$\require{mediawiki-texvc}$

연합인증

연합인증 가입 기관의 연구자들은 소속기관의 인증정보(ID와 암호)를 이용해 다른 대학, 연구기관, 서비스 공급자의 다양한 온라인 자원과 연구 데이터를 이용할 수 있습니다.

이는 여행자가 자국에서 발행 받은 여권으로 세계 각국을 자유롭게 여행할 수 있는 것과 같습니다.

연합인증으로 이용이 가능한 서비스는 NTIS, DataON, Edison, Kafe, Webinar 등이 있습니다.

한번의 인증절차만으로 연합인증 가입 서비스에 추가 로그인 없이 이용이 가능합니다.

다만, 연합인증을 위해서는 최초 1회만 인증 절차가 필요합니다. (회원이 아닐 경우 회원 가입이 필요합니다.)

연합인증 절차는 다음과 같습니다.

최초이용시에는
ScienceON에 로그인 → 연합인증 서비스 접속 → 로그인 (본인 확인 또는 회원가입) → 서비스 이용

그 이후에는
ScienceON 로그인 → 연합인증 서비스 접속 → 서비스 이용

연합인증을 활용하시면 KISTI가 제공하는 다양한 서비스를 편리하게 이용하실 수 있습니다.

[해외논문] FilterNet: A Many-to-Many Deep Learning Architecture for Time Series Classification 원문보기

Sensors, v.20 no.9, 2020년, pp.2498 -   

Chambers, Robert D. ,  Yoder, Nathanael C.

Abstract AI-Helper 아이콘AI-Helper

In this paper, we present and benchmark FilterNet, a flexible deep learning architecture for time series classification tasks, such as activity recognition via multichannel sensor data. It adapts popular convolutional neural network (CNN) and long short-term memory (LSTM) motifs which have excelled ...

주제어

참고문헌 (54)

  1. 1. Yang Q. Wu X. 10 challenging problems in data mining research Int. J. Inf. Technol. Decis. Mak. 2006 5 597 604 10.1142/S0219622006002258 

  2. 2. Altilio R. Andreasi G. Panella M. A Classification Approach to Modeling Financial Time Series Neural Advances in Processing Nonlinear Dynamic Signals Esposito A. Faundez-Zanuy M. Morabito F.C. Pasero E. Springer International Publishing Cham, Switzerland 2019 97 106 978-3-319-95098-3 

  3. 3. Susto G.A. Cenedese A. Terzi M. Chapter 9―Time-Series Classification Methods: Review and Applications to Power Systems Data Big Data Application in Power Systems Arghandeh R. Zhou Y. Elsevier Amsterdam, The Netherlands 2018 179 220 

  4. 4. Rajkomar A. Oren E. Chen K. Dai A.M. Hajaj N. Hardt M. Liu P.J. Liu X. Marcus J. Sun M. Scalable and accurate deep learning with electronic health records Npj Digit. Med. 2018 1 1 10 10.1038/s41746-018-0029-1 31304287 

  5. 5. Nwe T.L. Dat T.H. Ma B. Convolutional neural network with multi-task learning scheme for acoustic scene classification Proceedings of the 2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) Kuala Lumpur, Malaysia 12?15 December 2017 1347 1350 

  6. 6. Lotte F. Bougrain L. Cichocki A. Clerc M. Congedo M. Rakotomamonjy A. Yger F. A review of classification algorithms for EEG-based brain?computer interfaces: A 10 year update J. Neural Eng. 2018 15 031005 10.1088/1741-2552/aab2f2 29488902 

  7. 7. Wang J. Chen Y. Hao S. Peng X. Hu L. Deep Learning for Sensor-based Activity Recognition: A Survey Pattern Recognit. Lett. 2019 119 3 11 10.1016/j.patrec.2018.02.010 

  8. 8. Bagnall A. Lines J. Bostrom A. Large J. Keogh E. The great time series classification bake off: A review and experimental evaluation of recent algorithmic advances Data Min. Knowl. Discov. 2017 31 606 660 10.1007/s10618-016-0483-9 30930678 

  9. 9. Fortino G. Galzarano S. Gravina R. Li W. A framework for collaborative computing and multi-sensor data fusion in body sensor networks Inf. Fusion 2015 22 50 70 10.1016/j.inffus.2014.03.005 

  10. 10. Dau H.A. Bagnall A. Kamgar K. Yeh C.-C.M. Zhu Y. Gharghabi S. Ratanamahatana C.A. Keogh E. The UCR Time Series Archive IEEE/CAA J. Autom. Sin. 2019 6 1293 1305 10.1109/JAS.2019.1911747 

  11. 11. Dua D. Graff C. UCI Machine Learning Repository Available online: http://archive.ics.uci.edu/ml (accessed on 25 March 2020) 

  12. 12. Kumar S. Ubiquitous Smart Home System Using Android Application Int. J. Comput. Netw. Commun. 2014 6 33 43 10.5121/ijcnc.2014.6103 

