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On the Design of Smart Homes: A Framework for Activity Recognition in Home Environment

  • Patient Facing Systems
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Abstract

A smart home is a home environment enriched with sensing, actuation, communication and computation capabilities which permits to adapt it to inhabitants preferences and requirements. Establishing a proper strategy of actuation on the home environment can require complex computational tasks on the sensed data. This is the case of activity recognition, which consists in retrieving high-level knowledge about what occurs in the home environment and about the behaviour of the inhabitants. The inherent complexity of this application domain asks for tools able to properly support the design and implementation phases. This paper proposes a framework for the design and implementation of smart home applications focused on activity recognition in home environments. The framework mainly relies on the Cloud-assisted Agent-based Smart home Environment (CASE) architecture offering basic abstraction entities which easily allow to design and implement Smart Home applications. CASE is a three layered architecture which exploits the distributed multi-agent paradigm and the cloud technology for offering analytics services. Details about how to implement activity recognition onto the CASE architecture are supplied focusing on the low-level technological issues as well as the algorithms and the methodologies useful for the activity recognition. The effectiveness of the framework is shown through a case study consisting of a daily activity recognition of a person in a home environment.

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Notes

  1. Waspmote website: http://www.libelium.com/products/waspmote/.

  2. Raspberry Pi website: https://www.raspberrypi.org/.

  3. Shimmer website: http://www.shimmersensing.com/.

References

  1. Miorandi, D., Sicari, S., De Pellegrini, F., Chlamtac, I., Internet of Things. Ad Hoc Netw. 10(7): 1497–1516, 2012.

    Article  Google Scholar 

  2. Fortino, G., Guerrieri, A., Russo, W., Agent-oriented smart objects development. In: Computer Supported Cooperative Work in Design (CSCWD), 2012 IEEE 16th International Conference on, pp. 907–912 (2012)

  3. Bierhoff, I., van Berlo, A., Abascal, J., Allen, B., Civit, A., Fellbaum, K., Kemppainen, E., Bitterman, N., Freitas, D., Kristiansson, K. Smart home environment. COST Brussels (2007)

  4. Alkar, A. Z., and Buhur, U., An Internet Based Wireless Home Automation System for Multifunctional Devices. IEEE Trans. on Consum. Electron. 51(4):1169–1174, 2005.

    Article  Google Scholar 

  5. Serra, J., Pubill, D., Antonopoulos, A., Verikoukis, C. Smart HVAC Control in IoT: Energy Consumption Minimization with User Comfort Constraints The Scientific World Journal (2014)

  6. Fortino, G., Guerrieri, A., O’Hare, G., Ruzzelli, A., A flexible building management framework based on wireless sensor and actuator networks. J. Netw. Comput. Appl. 35:1934–1952, 2012.

    Article  Google Scholar 

  7. Guerrieri, A., Fortino, G., Ruzzelli, A., O’hare, G. A WSN-based Building Management Framework to Support Energy-Saving Applications in Buildings. Hershey, PA USA: IGI Global (2011)

  8. Rashidi, P., and Cook, D. J., Com: A method for mining and monitoring human activity patterns in home-based health monitoring systems. ACM Trans. Intell. Syst. Technol. 4(4):64:1–64:20, 2013.

    Article  Google Scholar 

  9. Pavón-Pulido, N., López-Riquelme, J. A., Ferruz-Melero, J., Vega-rodríguez, M. A., Barrios-León, A. J., A service robot for monitoring elderly people in the context of ambient assisted living. J. Ambient Intell. Smart Environ. 6(6):595–621, 2014.

    Google Scholar 

  10. Dohr, A., Modre-Opsrian, R., Drobics, M., Hayn, D., Schreier, G., The internet of things for ambient assisted living. In: Information Technology: New Generations (ITNG), 2010 Seventh International Conference on, pp. 804–809 (2010)

  11. Richter, P., Toledano-Ayala, M., Soto-Zarazúa, G. M., Rivas-Araiza, E.A., A Survey of Hybridisation Methods of GNSS and Wireless LAN Based Positioning System. J. Ambient Intell. Smart Environ. 6(6):723–738, 2014.

