撤稿:基于无线传感器网络的室内人体日常动作识别
RETRACTED:Indoor Human Activity Recognition Using Wireless Sensor Networks

作者: 罗晓牧 , 谭火媛 :广州中医药大学,医学信息工程学院,广东 广州 ;

关键词: 无线传感器网络热释电红外传感器定位跟踪动作识别随机森林Wireless Sensor Network Pyroelectric Infrared Sensor Locomotion Tracking Activity Recognition Random Forest

摘要:

撤稿声明:“基于无线传感器网络的室内人体日常动作识别”一文刊登在2017年4月出版的《无线通信》2017年第7卷第2期第53-69页上。因统计数据有误等问题,根据国际出版流程,编委会现决定撤除此稿件,保留原出版出处:罗晓牧, 谭火媛. 基于无线传感器网络的室内人体日常动作识别[J]. 无线通信, 2017, 7(2): 53-69. https://doi.org/10.12677/HJWC.2017.72008

文章引用: 罗晓牧 , 谭火媛 (2017) 撤稿:基于无线传感器网络的室内人体日常动作识别。 无线通信, 7, 53-69. doi: 10.12677/HJWC.2017.72008

参考文献

[1] Morgan, L.A., Perez, R., Frankowski, A.C., Nemec, M. and Bennett, C.R. (2016) Mental Illness in Assisted Living: Challenges for Quality of Life and Care. Journal of Housing for the Elderly, 30, 185-198.
https://doi.org/10.1080/02763893.2016.1162255

[2] Turaga, P., Chellappa, R., Subrahmanian, V. and Udrea, O. (2008) Machine Recognition of Human Activities: A Survey. IEEE Transactions on Circuits and Systems for Video Technology, 18, 1473-1488.
https://doi.org/10.1109/tcsvt.2008.2005594

[3] Liu, A.-A., Su, Y.-T., Nie, W.-Z. and Yang, Z.-X. (2015) Jointly Learning Multiple Sequential Dynamics for Human Action Recognition. PLoS ONE, 10, e0130884.
https://doi.org/10.1371/journal.pone.0130884

[4] Chaquet, J.M., Carmona, E.J. and Fernandez-Caballero, A. (2013) A Survey of Video Datasets for Human Action and Activity Recognition. Computer Vision and Image Understanding, 117, 633-659.
https://doi.org/10.1016/j.cviu.2013.01.013

[5] Ryoo, M.S., Rothrock, B., Fleming, C. and Yang, H.J. (2016) Privacy Preserving Human Activity Recognition from Extreme Low Resolution. arXiv preprint arXiv:1604.03196

[6] Storm, F.A., Heller, B.W. and Mazza, C. (2015) Step Detection and Activity Recognition Accuracy of Seven Physical Activity Monitors. PLoS ONE, 10, e0118723.
https://doi.org/10.1371/journal.pone.0118723

[7] Zhu, C., Sheng, W. and Liu, M. (2015) Wearable Sensor-Based Behavioral Anomaly Detection in Smart Assisted Living Systems. IEEE Transactions on Automation Science and Engineering, 12, 1225-1234.
https://doi.org/10.1109/tase.2015.2474743

[8] Chodon, P., Adhikari, D.M., Nepal, G.C., Biswa, R., Gyeltshen, S., et al. (2013) Passive Infrared (PIR) Sensor Based Security System. International Journal of Electrical, Electronics & Computer Systems, 14.

[9] Lee, M., Guo, R. and Bhalla, A.S. (1998) Pyroelectric Sensors. Journal of Electroceramics, 2, 229-242.

[10] Wilson, D.H. and Atkeson, C. (2005) Simultaneous Tracking and Activity Recognition (STAR) Using Many Anonymous, Binary Sensors. In: Gellersen, H.W., Want, R. and Schmidt, A., Eds., Pervasive Computing. Pervasive 2005. Lecture Notes in Computer Science, Vol. 3468, Springer, Berlin, Heidelberg, 62-79.
https://doi.org/10.1007/11428572_5

[11] Zhu, C. and Sheng, W. (2011) Motion- and Location-Based Online Human Daily Activity Recognition. Pervasive and Mobile Computing, 7, 256-269.
https://doi.org/10.1016/j.pmcj.2010.11.004

[12] Hao, Q., Brady, D., Guenther, B., Burchett, J., Shankar, M. and Feller, S. (2006) Human Tracking with Wireless Distributed Pyroelectric Sensors. IEEE Sensors Journal, 6, 1683-1696.
https://doi.org/10.1109/jsen.2006.884562

[13] Hao, Q., Hu, F. and Xiao, Y. (2009) Multiple Human Tracking and Identification with Wireless Distributed Pyroelectric Sensor Systems. IEEE Systems Journal, 3, 428-439.
https://doi.org/10.1109/JSYST.2009.2035734

[14] Yang, B. and Zhang, M. (2017) Credit-Based Multiple Human Location for Passive Binary Pyroelectric Infrared Sensor Tracking System: Free from Region Partition and Classifier. IEEE Sensors Journal, 17, 37-45.

