Spatial-frequency data acquisition using rotational invariant pattern matching in smart environments

  • Michael P. Poland
  • Chris D. Nugent
  • Hui Wang
  • Liming Chen


This article details the development and testing of an empirical data capture system with the ability to collect spatial-frequency statistics relating to the movement behaviour of a smart home inhabitant. This is achieved using a greyscale normalised cross-correlation pattern matching algorithm. Environmental obstructions on the floor space can also be inferred from a visual representation of the accumulated data. Whilst this methodology itself is not novel, its application to person tracking specifically within a smart home environment does not appear in the literature and is considered a novel approach. The results of tests performed on the pattern matching technique show a tracking competency rate of 94.45% with a standard deviation of 0.009027, indicating high fidelity across a wide variety of environmental factors.


Human positioning Indoor tracking Pattern matching Smart environments 


  1. 1.
    Demongeot J, Virone G, Duchêne F, Benchetrit G, Hervé T, Noury N, Rialle V (2002) Multi-sensors acquisition, data fusion, knowledge mining and alarm triggering in health smart homes for elderly people. CR Biol 325(6):673–682CrossRefGoogle Scholar
  2. 2.
    Chan M, Hariton C, Ringeard P, Campo E (1995) Smart house automation system for the elderly and the disabled. Proceedings of the IEEE International Conference on Systems, Man and Cybernetics. pp 1586–1589Google Scholar
  3. 3.
    Cook DJ, Das SK (2007) How smartare our environments? An updated look at the state of the art. Pervasive Mobile Comput 3(2):53–73CrossRefGoogle Scholar
  4. 4.
    Zhang S, McClean S, Scotney B, Hong X, Nugent C, Mulvenna M (2008) Decision support for Alzheimer’s patients in smart homes. 21st IEEE International Symposium on Computer-Based Medical Systems. CBMS ‘08. pp 236–24Google Scholar
  5. 5.
    Brummel-Smith K, Dangiolo M (2009) Assistive technologies in the home. Clin Geriatr Med 25(1):61–77CrossRefGoogle Scholar
  6. 6.
    Riedel DE, Venkatesh S, Wanquan L (2005) Spatial activity recognition in a smart home environment using a chemotactic model. Proceedings of the 2005 International Conference on Intelligent Sensors, Sensor Networks and Information Processing Conference, pp 301–306Google Scholar
  7. 7.
    Wen-Hau L, Chao-Lin W, Li-Chen F (2008) Inhabitants tracking system in a cluttered home environment via floor load sensors. IEEE Trans Automation Sci Eng 5(1):10–20CrossRefGoogle Scholar
  8. 8.
    Nirmalya R, Abhishek R, Das SK (2006) Context-aware resource management in multi-inhabitant smart homes a Nash H-learning based approach. Fourth Annual IEEE International Conference on Pervasive Computing and Communications, pp 147–158Google Scholar
  9. 9.
    Helal S, Winkler B, Choonhwa L, Kaddoura Y, Ran L, Giraldo C, Kuchibhotla S, Mann W (2003) Enabling location-aware pervasive computing applications for the elderly. Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, pp 531–536Google Scholar
  10. 10.
    AT&T Laboratories Cambridge Archive, “The Bat Ultrasonic Location System”., (July 2009)
  11. 11.
    Hexamite Ultrasound, “Microcomputer’s BatVision”,, (July 2009)
  12. 12.
    Iadanza E, Dori F, Miniati R, Bonaiuti R (2008) Patients tracking and identifying inside hospital: a multilayer method to plan an RFId solution. 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp 1462–1465Google Scholar
  13. 13.
    Tsai MH, Huang CL, Chung PC, Yang YK, Hsu YC, Hsiao SL (2006) A psychiatric patients tracking system. Proceedings of the IEEE International Symposium on Circuits and Systems. pp 4049–4053Google Scholar
  14. 14.
    Hallberg J, Nilsson M, Synnes K (2003) Positioning with Bluetooth. Proceedings of the 10th International Conference on Telecommunications 2:954–958Google Scholar
  15. 15.
    Pham VT, Qiu Q, Phyo AA, Biswas J (2006) Application of ultrasonic sensors in a smartenvironment. Pervasive Mobile Comput 3:180–207CrossRefGoogle Scholar
