Skip to main content
SpringerLink
Log in

Exploiting Home Infrastructure Data for the Good: Emergency Detection by Reusing Existing Data Sources

  • Conference paper
  • First Online:
Book cover Human Interaction, Emerging Technologies and Future Applications IV (IHIET-AI 2021)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1378))

Abstract

Monitoring people within their residence can enable elderly to live a self-determined life in their own home environment for a longer period of time.

Therefore, commonly activity profiles of the residents are created using various sensors in the house. Deviations from the typical activity profile may indicate an emergency situation. An alternative approach for monitoring people within their residence we investigates within our research is reusing existing data sources instead of installing additional sensors. In private households there are already numerous data sources such as smart meters, weather station, routers or voice assistants available. Intelligent algorithms can be used to evaluate this data and conclude on personal activities. This, in turn, allows the creation of activity profiles of the residents without using external sensor technology. This work outlines the research gap in reusing existing data sources for Human Activity Recognition (HAR) and emergency detection, which we intend to fill with our further work.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://www.home-assistant.io.

  2. 2.

    https://www.openhab.org.

References

  1. DIN SPEC 91280:2012-09, technikunterstütztes leben (AAL) - Klassifikation von Dienstleistungen für technikunterstütztes leben im bereich der Wohnung und des direkten Wohnumfelds. https://doi.org/10.31030/1909231

  2. Alpaydin, E.: Introduction to Machine Learning. MIT Press (2020)

    Google Scholar 

  3. Barger, T.S., Brown, D.E., Alwan, M.: Health-status monitoring through analysis of behavioral patterns. IEEE Trans. Syst. Man Cybern. - Part A: Syst. Hum. 35(1), 22–27 (2005). https://doi.org/10.1109/tsmca.2004.838474

  4. Braun, A., Kirchbuchner, F., Wichert, R.: Ambient assisted living. In: eHealth in Deutschland, pp. 203–222. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-49504-9_10

  5. Calvaresi, D., Cesarini, D., Sernani, P., Marinoni, M., Dragoni, A.F., Sturm, A.: Exploring the ambient assisted living domain: a systematic review. J. Ambient Intell. Hum. Comput. 8(2), 239–257 (2016). https://doi.org/10.1007/s12652-016-0374-3

  6. Cardinaux, F., Brownsell, S., Hawley, M., Bradley, D.: Modelling of behavioural patterns for abnormality detection in the context of lifestyle reassurance. In: Lecture Notes in Computer Science, pp. 243–251. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-85920-8_30

  7. Chen, L., Hoey, J., Nugent, C.D., Cook, D.J., Yu, Z.: Sensor-based activity recognition. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 42(6), 790–808 (2012). https://doi.org/10.1109/tsmcc.2012.2198883

  8. Clement, J., Ploennigs, J., Kabitzsch, K.: Smart meter: detect and individualize ADLs. In: Ambient Assisted Living, pp. 107–122. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-27491-6_8

  9. Clement, J., Ploennigs, J., Kabitzsch, K.: Detecting activities of daily living with smart meters. In: Ambient Assisted Living, pp. 143–160. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37988-8_10

  10. Dohr, A., Modre-Opsrian, R., Drobics, M., Hayn, D., Schreier, G.: The internet of things for ambient assisted living. In: 2010 Seventh International Conference on Information Technology: New Generations. IEEE (2010). https://doi.org/10.1109/itng.2010.104

  11. Elbert, D., Storf, H., Eisenbarth, M., Ünalan, Ö., Schmitt, M.: An approach for detecting deviations in daily routine for long-term behavior analysis. In: Proceedings of the 5th International ICST Conference on Pervasive Computing Technologies for Healthcare. IEEE (2011). https://doi.org/10.4108/icst.pervasivehealth.2011.246089

  12. Eldib, M., Deboeverie, F., Philips, W., Aghajan, H.: Behavior analysis for elderly care using a network of low-resolution visual sensors. J. Electron. Imaging 25(4), 041003 (2016). https://doi.org/10.1117/1.jei.25.4.041003

  13. Floeck, M., Litz, L.: Activity- and inactivity-based approaches to analyze an assisted living environment. In: 2008 Second International Conference on Emerging Security Information, Systems and Technologies. IEEE (2008). https://doi.org/10.1109/securware.2008.22

  14. Floeck, M., Litz, L.: Inactivity patterns and alarm generation in senior citizens’ houses. In: 2009 European Control Conference (ECC). IEEE (2009). https://doi.org/10.23919/ecc.2009.7074979

  15. Fouquet, Y., Franco, C., Demongeot, J., Villemazet, C., Vuillerme, N.: Telemonitoring of the elderly at home: real-time pervasive follow-up of daily routine, automatic detection of outliers and drifts. Smart Home Syst. 121–138 (2010). https://doi.org/10.5772/8414

  16. Germain, A., Kupfer, D.J.: Circadian rhythm disturbances in depression. Hum. Psychopharmacol.: Clin. Exp. 23(7), 571–585 (2008). https://doi.org/10.1002/hup.964

  17. Gu, Y., Ren, F., Li, J.: PAWS: passive human activity recognition based on WiFi ambient signals. IEEE Internet of Things J. 3(5), 796–805 (2016). https://doi.org/10.1109/jiot.2015.2511805

