Computational Natural Language Learning, pp.49-57, 2014.

A. Modi, I. Titov, V. Demberg, A. Sayeed, and M. Pinkal, Modelling semantic expectation: Using script knowledge for referent prediction, Transactions of the Association for Computational Linguistics, vol.5, pp.31-44, 2017.

F. J. Morales and D. Roggen, Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition, Sensors, vol.16, issue.1, p.115, 2016.

A. Murad and J. Pyun, Deep recurrent neural networks for human activity recognition, Sensors, vol.17, issue.11, p.2556, 2017.

K. T. Nguyen, F. Portet, and C. Garbay, Dealing with imbalanced data sets for human activity recognition using mobile phone sensors, Intelligent Environments 2018 -Workshop Proceedings of the 14th International Conference on Intelligent Environments, pp.129-138, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01950472

K. Ohnishi, A. Kanehira, A. Kanezaki, and T. Harada, Recognizing activities of daily living with a wrist-mounted camera, 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp.3103-3111, 2016.

V. Ordonez, G. Kulkarni, and T. L. Berg, Im2text: Describing images using 1 million captioned photographs, Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems, pp.1143-1151, 2011.

J. Pärkkä, M. Ermes, P. Korpipää, J. Mäntyjärvi, J. Peltola et al., , 2006.

, Activity classification using realistic data from wearable sensors, IEEE Trans. Information Technology in Biomedicine, vol.10, issue.1, pp.119-128

J. Pennington, R. Socher, and C. D. Manning, Glove: Global vectors for word representation, Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, pp.1532-1543, 2014.

A. J. Perez, M. A. Labrador, and S. J. Barbeau, G-sense: a scalable architecture for global sensing and monitoring, IEEE Network, vol.24, issue.4, pp.57-64, 2010.

K. Pichotta and R. J. Mooney, Statistical script learning with multi-argument events, Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, pp.220-229, 2014.

K. Pichotta and R. J. Mooney, Learning statistical scripts with lstm recurrent neural networks, AAAI, pp.2800-2806, 2016.

H. Pirsiavash and D. Ramanan, Detecting activities of daily living in first-person camera views, 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp.2847-2854, 2012.

T. Poibeau, H. Saggion, J. Piskorski, and R. Yangarber, Multi-source, Multilingual Information Extraction and Summarization. Theory and Applications of Natural Language Processing, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00782234

F. Portet, E. Reiter, A. Gatt, J. Hunter, S. Sripada et al., , 2009.

, Automatic generation of textual summaries from neonatal intensive care data, Artificial Intelligence, vol.173, issue.7-8, pp.789-816, 2012.

R. Qader, K. Jneid, F. Portet, and C. Labbé, Generation of company descriptions using concept-to-text and text-to-text deep models: dataset collection and systems evaluation, Proceedings of the 11th International Conference on Natural Language Generation, pp.254-263, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01950467

M. Regneri, A. Koller, and M. Pinkal, Learning script knowledge with web experiments, ACL 2010, Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp.979-988, 2010.

E. Reiter and R. Dale, Building Natural Language Generation Systems, 2000.

E. Reiter, S. Sripada, J. Hunter, J. Yu, D. et al., Choosing words in computer-generated weather forecasts, Artif. Intell, vol.167, issue.1-2, pp.137-169, 2005.

D. Riboni and C. Bettini, Cosar: Hybrid reasoning for context-aware activity recognition, Personal Ubiquitous Comput, vol.15, issue.3, pp.271-289, 2011.

M. Rohrbach, W. Qiu, I. Titov, S. Thater, M. Pinkal et al., Translating video content to natural language descriptions, IEEE International Conference on Computer Vision, ICCV 2013, pp.433-440, 2013.

C. A. Ronao and S. Cho, Deep convolutional neural networks for human activity recognition with smartphone sensors, ICONIP (4), vol.9492, pp.46-53, 2015.

