A Survey of Human Activity Recognition in Smart Homes Based on IoT Sensors Algorithms: Taxonomies, Challenges, and Opportunities with Deep Learning
暂无分享,去创建一个
Sao Mai Nguyen | Ioannis Kanellos | Benoit Leduc | Damien Bouchabou | Christophe Lohr | S. Nguyen | I. Kanellos | Christopher Lohr | Benoit Leduc | Damien Bouchabou
[1] Ivan Marsic,et al. Deep Learning for RFID-Based Activity Recognition , 2016, SenSys.
[2] Mohamed Sedky,et al. OpenSHS: Open Smart Home Simulator , 2017, Sensors.
[3] Diane J. Cook,et al. Learning Setting-Generalized Activity Models for Smart Spaces , 2012, IEEE Intelligent Systems.
[4] Sydney Katz. Assessing Self‐maintenance: Activities of Daily Living, Mobility, and Instrumental Activities of Daily Living , 1983, Journal of the American Geriatrics Society.
[5] Luke S. Zettlemoyer,et al. Deep Contextualized Word Representations , 2018, NAACL.
[6] Alaa Alhamoud,et al. Activity Recognition in Multi-User Environments Using Techniques of Multi-label Classification , 2016, IOT.
[7] Henry A. Kautz,et al. Inferring activities from interactions with objects , 2004, IEEE Pervasive Computing.
[8] Taghi M. Khoshgoftaar,et al. A survey of transfer learning , 2016, Journal of Big Data.
[9] Guilin Chen,et al. Human Activity Recognition in a Smart Home Environment with Stacked Denoising Autoencoders , 2016, WAIM Workshops.
[10] Diane J. Cook,et al. Activity Discovery and Activity Recognition: A New Partnership , 2013, IEEE Transactions on Cybernetics.
[11] Ian Craddock,et al. A dataset for room level indoor localization using a smart home in a box , 2019, Data in brief.
[12] Amos Storkey,et al. Meta-Learning in Neural Networks: A Survey , 2020, IEEE transactions on pattern analysis and machine intelligence.
[13] Germain Forestier,et al. Transfer learning for time series classification , 2018, 2018 IEEE International Conference on Big Data (Big Data).
[14] Yunqian Ma,et al. Imbalanced Learning: Foundations, Algorithms, and Applications , 2013 .
[15] Daniel Roggen,et al. Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition , 2016, Sensors.
[16] Chris D. Nugent,et al. Ensemble classifier of long short-term memory with fuzzy temporal windows on binary sensors for activity recognition , 2018, Expert Syst. Appl..
[17] Diane J. Cook,et al. CASAS: A Smart Home in a Box , 2013, Computer.
[18] Vladlen Koltun,et al. Playing for Benchmarks , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[19] Chris D. Nugent,et al. UCAmI Cup. Analyzing the UJA Human Activity Recognition Dataset of Activities of Daily Living , 2018, UCAmI.
[20] Lina Yao,et al. Deep Learning for Sensor-based Human Activity Recognition , 2021, ACM Comput. Surv..
[21] Ivan Marsic,et al. Concurrent Activity Recognition with Multimodal CNN-LSTM Structure , 2017, ArXiv.
[22] Lei He,et al. Human activity recognition based on feature selection in smart home using back-propagation algorithm. , 2014, ISA transactions.
[23] Francisco Herrera,et al. Learning from Imbalanced Data Sets , 2018, Springer International Publishing.
[24] Chris D. Nugent,et al. A Logical Framework for Behaviour Reasoning and Assistance in a Smart Home , 2008 .
[25] Ming-Wei Chang,et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.
[26] C A Nelson,et al. Learning to Learn , 2017, Encyclopedia of Machine Learning and Data Mining.
[27] Chris D. Nugent,et al. Human Activity Recognition and Behaviour Analysis , 2019, Springer International Publishing.
[28] Satoshi Tanaka,et al. Applying Ontology and Probabilistic Model to Human Activity Recognition from Surrounding Things , 2007 .