[15] Xiong, J., Li, F., Zhao, N. and Jiang, N. (2014) Tracking and Recognition of Multiple Human Targets Moving in a Wireless Pyroelectric Infrared Sensor Network. Sensors, 14, 7209-7228.
https://doi.org/10.3390/s140407209

[16] Luo, X., Liu, T., Shen, B., Gao, L., Luo, X., et al. (2016) Human Indoor Localization Based on Ceiling Mounted PIR Sensor Nodes. 13th IEEE Annual Consumer Communications & Networking Conference (CCNC), Las Vegas, 9-12 January 2016, 868-874.
https://doi.org/10.1109/CCNC.2016.7444903

[17] Liu, T. and Liu, J. (2014) Design and Implementation of a Compressive Infrared Sampling for Motion Acquisition. EURASIP Journal on Advances in Signal Processing, 2014, 20.
https://doi.org/10.1186/1687-6180-2014-20

[18] Luo, X., Tan, H., Guan, Q., Liu, T., Zhuo, H.H. and Shen, B. (2016) Abnormal Activity Detection Using Pyroelectric Infrared Sensors. Sensors, 16, 822.
https://doi.org/10.3390/s16060822

[19] Guan, Q., Yin, X., Guo, X. and Wang, G. (2016) A Novel Infrared Motion Sensing System for Compressive Classification of Physical Activity. IEEE Sensors Journal, 16, 2251-2259.
https://doi.org/10.1109/JSEN.2016.2514606

[20] Brady, D., Pitsianis, N. and Sun, X. (2004) Reference Structure Tomography. Journal of the Optical Society of America A, Optics, Image Science, and Vision, 21, 1140-1147.
https://doi.org/10.1364/JOSAA.21.001140

[21] Lu, L., Zhang, H. and Jiang, H. (2002) Content Analysis for Audio Classification and Segmentation. IEEE Transactions on Speech and Audio Processing, 10, 504-516.
https://doi.org/10.1109/TSA.2002.804546

[22] Barber, D. (2012) Bayesian Reasoning and Machine Learning. Cambridge University Press, Cambridge.

[23] Fernandez-Delgado, M., Cernadas, E., Barro, S. and Amorim, D. (2014) Do We Need Hundreds of Classifiers to Solve Real World Classification Problems. Journal of Machine Learning Research, 15, 3133-3181.

[24] Ooi, S.Y., Tan, S.C. and Cheah, W.P. (2016) Temporal Sampling Forest (TS-F): An Ensemble Temporal Learner. Soft Computing, 1-14.

[25] Breiman, L. (2001) Random Forests. Machine Learning, 45, 5-32.
https://doi.org/10.1023/A:1010933404324

[26] Feng, Z., Mo, L. and Li, M. (2015) A Random Forest-Based Ensemble Method for Activity Recognition. 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, 25-29 August 2015, 5074-5077.
https://doi.org/10.1109/EMBC.2015.7319532

[27] Shotton, J., Sharp, T., Kipman, A., Fitzgibbon, A., Finocchio, M., Blake, A., Cook, M. and Moore, R. (2013) Real- Time Human Pose Recognition in Parts from Single Depth Images. Communications of the ACM, 56, 116-124.
https://doi.org/10.1145/2398356.2398381

[28] Alliance, Z. Zigbee Specification. ZigBee Alliance. http://www.zigbee.org/

[29] Brdiczka, O., Reignier, P. and Crowley, J.L. (2007) Detecting Individual Activities from Video in a Smart Home. In: Apolloni, B., Howlett, R.J. and Jain, L., Eds., Knowledge-Based Intelligent Information and Engineering Systems. KES 2007. Lecture Notes in Computer Science, Vol. 4692, Springer, Berlin, Heidelberg, 363-370.
https://doi.org/10.1007/978-3-540-74819-9_45

[30] Jalal, A., Kamal, S. and Kim, D. (2014) A Depth Video Sensor-Based Life Logging Human Activity Recognition System for Elderly Care in Smart Indoor Environments. Sensors, 14, 11735-11759.
https://doi.org/10.3390/s140711735

[31] Saeb, S., Kording, K. and Mohr, D.C. (2015) Making Activity Recognition Robust against Deceptive Behavior. PLoS ONE, 10, e0144795.
https://doi.org/10.1371/journal.pone.0144795

[32] Liao, L., Fox, D. and Kautz, H. (2007) Hierarchical Conditional Random Fields for GPS-Based Activity Recognition. In: Thrun, S., Brooks, R. and Durrant-Whyte, H., Eds., Robotics Research, Springer, Berlin, Heidelberg, 487-506.
https://doi.org/10.1007/978-3-540-48113-3_41

[33] Raman, N. and Maybank, S. (2016) Activity Recognition Using a Supervised Non-Parametric Hierarchical HMM. Neurocomputing, 199, 163-177.
https://doi.org/10.1016/j.neucom.2016.03.024

[34] 有限状态机. 维基百科[EB/OL]. https://zh.wikipedia.org/wiki/有限状态机

分享
Top