참고문헌 (102)

1. Wannenburg, Johan, Malekian, Reza. Physical Activity Recognition From Smartphone Accelerometer Data for User Context Awareness Sensing. IEEE transactions on systems, man, and cybernetics. Systems, vol.47, no.12, 3142-3149.

   상세보기

2. Bi, Haixia, Perello-Nieto, Miquel, Santos-Rodriguez, Raul, Flach, Peter. Human Activity Recognition Based on Dynamic Active Learning. IEEE journal of biomedical and health informatics, vol.25, no.4, 922-934.

   상세보기

3. 10.18356/88fa44e7-en 

   

4. Ghasemzadeh, H, Jafari, R. Physical Movement Monitoring Using Body Sensor Networks: A Phonological Approach to Construct Spatial Decision Trees. IEEE transactions on industrial informatics, vol.7, no.1, 66-77.

   상세보기

5. Li, Pengfei, Wang, Yu, Tian, Yu, Zhou, Tian-Shu, Li, Jing-Song. An Automatic User-Adapted Physical Activity Classification Method Using Smartphones. IEEE transactions on bio-medical engineering, vol.64, no.3, 706-714.

   상세보기

6. Chen, Zhenghua, Jiang, Chaoyang, Xie, Lihua. A Novel Ensemble ELM for Human Activity Recognition Using Smartphone Sensors. IEEE transactions on industrial informatics, vol.15, no.5, 2691-2699.

   상세보기

7. Wang, Yi, Jiang, Xinli, Cao, Rongyu, Wang, Xiyang. Robust Indoor Human Activity Recognition Using Wireless Signals. Sensors, vol.15, no.7, 17195-17208.

   상세보기

8. Ronao, C.A., Cho, S.B.. Human activity recognition with smartphone sensors using deep learning neural networks. Expert systems with applications, vol.59, 235-244.

   상세보기

9. Monteiro-Guerra, Francisco, Rivera-Romero, Octavio, Fernandez-Luque, Luis, Caulfield, Brian. Personalization in Real-Time Physical Activity Coaching Using Mobile Applications: A Scoping Review. IEEE journal of biomedical and health informatics, vol.24, no.6, 1738-1751.

   상세보기

10. Kwapisz, Jennifer R., Weiss, Gary M., Moore, Samuel A.. Activity recognition using cell phone accelerometers. SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowlege Discovery & Data Mining, vol.12, no.2, 74-82.

    상세보기

11. Proc. Eur. Symp. Artif. Neural Netw., Comput. Intell. Mach. Learn. A public domain dataset for human activity recognition using smartphones Anguita 1 

12. 10.1109/ISMSIT.2018.8567275 

    

13. Voicu, Robert-Andrei, Dobre, Ciprian, Bajenaru, Lidia, Ciobanu, Radu-Ioan. Human Physical Activity Recognition Using Smartphone Sensors. Sensors, vol.19, no.3, 458-.

    상세보기

14. 10.1007/978-3-319-26561-2_6 

    

15. Bulling, Andreas, Blanke, Ulf, Schiele, Bernt. A tutorial on human activity recognition using body-worn inertial sensors. ACM computing surveys, vol.46, no.3, 1-33.

    상세보기

16. Yao, Lina, Sheng, Quan Z., Benatallah, Boualem, Dustdar, Schahram, Wang, Xianzhi, Shemshadi, Ali, Kanhere, Salil S.. WITS: an IoT-endowed computational framework for activity recognition in personalized smart homes. Computing, vol.100, no.4, 369-385.

    상세보기

17. Cvetković, Božidara, Szeklicki, Robert, Janko, Vito, Lutomski, Przemyslaw, Luštrek, Mitja. Real-time activity monitoring with a wristband and a smartphone. Information fusion, vol.43, 77-93.

    상세보기

18. 10.4108/icst.mobicase.2014.257786 

    

19. Lara, Oscar D., Labrador, Miguel A.. A Survey on Human Activity Recognition using Wearable Sensors. IEEE Communications surveys and tutorials, vol.15, no.3, 1192-1209.

    상세보기

20. 10.1145/1107548.1107591 

    

21. Proc. 24th Int. Conf. Artif. Intell. Deep convolutional neural networks on multichannel time series for human activity recognition Yang 3995 

22. CoRR Deep activity recognition models with triaxial accelerometers Alsheikh abs/1511.04664 1 2015 

23. Proc. 25th Int. Joint Conf. Artif. Intell. Deep, convolutional, and recurrent models for human activity recognition using wearables Hammerla 1533 

24. Wang, Yasi, Yao, Hongxun, Zhao, Sicheng. Auto-encoder based dimensionality reduction. Neurocomputing, vol.184, 232-242.

