Tracking a Person’s Behaviour in a Smart House

  • Gavin Chand
  • Mustafa Ali
  • Bashar BarmadaEmail author
  • Veronica Liesaputra
  • Guillermo Ramirez-Prado
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11434)


This paper proposes to use machine learning techniques with ultrasonic sensors to predict the behavior and status of a person when they live solely inside their house. The proposed system is tested on a single room. A grid of ultrasonic sensors is placed in the ceiling of a room to monitor the position and the status of a person (standing, sitting, lying down). The sensors readings are wirelessly communicated through a microcontroller to a cloud. An intelligent system will read the sensors values from the cloud and analyses them using machine learning algorithms to predict the person behavior and status and decide whether it is a normal situation or abnormal. If an abnormal situation is concluded, then an alert with be risen on a dashboard, where a care giver can take an immediate action. The proposed system managed to give results with accuracy exceeding 90%. Results out of this project will help people with supported needed, for example elderly people, to live their life as independent as possible, without too much interference from the caregivers. This will also free the care givers and allows them to monitors more units at the same time.


Smart home People with supported needs Behavior tracking Ultrasonic sensors Machine learning 


  1. 1.
    Steinmetz, J., Xu, Q., Fishbach, A., Zhang, Y.: Being observed magnifies action. J. Pers. Soc. Psychol. 111(6), 852–865 (2016)CrossRefGoogle Scholar
  2. 2.
    Adair, J.G.: The hawthorne effect: a reconsideration of the methodological artifact. J. Appl. Psychol. 69(2), 334–345 (1984)CrossRefGoogle Scholar
  3. 3.
    Ansari, A.N., Sedky, M., Sharma, S.: An internet of things approach for motion detection using raspberry Pi. In: Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things, Harbin, China (2015)Google Scholar
  4. 4.
    Lee, C., Park, S., Jung, Y., Lee, Y., Mathews, M.: Internet of things: technology to enable the elderly. In: The Second IEEE International Conference on Robotic Computing (IRC), Laguna Hills, CA, US, pp. 358–362 (2018)Google Scholar
  5. 5.
    Demiris, G., et al.: Smart Home Sensors for the Elderly: A Model for Participatory Formative Evaluation, Alan Institute for Artificial Intelligence (2006)Google Scholar
  6. 6.
    Lee, T.S., Kwon, Y.M., Kim, H.G.: Smart location tracking system using FSR (Force Sensing Resistor). In: ICAT 2004 International Conference on Automotive Technology, Istanbul – Turkey (2004)Google Scholar
  7. 7.
    Wang, J., Zhang, Z., Li, B., Lee, S., Sherratt, R.S.: An enhanced fall detection system for elderly person monitoring using consumer home networks. IEEE Trans. Consum. Electron. 60(1), 23–29 (2014)CrossRefGoogle Scholar
  8. 8.
    Nadee, C., Chamnongthai, K.: Ultrasonic array sensors for monitoring of human fall detection. In: 12th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). Hua Hin, Thailand (2015)Google Scholar
  9. 9.
    Nishida, Y., Aizawa, H., Hori, T., Hoffman, N.H., Kanade, T., Kakikura, M.: 3D ultra-sonic tagging system for observing human activity. In: Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems. Las Vegas, NV, USA (2003)Google Scholar
  10. 10.
    Cheng, R., Heinzelman, W., Sturge-Apple, M., Ignjatovic, Z.: A motion-tracking ultrasonic sensor array for behavioral monitoring. IEEE Sens. J. 12(3), 707–712 (2012)CrossRefGoogle Scholar
  11. 11.
    Gottfried, B., Guesgen, H.W., Hübner, S.: Spatiotemporal reasoning for smart homes. In: Augusto, J.C., Nugent, C.D., (eds.) Designing Smart Homes, pp. 16–34. Springer, Heidelberg (2006). Scholar
  12. 12.
    Vikramaditya, J., Diane, C.: Anomaly detection using temporal data mining in a smart home environment. Methods Inf. Med. 47, 70–75 (2008). Scholar
  13. 13.
    Agrawal, R., Srikant, R.: Mining sequential patterns. In: Yu, P.S., Chen, A.L.P. Proceedings of the Eleventh International Conference on Data Engineering, Taipei, Taiwan, pp. 3–14. IEEE Computer Society, 6–10 March 1995Google Scholar
  14. 14.
    Allen, J.F., Ferguson, G.: Actions and events in interval temporal logic. J. Logic Comput. 4(5), 531–579 (1994)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Brand, M., Kettnaker, V.: Discovery and segmentation of activities in video. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 844–851 (2000)CrossRefGoogle Scholar
  16. 16.
    Nguyen, N.T., Bui, H.H., Venkatesh, S., West, G.: Recognising and monitoring high-level behaviours in complex spatial environments. In: IEEE Computer Society Conf. Computer Vision and Pattern Recognition, Wisconsin, USA (2003)Google Scholar
  17. 17.
    Lühr, S., Venkatesh, S., West, G., Bui, H.H.: Explicit state duration HMM for abnormality detection in sequences of human activity. In: Zhang, C., Guesgen, H.W., Yeap, W.-K. (eds.) PRICAI 2004. LNCS (LNAI), vol. 3157, pp. 983–984. Springer, Heidelberg (2004). Scholar
  18. 18.
    Liao, L., Fox, D., Kautz, H.: Location-based activity recognition using relational Markov networks. In: Proceedings of the International Joint Conference on Artificial Intelligence, pp. 773–778. Professional Book Center, Edinburgh, Scotland (2005)Google Scholar
  19. 19.
    Pham, V.T., Qiu, Q., Wai, A.A.P., Biswas, J.: Application of ultrasonic sensors in a smart environment. Pervasive Mob. Comput. 3(2), 180–207 (2007)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Gavin Chand
    • 1
  • Mustafa Ali
    • 1
  • Bashar Barmada
    • 1
    Email author
  • Veronica Liesaputra
    • 1
  • Guillermo Ramirez-Prado
    • 1
  1. 1.Computer Science DepartmentUnitec Institute of TechnologyAucklandNew Zealand

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