    Google Scholar 

  12. Sang-hyun, L., Lee, J.g., Kyung-il, M., Smart Home Security System Using Multiple ANFIS. Int. J. Smart Home 7(3):121–132, 2013.

    Google Scholar 

  13. Fortino, G., Guerrieri, A., Lacopo, M., Lucia, M., Russo, W., An agent-based middleware for cooperating smart objects. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 387–398. Springer (2013)

  14. Jennings, N. R., On agent-based software engineering. Artif. Intell. 117(2):277–296, 2000.

    Article  Google Scholar 

  15. Cicirelli, F., and Nigro, L., Control centric framework for model continuity in time-dependent multi-agent systems. Concurrency and Computation: Practice and Experience. n/a–n/a cpe.3802 (2016)

  16. Giordano, A., Spezzano, G., Vinci, A., A smart platform for large-scale networked cyber-physical systems. Management of cyber physical objects in the future internet of things methods, architectures and applications. Springer (2016)

  17. Rashidi, P., and Mihailidis, A., A survey on Ambient-Assisted living tools for older adults. IEEE J. Biomedical Health Informat. 17(3):579–590, 2013.

    Article  Google Scholar 

  18. Savidis, A., and Stephanidis, C., Distributed interface bits: dynamic dialogue composition from ambient computing resources. Personal Ubiquitous Comput. 9(3):142–168, 2005.

    Article  Google Scholar 

  19. Bardram, J. E., Kjær, R. E., Pedersen, M., Context-Aware User Authentication - Supporting Proximity-Based Login in Pervasive Computing. In: Dey, A., Schmidt, A., McCarthy, J. (Eds.) UbiComp 2003: Ubiquitous Computing. Volume 2864 of Lecture Notes in Computer Science. Springer Berlin Heidelberg, pp. 107–123 (2003)

  20. Román, M., Hess, C., Cerqueira, R., Ranganathan, A., Campbell, R. H., Nahrstedt, K., A Middleware Infrastructure for Active Spaces. IEEE Pervasive Comput. 1(4):74–83, 2002.

    Article  Google Scholar 

  21. Cerqueira, R., Cassino, C., Ierusalimschy, R., Dynamic Component Gluing Across Different Componentware Systems. In: Proceedings of the International Symposium on Distributed Objects and Applications. DOA ’99, Washington, DC, USA, IEEE Computer Society, pp. 362– (1999)

  22. Evangelatos, O., Samarasinghe, K., Rolim, J., Syndesi: A Framework for Creating Personalized Smart Environments Using Wireless Sensor Networks. In: Proceedings of the 2013 IEEE international conference on distributed computing in sensor systems. DCOSS ’13, washington, DC, USA, pp. 325–330. IEEE Computer Society (2013)

  23. Hoque, E., and Stankovic, J. A., AALO: Activity recognition in smart homes using Active Learning in the presence of Overlapped activities. In: 6th International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth 2012, pp. 139–146 (2012)

  24. Aggarwal, C. C., Kong, X., Gu, Q., Han, J., Yu, P. S., Active learning: A survey. In: Data Classification: Algorithms and Applications, pp. 571–606 (2014)

  25. Cook, D. J., Krishnan, N. C., Rashidi, P., Activity Discovery and Activity Recognition: A New Partnership. IEEE Trans. on Systems, Man, and Cybernetics 43(3):820–828, 2013.

    Google Scholar 

  26. Suryadevara, N., Mukhopadhyay, S., Wang, R., Rayudu, R., Forecasting the behavior of an elderly using wireless sensors data in a smart home. Eng. Appl. Artif. Intell. 26(10):2641–2652, 2013.

    Article  Google Scholar 

  27. Chen, L., Nugent, C., Okeyo, G., An Ontology-Based hybrid approach to activity modeling for smart homes. IEEE Trans. on Human-Machine Syst. 44(1):92–105, 2014.

    Article  Google Scholar 

  28. Steele, R., Lo, A., Secombe, C., Wong, Y. K., Elderly persons perception and acceptance of using wireless sensor networks to assist healthcare. Int. J. Med. Inform. 78(12):788–801, 2009.