    상세보기

25. Ryu, Seunghyoung, Choi, Hyungeun, Lee, Hyoseop, Kim, Hongseok. Convolutional Autoencoder Based Feature Extraction and Clustering for Customer Load Analysis. IEEE transactions on power systems : a publication of the Power Engineering Society, vol.35, no.2, 1048-1060.

    상세보기

26. 10.1145/1553374.1553453 

    

27. Essien, Aniekan, Giannetti, Cinzia. A Deep Learning Model for Smart Manufacturing Using Convolutional LSTM Neural Network Autoencoders. IEEE transactions on industrial informatics, vol.16, no.9, 6069-6078.

    상세보기

28. Proc. 28th Int. Conf. Neural Inf. Process. Syst. Convolutional LSTM network: A machine learning approach for precipitation nowcasting Shi 1 802 

29. Steven Eyobu, Odongo, Han, Dong Seog. Feature Representation and Data Augmentation for Human Activity Classification Based on Wearable IMU Sensor Data Using a Deep LSTM Neural Network. Sensors, vol.18, no.9, 2892-.

    상세보기

30. Thakur, Dipanwita, Biswas, Suparna. Smartphone based human activity monitoring and recognition using ML and DL: a comprehensive survey. Journal of ambient intelligence and humanized computing, vol.11, no.11, 5433-5444.

    상세보기

31. Anguita, D., Ghio, A., Oneto, L., Parra, X., Reyes-Ortiz, J.L.. Human Activity Recognition on Smartphones Using a Multiclass Hardware-Friendly Support Vector Machine. Lecture notes in computer science, vol.7657, 216-223.

    상세보기

32. 10.1109/ChiCC.2016.7553975 

    

33. 10.23919/ICITST.2017.8356374 

    

34. 10.1145/3195106.3195157 

    

35. 10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00071 

    

36. 10.1109/ECACE.2019.8679226 

    

37. Zhu, Jiadong, San-Segundo, Rubén, Pardo, José M.. Feature extraction for robust physical activity recognition. Human-centric computing and information sciences, vol.7, no.1, 16-.

    상세보기

38. 10.1109/IntelCIS.2015.7397283 

    

39. 10.1109/ICCCC.2016.7496749 

    

40. Nguyen, Nhan Duc, Bui, Duong Trong, Truong, Phuc Huu, Jeong, Gu-Min. Position-Based Feature Selection for Body Sensors regarding Daily Living Activity Recognition. Journal of sensors, vol.2018, 1-13.

    상세보기

41. Attal, Ferhat, Mohammed, Samer, Dedabrishvili, Mariam, Chamroukhi, Faicel, Oukhellou, Latifa, Amirat, Yacine. Physical Human Activity Recognition Using Wearable Sensors. Sensors, vol.15, no.12, 31314-31338.

    상세보기

42. Ponce, Hiram, Martínez-Villaseñor, María de Lourdes, Miralles-Pechuán, Luis. A Novel Wearable Sensor-Based Human Activity Recognition Approach Using Artificial Hydrocarbon Networks. Sensors, vol.16, no.7, 1033-.

    상세보기

43. Jansi, R., Amutha, R.. A novel chaotic map based compressive classification scheme for human activity recognition using a tri-axial accelerometer. Multimedia tools and applications, vol.77, no.23, 31261-31280.

    상세보기

44. Rosati, Samanta, Balestra, Gabriella, Knaflitz, Marco. Comparison of Different Sets of Features for Human Activity Recognition by Wearable Sensors. Sensors, vol.18, no.12, 4189-.

    상세보기

45. Köping, Lukas, Shirahama, Kimiaki, Grzegorzek, Marcin. A general framework for sensor-based human activity recognition. Computers in biology and medicine, vol.95, 248-260.

    상세보기

46. Ravi, Daniele, Wong, Charence, Lo, Benny, Yang, Guang-Zhong. A Deep Learning Approach to on-Node Sensor Data Analytics for Mobile or Wearable Devices. IEEE journal of biomedical and health informatics, vol.21, no.1, 56-64.

    상세보기

47. 10.1007/978-3-030-01298-4_10 

    

48. Appl. Soft Comput. Real-time human activity recognition from accelerometer data using convolutional neural networks Andrey 62 915 2017 

49. Moya Rueda, Fernando, Grzeszick, René, Fink, Gernot A., Feldhorst, Sascha, Ten Hompel, Michael. Convolutional Neural Networks for Human Activity Recognition Using Body-Worn Sensors. Informatics, vol.5, no.2, 26-.