  16. 16.
    Chan M, Estève D, Escriba C, Campo E (2008) A review of smart homes—present state and future challenges. Comput Meth Programs Biomed 91(1):55–81CrossRefGoogle Scholar
  17. 17.
    Huijsing JH, Riedijk FR, van der Horn G (1994) Developments in integrated smartsensors. Phys Sens Actua A 43(1–3):76–288Google Scholar
  18. 18.
    Celler BG, Hesketh T, Earnshaw W, Ilsar E (1994) An instrumentation system for the remote monitoring of changes in functional health status of the elderly at home. Proceedings of the International Conference on IEEE-EMBS, pp 908–909Google Scholar
  19. 19.
    Celler BG, Earnshaw W, Ilsar ED, Betbeder-Matibet L, Harris MF, Clark R, Hesketh T, Lovell NH (1995) Remote monitoring of health status of the elderly at home. A multidisciplinary project on aging at the University of New South Wales. Int J Biomed Comput 40:147–155CrossRefGoogle Scholar
  20. 20.
    Yamazaki T (2006) Beyond the smart home. Proceedings of the International Conference on Hybrid Information Technology, pp 350–355Google Scholar
  21. 21.
    Kidd CD, Orr RJ, Abowd GD, Atkeson CG, Essa IA, MacIntyre B, Mynatt E, Starner TE, Newstetter W (1999) The aware home: a living laboratory for ubiquitous computing Research. Proceedings of 2nd International Workshop on Cooperative buildingGoogle Scholar
  22. 22.
    Addlesee MD, Jones A, Livesey F, Samaria F (1997) The ORL active floor [sensor system]. IEEE Personal Commun 4(5):35–41CrossRefGoogle Scholar
  23. 23.
    Orr RJ, Abowd GD (2000) The smartfloor: a mechanism for natural user identification and tracking. Conference on Human Factors in Computing System, pp 275–276Google Scholar
  24. 24.
    Helal S, Mann W, El-Zabadani H, King J, Kaddoura Y, Jansen E (2005) The Gator Tech SmartHouse: a programmable pervasive space. Computer 38(3):50–60CrossRefGoogle Scholar
  25. 25.
    Poland MP, Gueldenring D, Nugent CD, Wang H, Chen L (2009) Spatiotemporal data acquisition modalities for smart home inhabitant movement behavioural analysis. Proceedings of the 7th International Conference on Smart homes and Health Telematics, pp 294–298Google Scholar
  26. 26.
    Poland MP, Nugent CD, Wang H, Chen L (2009) Smart homes: projects and issues. Int J Ambient Comput Intell 1(4):32–45Google Scholar
  27. 27.
    Ivanov B, Ruser H, Kellner M (2002) Presence detection and person identification in Smart homes. International Conference Sensors and System, pp 80–85Google Scholar
  28. 28.
    Nam Ha K, Chang Lee K, Lee S (2006) Development of PIR sensor based indoor location detection system for smart home. International Joint Conference of SICE-ICASE, pp 2162–2167Google Scholar
  29. 29.
    West GAW, Newman C, Greenhill S (2005) Using a camera to implement virtual sensors in a smarthouse. Proceedings of the 3rd International Conference on Smart homes and Telehealth, From Smart homes to SmartCare, pp 83–90Google Scholar
  30. 30.
    Tapia EM (2003) Activity recognition in the home setting using simple and ubiquitous sensors. Master’s Thesis, MITGoogle Scholar
  31. 31.
    Ivanov B, Ruser H, Kellner M (2002) Presence detection and person identification in Smart homes. International Conference on Sensors and Systems, pp 80–85Google Scholar
  32. 32.
    Kaddourah Y, King J, Helal A (2005) Cost-precision tradeoffs in unencumbered floor-based indoor location tracking. Proceedings of the 3rd International Conference on Smart homes and Health TelematicGoogle Scholar
  33. 33.
    Glascock AP, Kutzik DM (2004) Moving telematics from the laboratory to a truly enabling technology within the community. Proceedings of the 2nd International Conference on Smart homes and Health Telematics, pp 145–153Google Scholar
  34. 34.
    Piccardi M (2004) Background subtraction techniques: a review. IEEE Int Conf Syst Man Cyber 4:3099–3104Google Scholar
  35. 35.
    McKenna SJ, Jabri S, Duric Z, Rosenfeld A, Wechsler H (2000) Tracking groups of people. Comput Vis Image Underst 80:45–56Google Scholar
  36. 36.
