{"title":"Fault Detection and Identification of COSMED K4b2 Based On PCA and Neural Network","authors":"Jing Zhou, Steven Su, Aihuang Guo","volume":72,"journal":"International Journal of Computer and Information Engineering","pagesStart":1678,"pagesEnd":1684,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/11613","abstract":"
COSMED K4b2 is a portable electrical device designed to test pulmonary functions. It is ideal for many applications that need the measurement of the cardio-respiratory response either in the field or in the lab is capable with the capability to delivery real time data to a sink node or a PC base station with storing data in the memory at the same time. But the actual sensor outputs and data received may contain some errors, such as impulsive noise which can be related to sensors, low batteries, environment or disturbance in data acquisition process. These abnormal outputs might cause misinterpretations of exercise or living activities to persons being monitored. In our paper we propose an effective and feasible method to detect and identify errors in applications by principal component analysis (PCA) and a back propagation (BP) neural network.<\/p>\r\n","references":"[1] COSMED K4b2 Cardio Pulmonary Exercise Testing (Online).Available:\r\nhttp:\/\/www.cosmed.com\/images\/pdf\/productliterature\/K4b2_Brochure_\r\nEN_C09052-02-93_A4_web.pdf.\r\n[2] R. Duffield, B. Dawson, H. Pinnington, and P. Wong, \"Accuracy and\r\nreliability of a Cosmed K4b 2 portable gas,\" Sci Med Sport, vol. 7, no.1,\r\npp. 11-22, Jan. 2004.\r\n[3] C. Srl, \"K4b2 User manual, XVIII Edition,\" Rome, Italy:\r\nhttp:\/\/www.cosmed.it.\r\n[4] W.Haerdle and L. Simar, Applied Multivariate Statistical Analysis,\r\nSecond Edition. Heidelberg: Springer, pp. 37-41, 2007.\r\n[5] L. H. Xie Tingfeng, Wu Jianjun, \"Fault Detection and Diagnosis for\r\nSensors of LRE Based on PCA,\" Journal of Astronautics, vol. 28, no.1,\r\npp. 668-1672, 1703, Jan, 2007.\r\n[6] L. Yu, J.-h. Zhu and L.-j. Chen, \"Parametric Study on PCA-based\r\nAlgorithm for Structural Health,\" in 2010 the Prognostics & System\r\nHealth Management Conference, pp.1-6.\r\n[7] P. Picton, Neural Networks. New York: Palgrave Houndmills, pp.\r\n215-229, 2000.\r\n[8] J. Liu, H. Chang, T.Y.Hsu, and X. Ruan, \"Prediction of the flow stress of\r\nhigh-speed steel during hot performation using a BP artifical neural\r\nnetwork,\" Journal of Materials Processing Technology vol. 103, no.2, pp.\r\n200-205, Feb, 2000.\r\n[9] Y. Wang, D. Gu, J. Xu, and J. Li, \"Back Propagation Neural Network for\r\nShort-term Electricity Load Forecasting with Weather Features,\" in 2009\r\nInternational Conference on Computational Intelligence and Natural\r\nComputing , pp. 58-61.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 72, 2012"}