    상세보기

50. 10.1109/IranianCEE.2019.8786578 

    

51. 10.1109/ICIT48102.2019.00061 

    

52. Zebin, Tahmina, Scully, Patricia J., Peek, Niels, Casson, Alexander J., Ozanyan, Krikor B.. Design and Implementation of a Convolutional Neural Network on an Edge Computing Smartphone for Human Activity Recognition. IEEE access : practical research, open solutions, vol.7, 133509-133520.

    상세보기

53. Zhou, Baoding, Yang, Jun, Li, Qingquan. Smartphone-Based Activity Recognition for Indoor Localization Using a Convolutional Neural Network. Sensors, vol.19, no.3, 621-.

    상세보기

54. Qi, Wen, Su, Hang, Yang, Chenguang, Ferrigno, Giancarlo, De Momi, Elena, Aliverti, Andrea. A Fast and Robust Deep Convolutional Neural Networks for Complex Human Activity Recognition Using Smartphone. Sensors, vol.19, no.17, 3731-.

    상세보기

55. 10.1109/SysCon47679.2020.9275924 

    

56. Cruciani, Federico, Vafeiadis, Anastasios, Nugent, Chris, Cleland, Ian, McCullagh, Paul, Votis, Konstantinos, Giakoumis, Dimitrios, Tzovaras, Dimitrios, Chen, Liming, Hamzaoui, Raouf. Feature learning for Human Activity Recognition using Convolutional Neural Networks : A case study for Inertial Measurement Unit and audio data. Ccf transactions on pervasive computing and interaction, vol.2, no.1, 18-32.

    상세보기

57. Yen, Chih-Ta, Liao, Jia-Xian, Huang, Yi-Kai. Human Daily Activity Recognition Performed Using Wearable Inertial Sensors Combined With Deep Learning Algorithms. IEEE access : practical research, open solutions, vol.8, 174105-174114.

    상세보기

58. Wan, Shaohua, Qi, Lianyong, Xu, Xiaolong, Tong, Chao, Gu, Zonghua. Deep Learning Models for Real-time Human Activity Recognition with Smartphones. Mobile networks and applications : MONET, vol.25, no.2, 743-755.

    상세보기

59. Li, Y., Shi, D., Ding, B., Liu, D.. Unsupervised Feature Learning for Human Activity Recognition Using Smartphone Sensors. Lecture notes in computer science, vol.8891, 99-107.

    상세보기

60. Hasan, M., Roy-Chowdhury, A.K.. Continuous Learning of Human Activity Models Using Deep Nets. Lecture notes in computer science, vol.8691, 705-720.

    상세보기

61. Int. J. Comput. Sci. Netw. Secur. An effective deep autoencoder approach for online smartphonebased human activity recognition Almaslukh 17 4 160 2017 

62. 10.1145/3341162.3344854 

    

63. Gao, Xile, Luo, Haiyong, Wang, Qu, Zhao, Fang, Ye, Langlang, Zhang, Yuexia. A Human Activity Recognition Algorithm Based on Stacking Denoising Autoencoder and LightGBM. Sensors, vol.19, no.4, 947-.

    상세보기

64. Ozcan, Tayyip, Basturk, Alper. Human action recognition with deep learning and structural optimization using a hybrid heuristic algorithm. Cluster computing : the journal of networks, software tools and applications, vol.23, no.4, 2847-2860.

    상세보기

65. Prabono, Aria Ghora, Yahya, Bernardo Nugroho, Lee, Seok-Lyong. Atypical Sample Regularizer Autoencoder for Cross-Domain Human Activity Recognition. Information systems frontiers, vol.23, no.1, 71-80.

    상세보기

66. Garcia, Kemilly Dearo, de Sá, Cláudio Rebelo, Poel, Mannes, Carvalho, Tiago, Mendes-Moreira, João, Cardoso, João M.P., de Carvalho, André C.P.L.F., Kok, Joost N.. An ensemble of autonomous auto-encoders for human activity recognition. Neurocomputing, vol.439, 271-280.

    상세보기

67. 10.1007/978-3-030-75768-7_28 

    

68. 10.2991/iccsae-15.2016.173 

    

69. 10.1145/3286978.3287024 

    

70. 10.1109/EMBC.2018.8513115 

    

71. 10.1109/ICMCCE.2018.00052 

    

72. Zhao, Yu, Yang, Rennong, Chevalier, Guillaume, Xu, Ximeng, Zhang, Zhenxing. Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable Sensors. Mathematical problems in engineering, vol.2018, 1-13.