S. Sani, N. Wiratunga, S. Massie, C. , and K. , knn sampling for personalised human activity recognition, Case-Based Reasoning Research and Development -25th International Conference, pp.330-344, 2017.

T. R. Saputri, A. M. Khan, and S. Lee, User-independent activity recognition via three-stage ga-based feature selection, p.10, 2014.

R. C. Schank and R. P. Abelson, Scripts, plans and knowledge, Advance Papers of the Fourth International Joint Conference on Artificial Intelligence, pp.151-157, 1975.
URL : https://hal.archives-ouvertes.fr/hal-00692030

B. Settles, Active learning literature survey, 2010.

S. Shaheen, W. El-hajj, H. M. Hajj, and S. Elbassuoni, Emotion recognition from text based on automatically generated rules, 2014 IEEE International Conference on Data Mining Workshops, ICDM Workshops, pp.383-392, 2014.

C. E. Shannon, A mathematical theory of communication, Mobile Computing and Communications Review, vol.5, pp.3-55, 2001.

N. A. Smith and J. Eisner, Contrastive estimation: Training log-linear models on unlabeled data, ACL 2005, 43rd Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, pp.354-362, 2005.

R. Socher, B. Huval, C. D. Manning, and A. Y. Ng, Semantic compositionality through recursive matrix-vector spaces, Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp.1201-1211, 2012.

R. Socher, A. Perelygin, J. Wu, J. Chuang, C. D. Manning et al., Recursive deep models for semantic compositionality over a sentiment treebank, Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp.1631-1642, 2013.

I. Sutskever, O. Vinyals, and Q. V. Le, Sequence to sequence learning with neural networks, Proceedings of the Advances in Neural Information Processing Systems, pp.3104-3112, 2014.

I. Sutskever, O. Vinyals, and Q. V. Le, Sequence to sequence learning with neural networks, Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems, pp.3104-3112, 2014.

T. Sztyler, J. Carmona, J. Völker, and H. Stuckenschmidt, Self-tracking reloaded: Applying process mining to personalized health care from labeled sensor data, 2016.

T. Petri, Nets and Other Models of Concurrency, vol.11, pp.160-180

T. Sztyler and H. Stuckenschmidt, On-body localization of wearable devices: An investigation of position-aware activity recognition, 2016 IEEE International Conference on Pervasive Computing and Communications, pp.1-9, 2016.

K. S. Tai, R. Socher, and C. D. Manning, Improved semantic representations from tree-structured long short-term memory networks, Proceedings of the 53rd, 2015.

, Annual Meeting of the Association for Computational Linguistics, pp.1556-1566

C. C. Tan, Y. Jiang, and C. Ngo, Towards textually describing complex video contents with audio-visual concept classifiers, Proceedings of the 19th International Conference on Multimedia, pp.655-658, 2011.

E. M. Tapia, S. S. Intille, W. L. Haskell, K. Larson, J. A. Wright et al., Real-time recognition of physical activities and their intensities using wireless accelerometers and a heart rate monitor, 11th IEEE International Symposium on Wearable Computers (ISWC 2007), pp.37-40, 2007.

P. Tarnowski, M. Kolodziej, A. Majkowski, and R. J. Rak, Emotion recognition using facial expressions, International Conference on Computational Science, pp.1175-1184, 2017.

S. S. Tomkins, Script theory: differential magnification of affects, Nebraska Symposium on Motivation, vol.26, pp.201-236, 1978.

S. Tong and D. Koller, Support vector machine active learning with applications to text classification, Journal of Machine Learning Research, vol.2, pp.45-66, 2001.

Y. Vaizman, K. Ellis, and G. R. Lanckriet, Recognizing detailed human context in the wild from smartphones and smartwatches, IEEE Pervasive Computing, pp.62-74, 2017.

S. Venugopalan, L. A. Hendricks, R. J. Mooney, and K. Saenko, Improving lstm-based video description with linguistic knowledge mined from text, Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp.1961-1966, 2016.