[29] LuKun Wang,et al. Human Activity Recognition Based on Wearable Sensor Using Hierarchical Deep LSTM Networks , 2019, Circuits, Systems, and Signal Processing.
[30] L. Whitmarsh,et al. Social barriers to the adoption of smart homes , 2013 .
[31] Maxime Devanne,et al. Recognition of Activities of Daily Living via Hierarchical Long-Short Term Memory Networks , 2019, 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC).
[32] Wai Lok Woo,et al. Joint Learning of Temporal Models to Handle Imbalanced Data for Human Activity Recognition , 2020, Applied Sciences.
[33] Mohamed-Rafik Bouguelia,et al. Efficient Activity Recognition in Smart Homes Using Delayed Fuzzy Temporal Windows on Binary Sensors , 2020, IEEE Journal of Biomedical and Health Informatics.
[34] Christophe Lohr,et al. Fully Convolutional Network Bootstrapped by Word Encoding and Embedding for Activity Recognition in Smart Homes , 2020, Communications in Computer and Information Science.
[35] Anthony G. Cohn,et al. Feature Space Analysis for Human Activity Recognition in Smart Environments , 2016, 2016 12th International Conference on Intelligent Environments (IE).
[36] L. Minh Dang,et al. Sensor-based and vision-based human activity recognition: A comprehensive survey , 2020, Pattern Recognit..
[37] Sohail Sarwar,et al. Ontology-driven semantic unified modelling for concurrent activity recognition (OSCAR) , 2018, Multimedia Tools and Applications.
[38] George Moore,et al. A Comparative Analysis of Windowing Approaches in Dense Sensing Environments , 2018, UCAmI.
[39] Kyungeun Cho,et al. Persim 3D: Context-Driven Simulation and Modeling of Human Activities in Smart Spaces , 2015, IEEE Transactions on Automation Science and Engineering.
[40] Chris D. Nugent,et al. Ontology-based activity recognition in intelligent pervasive environments , 2009, Int. J. Web Inf. Syst..
[41] Parviz Asghari,et al. Online human activity recognition employing hierarchical hidden Markov models , 2019, J. Ambient Intell. Humaniz. Comput..
[42] Jesse Hoey,et al. Activity Recognition in Pervasive Intelligent Environments , 2011 .
[43] Andreas Holzinger,et al. Users' Perceptions and Attitudes Towards Smart Home Technologies , 2018, ICOST.
[44] Manuela M. Veloso,et al. Conditional random fields for activity recognition , 2007, AAMAS '07.
[45] Matjaz Gams,et al. Competitive Live Evaluations of Activity-Recognition Systems , 2015, IEEE Pervasive Computing.
[46] Stefan Wermter,et al. Continual Lifelong Learning with Neural Networks: A Review , 2019, Neural Networks.
[47] Nawel Yala,et al. Feature extraction for human activity recognition on streaming data , 2015, 2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA).
[48] Chris D. Nugent,et al. Evidential fusion of sensor data for activity recognition in smart homes , 2009, Pervasive Mob. Comput..
[49] Wing W. Y. Ng,et al. Neural Network Ensembles for Sensor-Based Human Activity Recognition Within Smart Environments , 2019, Sensors.
[50] Andres Mendez-Vazquez,et al. Simulating Events to Generate Synthetic Data for Pervasive Spaces , 2008 .
[51] Chris D. Nugent,et al. The creation of simulated activity datasets using a graphical intelligent environment simulation tool , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[52] Jesse Hoey,et al. Sensor-Based Activity Recognition , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).
[53] Chris D. Nugent,et al. Semantic Smart Homes: Towards Knowledge Rich Assisted Living Environments , 2009 .
[54] R. Stiefelhagen,et al. Let’s Play for Action: Recognizing Activities of Daily Living by Learning from Life Simulation Video Games , 2021, 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[55] Jake K. Aggarwal,et al. Human activity recognition from 3D data: A review , 2014, Pattern Recognit. Lett..
[56] Guy Lapalme,et al. A systematic analysis of performance measures for classification tasks , 2009, Inf. Process. Manag..
