Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Annals of Telecommunications
Erschienen in: Annals of Telecommunications 9-10/2010

01.10.2010

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

verfasst von: Michael P. Poland, Chris D. Nugent, Hui Wang, Liming Chen

Erschienen in: Annals of Telecommunications | Ausgabe 9-10/2010

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

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.
Vorheriger Artikel User-driven context aware creation and execution of home care applications
Nächster Artikel Detection of aberrant behaviour in home environments from video sequence

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren
Literatur
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):673–682CrossRef
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 1586–1589
3.
Cook DJ, Das SK (2007) How smartare our environments? An updated look at the state of the art. Pervasive Mobile Comput 3(2):53–73CrossRef
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 236–24
5.
Brummel-Smith K, Dangiolo M (2009) Assistive technologies in the home. Clin Geriatr Med 25(1):61–77CrossRef
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 301–306
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):10–20CrossRef
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 147–158
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 531–536
10.
11.
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 1462–1465
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 4049–4053
14.
Hallberg J, Nilsson M, Synnes K (2003) Positioning with Bluetooth. Proceedings of the 10th International Conference on Telecommunications 2:954–958
15.
Pham VT, Qiu Q, Phyo AA, Biswas J (2006) Application of ultrasonic sensors in a smartenvironment. Pervasive Mobile Comput 3:180–207CrossRef
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):55–81CrossRef
17.
Huijsing JH, Riedijk FR, van der Horn G (1994) Developments in integrated smartsensors. Phys Sens Actua A 43(1–3):76–288
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 908–909
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 40:147–155CrossRef
20.
Yamazaki T (2006) Beyond the smart home. Proceedings of the International Conference on Hybrid Information Technology, pp 350–355
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 building
22.
Addlesee MD, Jones A, Livesey F, Samaria F (1997) The ORL active floor [sensor system]. IEEE Personal Commun 4(5):35–41CrossRef
23.
Orr RJ, Abowd GD (2000) The smartfloor: a mechanism for natural user identification and tracking. Conference on Human Factors in Computing System, pp 275–276
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):50–60CrossRef
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 294–298
26.
Poland MP, Nugent CD, Wang H, Chen L (2009) Smart homes: projects and issues. Int J Ambient Comput Intell 1(4):32–45
27.
Ivanov B, Ruser H, Kellner M (2002) Presence detection and person identification in Smart homes. International Conference Sensors and System, pp 80–85
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 2162–2167
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 83–90
30.
Tapia EM (2003) Activity recognition in the home setting using simple and ubiquitous sensors. Master’s Thesis, MIT
31.
Ivanov B, Ruser H, Kellner M (2002) Presence detection and person identification in Smart homes. International Conference on Sensors and Systems, pp 80–85
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 Telematic
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 145–153
34.
Piccardi M (2004) Background subtraction techniques: a review. IEEE Int Conf Syst Man Cyber 4:3099–3104
35.
McKenna SJ, Jabri S, Duric Z, Rosenfeld A, Wechsler H (2000) Tracking groups of people. Comput Vis Image Underst 80:45–56
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. 8
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 Recognition
38.
Ivanov Y, Bobick A, Liu J (1997) Fast lighting independent background subtraction, technical report no. 437. MIT Media Laboratory
39.
Oliver N, Rosario B, Pentland A (1999) A bayesian computer vision system for modeling human interactions. Proccedings of the International Conference on Vision Systems
40.
Friedman N, Russell S (1997) Image segmentation in video sequences: a probabilistic approach. Proceedings of the 13th International Conference on Uncertainty in Artificial Intelligence
41.
Elgammal A, Harwood D, Davis LS (1999) Non-parametric model for background subtraction. Proceedings of the IEEE International Conference on Computer Vision
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):809–830CrossRef
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.10
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. 3157
45.
Campbell RJ, Flynn PJ (2001) A survey of free-form object representation and recognition techniques. Comput Vis Image Underst 81(2):166–210MATHCrossRef
46.
Perrott CG, Hamey LGC (2000) Object recognition, a survey of the literature. Technical report, Macquarie University, NSW Australia
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 1:82–89CrossRef
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 2550–2553
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):319–331CrossRef
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 2787–2790
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 4:929–934CrossRef
52.
53.
54.
de Ipina DL (2000) Building components for a distributed sentient framework with python and CORBA. Proceedings of the 8th International Python Conference
Metadaten
Titel
Spatial-frequency data acquisition using rotational invariant pattern matching in smart environments
verfasst von
Michael P. Poland
Chris D. Nugent
Hui Wang
Liming Chen
Publikationsdatum
01.10.2010
Verlag
Springer-Verlag
Erschienen in
Annals of Telecommunications / Ausgabe 9-10/2010
Print ISSN: 0003-4347
Elektronische ISSN: 1958-9395
DOI
https://doi.org/10.1007/s12243-010-0172-4

Weitere Artikel der Ausgabe 9-10/2010

Annals of Telecommunications 9-10/2010 Zur Ausgabe

OriginalPaper

The potential of information and communication technologies to support ageing and independent living

Foreword

Ambient intelligence for ubiquitous health and wellbeing

OriginalPaper

A model and framework for human–environment interaction

OriginalPaper

Channel characterization of time reversal UWB communication systems

OriginalPaper

Blind signal-type classification using a novel robust feature subset selection method and neural network classifier

OriginalPaper

Intelligent agents in home healthcare

Premium Partner

    Bildnachweise