    상세보기

73. Ordóñez, Francisco Javier, Roggen, Daniel. Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition. Sensors, vol.16, no.1, 115-.

    상세보기

74. 10.1109/PDCAT46702.2019.00055 

    

75. Wang, Huaijun, Zhao, Jing, Li, Junhuai, Tian, Ling, Tu, Pengjia, Cao, Ting, An, Yang, Wang, Kan, Li, Shancang. Wearable Sensor-Based Human Activity Recognition Using Hybrid Deep Learning Techniques. Security and communication networks, vol.2020, 1-12.

    상세보기

76. 10.1109/ICAIIC48513.2020.9065078 

    

77. Ercolano, Giovanni, Rossi, Silvia. Combining CNN and LSTM for activity of daily living recognition with a 3D matrix skeleton representation. Intelligent service robotics, vol.14, no.2, 175-185.

    상세보기

78. Ye, W., Cheng, J., Yang, F., Xu, Y.. Two-Stream Convolutional Network for Improving Activity Recognition Using Convolutional Long Short-Term Memory Networks. IEEE access : practical research, open solutions, vol.7, 67772-67780.

    상세보기

79. Xu, Cheng, Chai, Duo, He, Jie, Zhang, Xiaotong, Duan, Shihong. InnoHAR: A Deep Neural Network for Complex Human Activity Recognition. IEEE access : practical research, open solutions, vol.7, 9893-9902.

    상세보기

80. Xia, Kun, Huang, Jianguang, Wang, Hanyu. LSTM-CNN Architecture for Human Activity Recognition. IEEE access : practical research, open solutions, vol.8, 56855-56866.

    상세보기

81. 10.1109/ICC.2018.8422895 

    

82. Chavarriaga, R., Sagha, H., Calatroni, A., Digumarti, S.T., Troster, G., Millan, J.d.R., Roggen, D.. The Opportunity challenge: A benchmark database for on-body sensor-based activity recognition. Pattern recognition letters, vol.34, no.15, 2033-2042.

    상세보기

83. 10.1109/INSS.2010.5573462 

    

84. 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. Lecture notes in computer science, vol.4913, 17-33.

    상세보기

85. 10.1145/2003653.2003656 

    

86. Bachlin, M., Plotnik, M., Roggen, D., Maidan, I., Hausdorff, J.M., Giladi, N., Troster, G.. Wearable Assistant for Parkinson’s Disease Patients With the Freezing of Gait Symptom. IEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology Society, vol.14, no.2, 436-446.

    상세보기

87. 10.1007/978-3-030-10997-4_33 

    

88. 10.1109/ISWC.2012.13 

    

89. Vaizman, Yonatan, Ellis, Katherine, Lanckriet, Gert. Recognizing Detailed Human Context in the Wild from Smartphones and Smartwatches. IEEE pervasive computing, vol.16, no.4, 62-74.

    상세보기

90. Karim, Fazle, Majumdar, Somshubra, Darabi, Houshang, Harford, Samuel. Multivariate LSTM-FCNs for time series classification. Neural networks : the official journal of the International Neural Network Society, vol.116, 237-245.

    상세보기

91. 10.23919/ChiCC.2019.8865142 

    

92. Liu, Xingchen, Zhou, Qicai, Zhao, Jiong, Shen, Hehong, Xiong, Xiaolei. Fault Diagnosis of Rotating Machinery under Noisy Environment Conditions Based on a 1-D Convolutional Autoencoder and 1-D Convolutional Neural Network. Sensors, vol.19, no.4, 972-.

    상세보기

93. 10.7551/mitpress/7503.003.0024 

    

94. Layer Wrappers Chollet 2015 

95. Masci, J., Meier, U., Ciresan, D., Schmidhuber, J.. Stacked Convolutional Auto-Encoders for Hierarchical Feature Extraction. Lecture notes in computer science, vol.6791, 52-59.

    상세보기

96. Hochreiter, Sepp, Schmidhuber, Jürgen. Long Short-Term Memory. Neural computation, vol.9, no.8, 1735-1780.

    상세보기

97. arXiv:1412.6980 Adam: A method for stochastic optimization Kingma 2014 

98. Cawley, Gavin C., Talbot, Nicola L. C.. Efficient approximate leave-one-out cross-validation for kernel logistic regression. Machine learning, vol.71, no.2, 243-264.

    상세보기

99. 10.1109/EUVIP47703.2019.8946180 

    

100. 10.2307/2279372 

     

101. 10.1214/aoms/1177731944 

     

102. Armstrong, Richard A.. When to use the Bonferroni correction. Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists), vol.34, no.5, 502-508.

     상세보기