Acknowledgement
Grant : 신체기능의 이상이나 저하를 극복하기 위한 휴먼 청각 및 근력 증강 원천기술 개발
Supported by : 정보통신기술진흥센터
References
- H. Osman, ""차세대 스마트폰 시장 동력 기술은 5G.AI.행동인식" 가트너," CIO, Accessed Mar. 29, 2018. http://www.ciokorea.com/news/34639
- D. Cook, K.D. Feuz, and N.C. Krishnan, "Transfer Learning for Activity Recognition: A Survey," Knowl. Inform. Syst., vol. 36, no. 3, Sept. 2013, pp. 537-556. https://doi.org/10.1007/s10115-013-0665-3
- H. Martin, A.M. Bernardos, J. Iglesias, and J.R. Casar, "Activity Logging Using Lightweight Classification Techniques in Mobile Devices," Personal Ubiquitous Comput., vol. 17, no. 4, Apr. 2013, pp. 675-695. https://doi.org/10.1007/s00779-012-0515-4
- L. Bao and S. Intille, "Activity Recognition from User-Annotated Acceleration Data," in Pervasive Computing. Pervasive 2004. Lecture Notes in Computer Science, vol 3001, Berlin, Heidelberg: Springer, 2014.
- O.C. Ann and L.B. Theng, "Human Activity Recognition: a Review," IEEE Int. Conf. Contr. Syst. Comput. Eng., Batu Ferringhi, Malaysia, Nov. 28-30, 2014, pp. 389-393.
- M. Shoaib, S. Bosch, O.D. Incel, H. Scholten, and P.J.M. Havinga, "A Survey of Online Activity Recognition Using Mobile Phones," Sensors, vol. 15, no. 1, 2015, pp. 2059-2085. https://doi.org/10.3390/s150102059
- M.A.A. de la Concepcion, L.M. Soria Morillo, L. Gonzlez-Abril, and J.A. Ortega Ramiez, "Discrete Techniques Applied to Low-Energy Mobile Human Activity Recognition. A New Approach," Expert Syst. Applicat., vol. 41, no. 14, Oct. 2014, pp. 6138-6146. https://doi.org/10.1016/j.eswa.2014.04.018
- S.D. Bersch, D. Azzi, R. Khusainov, I.E. Achumba, and J. Ries, "Sensor Data Acquisition and Processing Parameters for Human Activity Classification," Sensors, vol. 14, no. 3, 2014, pp. 4239-4270. https://doi.org/10.3390/s140304239
- S. Gonzalez, J. Sedano, J.R. Villar, E. Corchado, A. Herrero, and B. Baruque, "Features and Models for Human Activity Recognition," Neurocomput., vol. 167, Nov. 2015, pp. 52-60. https://doi.org/10.1016/j.neucom.2015.01.082
- J.R. Kwapisz, G.M. Weiss, and S.A. Moore, "Activity Recognition using Cell Phone Accelerometers," Sensor KDD, Washington, DC, USA, July 25, 2010, pp. 1-9.
- D. Ravi, C. Wong, B. Lo, and G.Z. Yang, "Deep Learning for Human Activity Recognition: A Resource Efficient Implementation on Low-Power Devices," IEEE Int. Conf. Wearable Implantable BSN, San Francisco, CA, USA, June 14-17, 2016, pp.71-76.
- J. Wang, Y. Chen, S. Hao, X. Peng, and L. Hu, "Deep Learning for Sensor-Based Activity Recognition: A Survey," Pattern, Recogn. Lett., Feb. 21, 2018.
- Y. Vaizman, N. Weibel, and G. Lanckriet, "Context Recognition In-the-Wild: Unified Model for Multi-modal Sensors and Multi-label Classification," Proc. Interactive Mobile Wearable Ubiquitous, Technol., vol. 1, no. 4, Dec. 2017, pp. 168:1-168:22.
- Y. Vaizman, K. Ellis, G. Lanckriet, and N. Weibel, "ExtraSensory App: Data Collection In-the-Wild with Rich User Interface to Self-Report Behavior," Proc. CHI Conf. Human Factors Comput. Syst., Montreal, Canada, Apr. 21-26, 2018, pp. 1-12.
- 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," in Proc. of IWAAL, Lecture Notes in Computer Science, vol 7657, Berlin, Heidelberg: Springer, 2012.
- W. Jiang and Z. Yin, "Human Activity Recognition Using Wearable Sensors by Deep Convolutional Neural Networks," Proc. ACM Int. Conf. Multimedia, Brisbane, Australia, Oct. 26-30, 2015, pp. 1307-1310.
- D. Micucci, M. Mobilio, and P. Napoletano, "UniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones," Appl. Sci., vol. 7, no. 10, 2017, pp. 1101:1-1101:19.
- Y. Vaizman, K. Ellis, and G. Lanckriet, "Recognizing Detailed Human Context In-the-Wild from Smartphones and Smartwatches," IEEE Pervasive Comput., vol. 16, no. 4, 2017, pp. 62-74.