  13. 13. Qin Z. Hu L. Zhang N. Chen D. Zhang K. Qin Z. Choo K.-K.R. Learning-Aided User Identification Using Smartphone Sensors for Smart Homes IEEE Internet Things J. 2019 6 7760 7772 10.1109/JIOT.2019.2900862 

  14. 14. Kim Y. Toomajian B. Hand Gesture Recognition Using Micro-Doppler Signatures with Convolutional Neural Network IEEE Access 2016 4 7125 7130 10.1109/ACCESS.2016.2617282 

  15. 15. Rautaray S.S. Agrawal A. Vision based hand gesture recognition for human computer interaction: A survey Artif. Intell. Rev. 2015 43 1 54 10.1007/s10462-012-9356-9 

  16. 16. Pantelopoulos A. Bourbakis N.G. A Survey on Wearable Sensor-Based Systems for Health Monitoring and Prognosis IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 2010 40 1 12 10.1109/TSMCC.2009.2032660 

  17. 17. Vepakomma P. De D. Das S.K. Bhansali S. A-Wristocracy: Deep learning on wrist-worn sensing for recognition of user complex activities Proceedings of the 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN) Cambridge, MA, USA 9?12 June 2015 1 6 

  18. 18. Pet Insight Project Available online: https://www.petinsight.com (accessed on 16 December 2019) 

  19. 19. Whistle Available online: https://www.whistle.com/ (accessed on 16 December 2019) 

  20. 20. Lines J. Taylor S. Bagnall A. Time Series Classification with HIVE-COTE: The Hierarchical Vote Collective of Transformation-Based Ensembles ACM Trans Knowl. Discov Data 2018 12 1 35 10.1145/3182382 

  21. 21. Ismail Fawaz H. Forestier G. Weber J. Idoumghar L. Muller P.-A. Deep learning for time series classification: A review Data Min. Knowl. Discov. 2019 33 917 963 10.1007/s10618-019-00619-1 

  22. 22. Alsheikh M.A. Selim A. Niyato D. Doyle L. Lin S. Tan H.-P. Deep activity recognition models with triaxial accelerometers Proceedings of the Workshops at the Thirtieth AAAI Conference on Artificial Intelligence Phoenix, AZ, USA 12?13 February 2016 8 13 

  23. 23. Plotz T. Hammerla N.Y. Olivier P. Feature learning for activity recognition in ubiquitous computing Proceedings of the IJCAI 2011―22nd International Joint Conference on Artificial Intelligence Barcelona, Spain 16?22 July 2011 1729 1734 

  24. 24. Bengio Y. Deep Learning of Representations: Looking Forward Proceedings of the Statistical Language and Speech Processing Dediu A.-H. Martin-Vide C. Mitkov R. Truthe B. Springer Berlin/Heidelberg, Germany 2013 1 37 

  25. 25. Yang J.B. Nguyen M.N. San P.P. Li X.L. Krishnaswamy S. Deep convolutional neural networks on multichannel time series for human activity recognition Proceedings of the 24th International Conference on Artificial Intelligence Buenos Aires, Argentina 25?31 July 2015 3995 4001 

  26. 26. Yazdanbakhsh O. Dick S. Multivariate Time Series Classification using Dilated Convolutional Neural Network arXiv 2019 1905.01697 

  27. 27. Hatami N. Gavet Y. Debayle J. Classification of time-series images using deep convolutional neural networks Proceedings of the Tenth International Conference on Machine Vision (ICMV 2017), International Society for Optics and Photonics Vienna, Austria 13 April 2018 Volume 10696 106960Y 

  28. 28. Qin Z. Zhang Y. Meng S. Qin Z. Choo K.-K.R. Imaging and fusing time series for wearable sensor-based human activity recognition Inf. Fusion 2020 53 80 87 10.1016/j.inffus.2019.06.014 

  29. 29. Thanaraj K.P. Parvathavarthini B. Tanik U.J. Rajinikanth V. Kadry S. Kamalanand K. Implementation of Deep Neural Networks to Classify EEG Signals using Gramian Angular Summation Field for Epilepsy Diagnosis arXiv 2020 2003.04534 

  30. 30. Bengio Y. Simard P. Frasconi P. Learning long-term dependencies with gradient descent is difficult IEEE Trans. Neural Netw. 1994 5 157 166 10.1109/72.279181 18267787 

  31. 31. Hochreiter S. Schmidhuber J. Long Short-Term Memory Neural Comput. 1997 9 1735 1780 10.1162/neco.1997.9.8.1735 9377276 

  32. 32. Lipton Z.C. Berkowitz J. Elkan C. A Critical Review of Recurrent Neural Networks for Sequence Learning arXiv 2015 1506.00019 

  33. 33. Ordonez F.J. Roggen D. Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition Sensors 2016 16 115 10.3390/s16010115 26797612 

  34. 34. Chavarriaga R. Sagha H. Calatroni A. Digumarti S.T. Troster G. Millan J. del R.; Roggen, D. The Opportunity challenge: A benchmark database for on-body sensor-based activity recognition Pattern Recognit. Lett. 2013 34 2033 2042 10.1016/j.patrec.2012.12.014 