    Stauffer C, Grimson WEL (2000) Learning patterns of activity using real-time tracking. IEEE transactions of pattern analysis and machine intelligence Vol. 22, No. 8Google Scholar
  37. 37.
    Haritaoglu I, Harwood D, Davis LS (1998) W4: Who? When? Where? What? A real time system for detecting and tracking people. Proceedings of the third international conference on Automatic Face and Gesture RecognitionGoogle Scholar
  38. 38.
    Ivanov Y, Bobick A, Liu J (1997) Fast lighting independent background subtraction, technical report no. 437. MIT Media LaboratoryGoogle Scholar
  39. 39.
    Oliver N, Rosario B, Pentland A (1999) A bayesian computer vision system for modeling human interactions. Proccedings of the International Conference on Vision SystemsGoogle Scholar
  40. 40.
    Friedman N, Russell S (1997) Image segmentation in video sequences: a probabilistic approach. Proceedings of the 13th International Conference on Uncertainty in Artificial IntelligenceGoogle Scholar
  41. 41.
    Elgammal A, Harwood D, Davis LS (1999) Non-parametric model for background subtraction. Proceedings of the IEEE International Conference on Computer VisionGoogle Scholar
  42. 42.
    Haritaoglu I, Harwood D, Davis LS (2000) W4: real-time surveillance of people and their activities. IEEE Trans Pattern Analys Mach Intell 22(8):809–830CrossRefGoogle Scholar
  43. 43.
    Cucchiara R, Grana C, Piccardi M, Prati A (2003) Detecting moving objects, ghosts, and shadows in video streams. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 25, No.10Google Scholar
  44. 44.
    Greenhill S, Venkatesh S, West G (2004) Adaptive model for foreground extraction in adverse lighting conditions. 8th Pacific Rim International Conference on Artificial Intelligence, Vol. 3157Google Scholar
  45. 45.
    Campbell RJ, Flynn PJ (2001) A survey of free-form object representation and recognition techniques. Comput Vis Image Underst 81(2):166–210MATHCrossRefGoogle Scholar
  46. 46.
    Perrott CG, Hamey LGC (2000) Object recognition, a survey of the literature. Technical report, Macquarie University, NSW AustraliaGoogle Scholar
  47. 47.
    Peursum P, West G, Venkatesh S (2005) Combining image regions and human activity for indirect object recognition in indoor wide-angle views. Tenth IEEE Int Conf Comput Vision 1:82–89CrossRefGoogle Scholar
  48. 48.
    Uhrikova Z, Nugent CD, Hlavac V (2008) The use of computer vision techniques to augment home based sensorised environments. 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp 2550–2553Google Scholar
  49. 49.
    Nugent CD, Mulvenna MD, Hong X (2009) Experiences in the development of a smartlab, international journal of biomedical engineering and technology. Int J Biomed Eng Technol 2(4):319–331CrossRefGoogle Scholar
  50. 50.
    Nugent CD, Davies RJ, Hallberg J, Donnelly M, Synnes K, Poland M, Wallace JG, Finlay DD, Mulvenna MD, Craig D (2007) HomeCI—A visual editor for healthcare professionals in the design of home based care. Proceedings of 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp 2787–2790Google Scholar
  51. 51.
    Peursum P, Venkatesh S, West G (2006) Observation-switching linear dynamic systems for tracking humans through unexpected partial occlusions by scene objects. 18th International Conference on Pattern Recognition 4:929–934CrossRefGoogle Scholar
  52. 52.
    Better Health Channel. Dementia—how carers can help with dressing., (Jul 2009).
  53. 53.
    Smartlab: TRIP: Target Recognition using Image Processing., (Jul 2009).
  54. 54.
    de Ipina DL (2000) Building components for a distributed sentient framework with python and CORBA. Proceedings of the 8th International Python ConferenceGoogle Scholar

Copyright information

© Institut Télécom and Springer-Verlag 2010

Authors and Affiliations

  • Michael P. Poland
    • 1
  • Chris D. Nugent
    • 1
  • Hui Wang
    • 1
  • Liming Chen
    • 1
  1. 1.Computer Science Research Institute and School of Computing and Mathematics, Faculty of Computing and EngineeringUniversity of UlsterCounty AntrimUK

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