    Article  PubMed  Google Scholar 

  29. Giordano, A., Spezzano, G., Vinci, A., Rainbow: An Intelligent Platform for Large-Scale Networked Cyber-Physical Systems. In: Proc. of the 5th International Workshop on Networks of Cooperating Objects for Smart Cities (UBICITEC), pp. 70–85 (2014)

  30. Gomez, C., and Paradells, J., Wireless home automation networks: A survey of architectures and technologies. IEEE Commun. Mag. 48(6):92–101, 2010.

    Article  Google Scholar 

  31. Starsinic, M., System architecture challenges in the home m2m network. In: Applications and Technology Conference (LISAT), 2010 Long Island Systems, pp. 1–7 (2010)

  32. Ivanov, B., Ruser, H., Kellner, M., Presence detection and person identification in smart homes. In: Int. Conf. Sensors and Systems, St. Petersburg, pp. 12–14 (2002)

  33. Rawi, M. I. M., and Al-Anbuky, A., Wireless sensor networks and human comfort index. Pers. Ubiquit. Comput. 17(5):999–1011, 2013.

    Article  Google Scholar 

  34. Basu, D., Moretti, G., Gupta, G.S., Marsland, S., Wireless sensor network based smart home: Sensor selection, deployment and monitoring. In: Sensors applications symposium (SAS) 2013 IEEE, IEEE, pp. 49–54 (2013)

  35. Chunduru, V., and Subramanian, N., Effects of power lines on performance of home control system. In: Power electronics, drives and energy systems, 2006. PEDES’06. International Conference on, IEEE, pp. 1–6 (2006)

  36. Pulseworx: Upb technology description (2016)

  37. Darbee, P. Insteon: The details. Smarthome Technology (2005)

  38. Petrova, M., Riihijarvi, J., Mahonen, P., Labella, S., Performance study of ieee 802.15.4 using measurements and simulations. In: Wireless Communications and Networking Conference, 2006. WCNC 2006. IEEE, Vol. 1, pp. 487–492 (2006)

  39. Baronti, P., Pillai, P., Chook, V.W., Chessa, S., Gotta, A., Hu, Y. F., Wireless sensor networks: A survey on the state of the art and the 802.15.4 and zigbee standards. Comput. Commun. 30(7):1655–1695, 2007. Wired/Wireless Internet Communications.

    Article  Google Scholar 

  40. Shelby, Z., and Bormann, C. 6loWPAN: The wireless embedded Internet. Volume 43 John Wiley & Sons (2011)

  41. Alliance, Z. W., Z-wave: The new standard in wireless remote control (2009)

  42. Galeev, M. T., Catching the z-wave. Embed. Syst. Des. 19(10):28, 2006.

    Google Scholar 

  43. Zou, Z., Li, K. J., Li, R., Wu, S., Smart home system based on ipv6 and zigbee technology. Procedia Eng. 15:1529–1533 , 2011.

    Article  Google Scholar 

  44. Gill, K., Yang, S. H., Yao, F., Lu, X., A zigbee-based home automation system. IEEE Trans. Consum. Electron. 55(2):422–430, 2009.

    Article  Google Scholar 

  45. Alliance, Z. Zigbee home automation public application profile. IEEE J. Select Areas Commun (2007)

  46. Hazmi, A., Rinne, J., Valkama, M., Feasibility study of ieee 802.11 ah radio technology for iot and m2m use cases. In: Globecom workshops (GC wkshps) 2012 IEEE, IEEE, pp. 1687–1692 (2012)

  47. Sun, W., Choi, M., Choi, S., Ieee 802.11 ah: a long range 802.11 wlan at sub 1 ghz. J. ICT Stand. 1(1): 83–108, 2013.