S. Venugopalan, M. Rohrbach, J. Donahue, R. J. Mooney, T. Darrell et al., Sequence to sequence -video to text, 2015 IEEE International Conference on Computer Vision, pp.4534-4542, 2015.

S. Venugopalan, H. Xu, J. Donahue, M. Rohrbach, R. J. Mooney et al., Translating videos to natural language using deep recurrent neural networks, 2015.

N. In and . Hlt, The 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp.1494-1504, 2015.

P. Vepakomma, D. De, S. K. Das, and S. Bhansali, A-wristocracy: Deep learning on wrist-worn sensing for recognition of user complex activities, 12th IEEE International Conference on Wearable and Implantable Body Sensor Networks, BSN 2015, pp.1-6, 2015.

M. Verhagen, I. Mani, R. Saurí, J. Littman, R. Knippen et al., Automating temporal annotation with TARSQI, 2005.

, ACL 2005, 43rd Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, pp.81-84, 2005.

P. Vincent, H. Larochelle, Y. Bengio, and P. Manzagol, Extracting and composing robust features with denoising autoencoders, Machine Learning, Proceedings of the Twenty-Fifth International Conference (ICML 2008), pp.1096-1103, 2008.

J. Wang, Y. Chen, S. Hao, X. Peng, and L. Hu, Deep learning for sensor-based activity recognition: A survey, Pattern Recognition Letters, vol.119, pp.3-11, 2019.

L. Wang, Recognition of human activities using continuous autoencoders with wearable sensors, Sensors, vol.16, issue.2, p.189, 2016.

S. Williams and E. Reiter, Generating basic skills reports for low-skilled readers*, Nat. Lang. Eng, vol.14, issue.4, pp.495-525, 2008.

J. Yang, M. N. Nguyen, P. P. San, X. Li, and S. Krishnaswamy, Deep convolutional neural networks on multichannel time series for human activity recognition, 2015.

, Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI 2015, pp.3995-4001, 2015.

Q. Yang, Activity recognition: Linking low-level sensors to high-level intelligence, IJCAI 2009, Proceedings of the 21st International Joint Conference on Artificial Intelligence, pp.20-25, 2009.

S. Yao, S. Hu, Y. Zhao, A. Zhang, and T. F. Abdelzaher, Deepsense: A unified deep learning framework for time-series mobile sensing data processing, Proceedings of the 26th International Conference on World Wide Web, pp.351-360, 2017.

S. Yen and Y. Lee, Cluster-based under-sampling approaches for imbalanced data distributions, Expert Syst. Appl, vol.36, issue.3, pp.5718-5727, 2009.

T. Zebin, P. J. Scully, and K. B. Ozanyan, Inertial sensor based modelling of human activity classes: Feature extraction and multi-sensor data fusion using machine learning algorithms, eHealth 360°-International Summit on eHealth, pp.306-314, 2016.

M. Zeng, L. T. Nguyen, B. Yu, O. J. Mengshoel, J. Zhu et al., Convolutional neural networks for human activity recognition using mobile sensors, 6th International Conference on Mobile Computing, Applications and Services, pp.197-205, 2014.

M. Zeng, L. T. Nguyen, B. Yu, O. J. Mengshoel, J. Zhu et al., Convolutional neural networks for human activity recognition using mobile sensors, 6th International Conference on Mobile Computing, Applications and Services, pp.197-205, 2014.

L. Zhang, X. Wu, and D. Luo, Real-time activity recognition on smartphones using deep neural networks, 2015 IEEE 12th Intl Conf on Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom), pp.1236-1242, 2015.

Y. Zheng, Q. Liu, E. Chen, Y. Ge, and J. L. Zhao, Exploiting multi-channels deep convolutional neural networks for multivariate time series classification, Frontiers Comput. Sci, vol.10, issue.1, pp.96-112, 2016.

C. Zhu and W. Sheng, Multi-sensor fusion for human daily activity recognition in robot-assisted living, Proceedings of the 4th ACM/IEEE International Conference on Human Robot Interaction, HRI '09, pp.303-304, 2009.