  18. Hassan, M.M., Uddin, M.Z., Mohamed, A., Almogren, A.: A robust human activity recognition system using smartphone sensors and deep learning. Future Gener. Comput. Syst. 81, 307–313 (2018). https://doi.org/10.1016/j.future.2017.11.029

  19. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015). https://doi.org/10.1038/nature14539

    Article  Google Scholar 

  20. Munstermann, M.: Technischunterstützte Pflege von morgen. Springer Fachmedien Wiesbaden (2015). https://doi.org/10.1007/978-3-658-09797-4

  21. Parra, L., Sendra, S., Jiménez, J.M., Lloret, J.: Multimedia sensors embedded in smartphones for ambient assisted living and e-health. Multimed. Tools Appl. 75(21), 13271–13297 (2015). https://doi.org/10.1007/s11042-015-2745-8

  22. Perkowitz, M., Philipose, M., Fishkin, K., Patterson, D.J.: Mining models of human activities from the web. In: Proceedings of the 13th Conference on World Wide Web - WWW 2004. ACM Press (2004). https://doi.org/10.1145/988672.988750

  23. Pu, Q., Gupta, S., Gollakota, S., Patel, S.: Whole-home gesture recognition using wireless signals. In: Proceedings of the 19th Annual International Conference on Mobile Computing & Networking - MobiCom 2013. ACM Press (2013). https://doi.org/10.1145/2500423.2500436

  24. Rashidi, P., Mihailidis, A.: A survey on ambient-assisted living tools for older adults. IEEE J. Biomed. Health Inform. 17(3), 579–590 (2013). https://doi.org/10.1109/jbhi.2012.2234129

  25. Reyes-Ortiz, J.L., Oneto, L., Samà, A., Parra, X., Anguita, D.: Transition-aware human activity recognition using smartphones. Neurocomputing 171, 754–767 (2016). https://doi.org/10.1016/j.neucom.2015.07.085

  26. Uddin, M., Khaksar, W., Torresen, J.: Ambient sensors for elderly care and independent living: a survey. Sensors 18(7), 2027 (2018). https://doi.org/10.3390/s18072027

  27. Virone, G., Noury, N., Demongeot, J.: A system for automatic measurement of circadian activity deviations in telemedicine. IEEE Trans. Biomed. Eng. 49(12), 1463–1469 (2002). https://doi.org/10.1109/tbme.2002.805452

  28. Virone, G., Alwan, M., Dalal, S., Kell, S.W., Turner, B., Stankovic, J.A., Felder, R.: Behavioral patterns of older adults in assisted living. IEEE Trans. Inf. Technol. Biomed. 12(3), 387–398 (2008). https://doi.org/10.1109/titb.2007.904157

  29. Wang, W., Liu, A.X., Shahzad, M., Ling, K., Lu, S.: Understanding and modeling of WiFi signal based human activity recognition. In: Proceedings of the 21st Annual International Conference on Mobile Computing and Networking - MobiCom 2015. ACM Press (2015). https://doi.org/10.1145/2789168.2790093

  30. Wilhelm, S.: Activity-monitoring in private households for emergency detection: a survey of common methods and existing disaggregable data sources (2021). https://doi.org/10.5220/0010180002630272

  31. Wilhelm, S., Jakob, D., Ahrens, D.: Human presence detection by monitoring the indoor CO2 concentration. In: Proceedings of the Conference on Mensch Und Computer, MuC 2020, pp. 199–203. Association for Computing Machinery, New York (2020). https://doi.org/10.1145/3404983.3409991

  32. Wilhelm, S., Jakob, D., Kasbauer, J., Ahrens, D.: GeLaP: German labeled dataset for power consumption (2021, to appear)

    Google Scholar 

  33. Xie, X., Xu, H., Yang, G., Mao, Z.H., Jia, W., Sun, M.: Reuse of WiFi in- formation for indoor monitoring of the elderly. In: 2016 IEEE 17th International Conference on Information Reuse and Integration (IRI). IEEE (2016). https://doi.org/10.1109/iri.2016.41

Download references

Acknowledgments

This work was funded by the Bavarian State Ministry of Family Affairs, Labor and Social Affairs.

Author information

Authors and Affiliations

  1. Deggendorf Institute of Technology, Technology Campus Grafenau, Hauptstrasse 3, Grafenau, Germany

    Sebastian Wilhelm

Authors
  1. Sebastian Wilhelm
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sebastian Wilhelm .

Editor information

Editors and Affiliations

  1. Institute for Advanced Systems Engineering, University of Central Florida, Orlando, FL, USA

    Tareq Ahram

  2. Campus du Moulin de la Housse, Université de Reims Champagne-Ardenne GRESPI, Reims Cedex 2, France

    Redha Taiar

  3. Directrice Hôpitaux Universitaires de Strasbourg, IFMK, Strasbourg, France

    Fabienne Groff

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wilhelm, S. (2021). Exploiting Home Infrastructure Data for the Good: Emergency Detection by Reusing Existing Data Sources. In: Ahram, T., Taiar, R., Groff, F. (eds) Human Interaction, Emerging Technologies and Future Applications IV. IHIET-AI 2021. Advances in Intelligent Systems and Computing, vol 1378. Springer, Cham. https://doi.org/10.1007/978-3-030-74009-2_7

Download citation

Publish with us

Policies and ethics

Search

Navigation