A Nonintrusive and single-point infrastructure-mediated sensing approach for water-use activity recognition

Proceedings - 2013 IEEE International Conference on High Performance Computing and Communications, HPCC 2013 and 2013 IEEE International Conference on Embedded and Ubiquitous Computing, EUC 2013

Volumn , Issue , 2014, Pages 2120-2126

  Hu, Lisha ^a   Chen, Yiqiang ^a   Wang, Shuangquan ^a   Jia, Liping ^b  

^a INSTITUTE OF COMPUTING TECHNOLOGY   (China)

^b BEIHANG UNIVERSITY   (China)

Author keywords

infrastructure mediated sensing; machine learning; nonintrusive; water use activity recognition

Indexed keywords

ARTIFICIAL INTELLIGENCE; HOUSES; LEARNING SYSTEMS; SENSORS; UBIQUITOUS COMPUTING; WATER PIPELINES; WATER PIPING SYSTEMS;

3-AXIS ACCELEROMETER; ACTIVITIES OF DAILY LIVING (ADLS); ACTIVITY RECOGNITION; INFRASTRUCTURE-MEDIATED SENSING; NON-INTRUSIVE; SIGNAL DATA; VIBRATION SIGNAL; WATER PIPES;

PATTERN RECOGNITION;

EID: 84903975445     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/HPCC.and.EUC.2013.304     Document Type: Conference Paper

References (12)

1. R. P. Evans, J. D. Blotter and A. G. Stephens, "Flow rate measurements using flow-induced pipe vibration", Fluids Engineering, vol. 126, pp. 280-285, May 2004.
2. Y. Kim, T. Schmid and Z. M. Charbiwala. "nawms: nonintrusive autonomous water monitoring system", in Proceedings of the 6th ACM Conference on Embeded Network Sensor Systems, 2008, pp. 309-322.
3. S. N. Patel, M. S. Reynolds and G. D. Abowd, "Detecting human movement by differential air pressure sensing in HVAC system ductwork: an exploration in infrastructure mediated sensing", in Proceedings of the 6th International Conference on Pervasive Computing, 2008, pp. 1-18.
4. S. N. Patel, E. P. Stuntebeck and T. Robertson, "PL-Tags: detecting batteryless tags through the power lines in a building", in Proceedings of the 7th International Conference on Pervasive Computing, 2009, pp. 256-273.
5. J. Froehlich et al, "a longitudinal study of pressure sensing to infer real-world water USAge events in the home", in Proceedings of the Ninth International Conference on Pervasive Computing, 2011, pp. 50-69.
6. S. Gupta, M. S. Reynolds and S. N. Patel, "ElectriSense: single-point sensing using EMI for electrical event detection and classification in the home", in Proceedings of the 12th International Conference on Ubiquitous Computing, 2010, pp. 139-148.
7. S. N. Patel, T. Robertson, J. A. Kientz, M. S. Reynolds and G. D. Abowd, "At the flick of a switch: detecting and classifying unique electrical events on the residential power line", in Proceedings of the 9th International Conference on Ubiquitous Computing, 2007, pp. 271-288.
8. D. Lymberopoulos, A. Bamis and A. Savvides, "a methodology for extracting temporal properties from sensor network data streams", in Proceedings of the 7th International Conference on Mobile Systems, Applications and Services, 2009, pp. 193-206.
9. F. James, and A. S. E. H. Carolyn, "Sensing from the basement: a feasibility study of unobtrusive and low-cost home activity recognition", in Proceedings of the 19th Annual ACM Symposium on User Interface Software and Technology, 2006, pp. 91-100.
10. J. Froehlich et al, "HydroSense: Infrastructure-mediated single-point sensing of whole-home water activity", in Proceedings of the 11th International Conference on Ubiquitous Computing, 2009, pp. 235-244.
11. E. Thomaz et al, "Recognizing water-based activities in the home through infrastructure-mediated sensing", in Proceedings of the 14th International Conference on Ubiquitous Computing, 2012, pp. 85-94.
12. C. C. Chang and C. J. Lin, "LIBSVM: a library for support vector machines", ACM Transactions on Intelligent Systems and Techology, 2011, http://www.csie.ntu.edu.tw/~cjlin/libsvm.