[57] Heinrich C. Mayr,et al. A windowing approach for activity recognition in sensor data streams , 2016, 2016 Eighth International Conference on Ubiquitous and Future Networks (ICUFN).
[58] Trevor Darrell,et al. Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[59] B. Kröse,et al. Bayesian Activity Recognition in Residence for Elders , 2007 .
[60] Qing Zhang,et al. Multi-Resident Activity Monitoring in Smart Homes: A Case Study , 2018, 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops).
[61] Özlem Durmaz Incel,et al. ARAS human activity datasets in multiple homes with multiple residents , 2013, 2013 7th International Conference on Pervasive Computing Technologies for Healthcare and Workshops.
[62] Tan-Hsu Tan,et al. Unobtrusive Activity Recognition of Elderly People Living Alone Using Anonymous Binary Sensors and DCNN , 2019, IEEE Journal of Biomedical and Health Informatics.
[63] James L. Crowley,et al. A Dataset of Routine Daily Activities in an Instrumented Home , 2017, UCAmI.
[64] T. V. Kasteren,et al. Activity recognition for health monitoring elderly using temporal probabilistic models , 2007 .
[65] Wai Lok Woo,et al. Dilated causal convolution with multi-head self attention for sensor human activity recognition , 2021, Neural Computing and Applications.
[66] Kent Larson,et al. Activity Recognition in the Home Using Simple and Ubiquitous Sensors , 2004, Pervasive.
[67] Gwenn Englebienne,et al. Human activity recognition from wireless sensor network data: benchmark and software , 2011 .
[68] Macarena Espinilla,et al. Sensor-Based Datasets for Human Activity Recognition – A Systematic Review of Literature , 2018, IEEE Access.
[69] Eric Campo,et al. A review of smart homes - Present state and future challenges , 2008, Comput. Methods Programs Biomed..
[70] Chris D. Nugent,et al. A Knowledge-Driven Approach to Activity Recognition in Smart Homes , 2012, IEEE Transactions on Knowledge and Data Engineering.
[71] Ahmad Lotfi,et al. Employing a deep convolutional neural network for human activity recognition based on binary ambient sensor data , 2020, PETRA.
[72] Diane J. Cook,et al. Transfer learning for activity recognition: a survey , 2013, Knowledge and Information Systems.
[73] Matthieu Geist,et al. Human Activity Recognition Using Recurrent Neural Networks , 2017, CD-MAKE.
[74] Matthai Philipose,et al. Mining models of human activities from the web , 2004, WWW '04.
[75] Oliver Brdiczka,et al. Learning Situation Models in a Smart Home , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).
[76] Quoc V. Le,et al. Sequence to Sequence Learning with Neural Networks , 2014, NIPS.
[77] Mohamed Sedky,et al. Evaluating Machine Learning Techniques for Activity Classification in Smart Home Environments , 2018 .
[78] Chih-Yung Chang,et al. Activities of Daily Living Recognition With Binary Environment Sensors Using Deep Learning: A Comparative Study , 2021, IEEE Sensors Journal.
[79] Yoshua Bengio,et al. Learning long-term dependencies with gradient descent is difficult , 1994, IEEE Trans. Neural Networks.
[80] Emanuele Frontoni,et al. A sequential deep learning application for recognising human activities in smart homes , 2020, Neurocomputing.
[81] Tan-Hsu Tan,et al. Multi-Resident Activity Recognition in a Smart Home Using RGB Activity Image and DCNN , 2018, IEEE Sensors Journal.
[82] Anthony G. Cohn,et al. Learning Hierarchical Models of Complex Daily Activities from Annotated Videos , 2018, 2018 IEEE Winter Conference on Applications of Computer Vision (WACV).
[83] Vladlen Koltun,et al. Playing for Data: Ground Truth from Computer Games , 2016, ECCV.
[84] Xiaohui Peng,et al. Deep Learning for Sensor-based Activity Recognition: A Survey , 2017, Pattern Recognit. Lett..
[85] Diane J Cook,et al. Assessing the Quality of Activities in a Smart Environment , 2009, Methods of Information in Medicine.