  35. 35. Zappi P. Lombriser C. Stiefmeier T. Farella E. Roggen D. Benini L. Troster G. Activity Recognition from On-Body Sensors: Accuracy-Power Trade-Off by Dynamic Sensor Selection Proceedings of the Wireless Sensor Networks Verdone R. Springer Berlin/Heidelberg, Germany 2008 17 33 

  36. 36. Hammerla N.Y. Halloran S. Plotz T. Deep, convolutional, and recurrent models for human activity recognition using wearables Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence New York, NY, USA 9?15 July 2016 1533 1540 

  37. 37. Inoue M. Inoue S. Nishida T. Deep Recurrent Neural Network for Mobile Human Activity Recognition with High Throughput Artif Life Robot 2018 23 173 185 10.1007/s10015-017-0422-x 

  38. 38. Edel M. Koppe E. Binarized-BLSTM-RNN based Human Activity Recognition Proceedings of the 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Alcala de Henares Alcala de Henares, Spain 4?7 October 2016 1 7 

  39. 39. Guan Y. Ploetz T. Ensembles of Deep LSTM Learners for Activity Recognition using Wearables Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2017 1 1 28 10.1145/3090076 

  40. 40. Ronneberger O. Fischer P. Brox T. U-Net: Convolutional Networks for Biomedical Image Segmentation Proceedings of the Medical Image Computing and Computer-Assisted Intervention―MICCAI 2015 Navab N. Hornegger J. Wells W.M. Frangi A.F. Springer International Publishing Cham, Switzerland 2015 234 241 

  41. 41. Humayun A.I. Ghaffarzadegan S. Feng Z. Hasan T. Learning Front-end Filter-bank Parameters using Convolutional Neural Networks for Abnormal Heart Sound Detection Proceedings of the 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Honolulu, HI, USA 17?21 July 2018 1408 1411 

  42. 42. Goodfellow I. Bengio Y. Courville A. Deep Learning The MIT Press Cambridge, MA, USA 2016 978-0-262-03561-3 

  43. 43. Baloch Z. Shaikh F.K. Unar M.A. Deep architectures for human activity recognition using sensors 3c Tecnol. Glosas Innov. Apl. Pyme 2019 8 14 35 10.17993/3ctecno.2019.specialissue2.14-35 

  44. 44. UCI Machine Learning Repository: OPPORTUNITY Activity Recognition Data Set Available online: https://archive.ics.uci.edu/ml/datasets/OPPORTUNITY+Activity+Recognition (accessed on 4 December 2019) 

  45. 45. nhammerla/deepHAR Available online: https://github.com/nhammerla/deepHAR (accessed on 5 December 2019) 

  46. 46. Sussexwearlab Sussexwearlab/DeepConvLSTM Available online: https://github.com/sussexwearlab/DeepConvLSTM (accessed on 9 December 2019) 

  47. 47. Paszke A. Gross S. Massa F. Lerer A. Bradbury J. Chanan G. Killeen T. Lin Z. Gimelshein N. Antiga L. PyTorch: An Imperative Style, High-Performance Deep Learning Library Advances in Neural Information Processing Systems 32 Wallach H. Larochelle H. Beygelzimer A. Alche-Buc F. Fox E. Garnett R. Curran Associates Inc. Red Hook, NY, USA 2019 8024 8035 

  48. 48. Hevesi P. Phev8/ward-metrics Available online: https://github.com/phev8/ward-metrics (accessed on 6 December 2019) 

  49. 49. Ward J.A. Lukowicz P. Gellersen H.W. Performance metrics for activity recognition ACM Trans. Intell. Syst. Technol. 2011 2 1 23 10.1145/1889681.1889687 

  50. 50. Amazon EC2-P2 Instances Available online: https://aws.amazon.com/ec2/instance-types/p2/ (accessed on 6 December 2019) 

  51. 51. Zhao Y. Yang R. Chevalier G. Xu X. Zhang Z. Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable Sensors Math. Probl. Eng. 2018 2018 10.1155/2018/7316954 

  52. 52. Long J. Sun W. Yang Z. Raymond O.I. Asymmetric Residual Neural Network for Accurate Human Activity Recognition Information 2019 10 203 10.3390/info10060203 

  53. 53. Murahari V.S. Plotz T. On Attention Models for Human Activity Recognition Proceedings of the 2018 ACM International Symposium on Wearable Computers ACM New York, NY, USA 2018 100 103 

  54. 54. Hatami N. Chira C. Classifiers with a reject option for early time-series classification Proceedings of the 2013 IEEE Symposium on Computational Intelligence and Ensemble Learning (CIEL) Singapore 16?19 April 2013 9 16 

관련 콘텐츠

오픈액세스(OA) 유형

GOLD

오픈액세스 학술지에 출판된 논문

저작권 관리 안내
섹션별 컨텐츠 바로가기

AI-Helper ※ AI-Helper는 오픈소스 모델을 사용합니다.

AI-Helper 아이콘
AI-Helper
안녕하세요, AI-Helper입니다. 좌측 "선택된 텍스트"에서 텍스트를 선택하여 요약, 번역, 용어설명을 실행하세요.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.

선택된 텍스트

맨위로