    Google Scholar 

  48. Mackensen, E., Lai, M., Wendt, T. M., Bluetooth low energy (ble) based wireless sensors. In: Sensors, 2012 IEEE, IEEE, pp. 1–4 (2012)

  49. Specification of the Bluetooth System v4.0. www.Bluetooth.org (2016)

  50. Kwak, K. S., Ullah, S., Ullah, N., An overview of ieee 802.15. 6 standard. In: Applied Sciences in Biomedical and Communication Technologies (ISABEL), 2010 3rd International Symposium on, IEEE, pp. 1–6 (2010)

  51. [Online], D.I.I. Thisisant: the wireless sensor network solution (2016)

  52. 1902.1-2009 - IEEE Standard for Long Wavelength Wireless Network Protocol. http://standards.ieee.org/findstds/standard/1902.1-2009.html (2016)

  53. Wong, A., McDonagh, D., Omeni, O., Nunn, C., Hernandez-Silveira, M., Burdett, A., Sensium: An ultra-low-power wireless body sensor network platform: Design and application challenges. In: Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE, pp. 6576–6579 (2009)

  54. Bellifemine, F., Fortino, G., Giannantonio, R., Gravina, R., Guerrieri, A., Sgroi, M., SPINE: a domain-specific framework for rapid prototyping of WBSN applications. Softw. Pract. Experience 41(03):237–265, 2011.

    Article  Google Scholar 

  55. Chen, M., Gonzalez, S., Vasilakos, A., Cao, H., Leung, V. C. M., Body area networks: a survey. Mobile Netw. Appl. 16(2):171–193, 2010.

    Article  Google Scholar 

  56. Dementyev, A., Hodges, S., Taylor, S., Smith, J.: Power consumption analysis of bluetooth low energy, zigbee and ant sensor nodes in a cyclic sleep scenario. In: Wireless symposium (IWS), 2013 IEEE International, IEEE, pp. 1–4 (2013)

  57. Fortino, G., Giannantonio, R., Gravina, R., Kuryloski, P., Jafari, R., Enabling Effective Programming and Flexible Management of Efficient Body Sensor Network Applications. IEEE Trans. Human-Machine Syst. 43(1): 115–133, 2013.

    Article  Google Scholar 

  58. Hao, Y., and Foster, R., Wireless body sensor networks for health-monitoring applications. Physiol. Meas. 29(11):R27 , 2008.

    Article  PubMed  Google Scholar 

  59. Agrawal, R., and Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th international conference on very large data bases. VLDB ’94, san francisco, CA, USA, Morgan Kaufmann Publishers Inc, pp. 487–499 (1994)

  60. Hipp, J., Güntzer, U., Nakhaeizadeh, G., Algorithms for association rule mining — a general survey and comparison. SIGKDD Explor. Newsl. 2(1):58–64, 2000.

    Article  Google Scholar 

  61. Ester, M., Kriegel, H. P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Kdd, Vol. 96, pp. 226–231 (1996)

  62. Pudil, P., Novovičová, J., Kittler, J., Floating search methods in feature selection. Pattern Recogn. Lett. 15(11):1119–1125, 1994.

    Article  Google Scholar 

  63. Cover, T., and Hart, P., Nearest neighbor pattern classification. IEEE Trans. Inf. Theor. 13(1):21–27, 1967.

    Article  Google Scholar 

  64. Weyns, D., Omicini, A., Odell, J., Environment as a first class abstraction in multiagent systems. Auton. Agent. Multi-Agent Syst. 14(1):5–30, 2007.

    Article  Google Scholar 

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Acknowledgments

This work has been partially supported by “Smart platform for monitoring and management of in-home security and safety of people and structures” project that is part of the DOMUS District, funded by the Italian Government (PON03PE_00050_1).

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Authors and Affiliations

  1. CNR – National Research Council of Italy, Institute for High Performance Computing and Networking (ICAR), Via P. Bucci 7-11C, 87036, Rende, Italy

    Franco Cicirelli, Giancarlo Fortino, Andrea Giordano, Antonio Guerrieri, Giandomenico Spezzano & Andrea Vinci

  2. Dipartimento di Ingegneria Informatica, Modellistica, Elettronica e Sistemistica, Università della Calabria, 87036, Rende, Italy

    Giancarlo Fortino

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  1. Franco Cicirelli
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  2. Giancarlo Fortino
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  3. Andrea Giordano
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  5. Giandomenico Spezzano
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Correspondence to Andrea Giordano.

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Cicirelli, F., Fortino, G., Giordano, A. et al. On the Design of Smart Homes: A Framework for Activity Recognition in Home Environment. J Med Syst 40, 200 (2016). https://doi.org/10.1007/s10916-016-0549-7

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