[86] Diane J. Cook,et al. Activity2Vec: Learning ADL Embeddings from Sensor Data with a Sequence-to-Sequence Model , 2019, ArXiv.
[87] Christian Igel,et al. U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging , 2019, NeurIPS.
[88] Diane J. Cook,et al. Enhancing activity recognition using CPD-based activity segmentation , 2019, Pervasive Mob. Comput..
[89] Diane J. Cook,et al. Activity recognition on streaming sensor data , 2014, Pervasive Mob. Comput..
[90] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[91] Matthieu Geist,et al. Convolutional and Recurrent Neural Networks for Activity Recognition in Smart Environment , 2015, BIRS-IMLKE.
[92] Diane Myung-kyung Woodbridge,et al. Sensor Selection for Activity Classification at Smart Home Environments , 2020, 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).
[93] Yoshua Bengio,et al. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.
[94] Thinagaran Perumal,et al. Sequential neural networks for multi-resident activity recognition in ambient sensing smart homes , 2021, Applied Intelligence.
[95] 知秀 柴田. 5分で分かる!? 有名論文ナナメ読み:Jacob Devlin et al. : BERT : Pre-training of Deep Bidirectional Transformers for Language Understanding , 2020 .
[96] Shu-Ching Chen,et al. Multimedia Big Data Analytics , 2018, ACM Comput. Surv..
[97] J. C. Schlimmer,et al. Incremental learning from noisy data , 2004, Machine Learning.
[98] Diane J. Cook,et al. Persim - Simulator for Human Activities in Pervasive Spaces , 2011, 2011 Seventh International Conference on Intelligent Environments.
[99] Ioannis A. Kakadiaris,et al. A Review of Human Activity Recognition Methods , 2015, Front. Robot. AI.
[100] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[101] Dominique Duhaut,et al. Intrinsically Motivated Open-Ended Multi-Task Learning Using Transfer Learning to Discover Task Hierarchy , 2021, Applied Sciences.
[102] Gregory D. Abowd,et al. Developing shared home behavior datasets to advance HCI and ubiquitous computing research , 2009, CHI Extended Abstracts.
[103] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[104] Quan Z. Sheng,et al. Different Approaches for Human Activity Recognition: A Survey , 2019, ArXiv.
[105] Michal Koperski,et al. Toyota Smarthome: Real-World Activities of Daily Living , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[106] Araceli Sanchis,et al. Activity Recognition Using Hybrid Generative/Discriminative Models on Home Environments Using Binary Sensors , 2013, Sensors.
[107] Daniel Retkowitz,et al. Simulation of Smart Environments , 2007, IEEE International Conference on Pervasive Services.
[108] Sung-Bong Yang,et al. Deep neural networks for activity recognition with multi-sensor data in a smart home , 2018, 2018 IEEE 4th World Forum on Internet of Things (WF-IoT).
[109] Abdenour Hadid,et al. Vision-based human activity recognition: a survey , 2020, Multimedia Tools and Applications.
[110] Chris D. Nugent,et al. Simulation of Smart Home Activity Datasets , 2015, Sensors.
[111] Seetha Hari,et al. Learning From Imbalanced Data , 2019, Advances in Computer and Electrical Engineering.
[112] Luis Gomes,et al. An Intelligent Smart Plug with Shared Knowledge Capabilities , 2018, Sensors.
[113] Jennifer Healey,et al. A Long-Term Evaluation of Sensing Modalities for Activity Recognition , 2007, UbiComp.
[114] Chen Yu,et al. A configurable context-aware simulator for smart home systems , 2011, 2011 6th International Conference on Pervasive Computing and Applications.
[115] Michel Vacher,et al. SVM-Based Multimodal Classification of Activities of Daily Living in Health Smart Homes: Sensors, Algorithms, and First Experimental Results , 2010, IEEE Transactions on Information Technology in Biomedicine.
[116] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[117] Kwei-Jay Lin,et al. Using Latent Knowledge to Improve Real-Time Activity Recognition for Smart IoT , 2020, IEEE Transactions on Knowledge and Data Engineering.