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Ontology driven human activity recognition in heterogeneous sensor measurements

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Abstract

In recent years, sensor-based activity recognition has integrated the field of sensor networks with data mining techniques to model a broad range of human activities and behaviour. Huge amounts of sensor data coming from smart gadgets such as smartphones and smartwatches opens up the possibility of probing and extracting knowledge from the data in the direction of monitoring and health care. Due to the immense popularity and extensive use of smart gadgets equipped with sensors, it is more realistic and effective to utilize them in the activity recognition systems. Sensor-based activity recognition is a challenging task due to the heterogeneous nature and noisy aspect of sensor data. This work presents an ontology-based knowledge model that conceptualizes the task of human activity recognition. The knowledge model is based on two newly developed ontologies: Sensor Measurements Ontology to model the heterogeneous sensor data and Activity Recognition Ontology to conceptualise the activity recognition process by capturing the relationships between the low level acts (simple activities) and high level (inferred activity). Besides activity recognition, the proposed model handles the issue of sensor heterogeneity and provides reusability, interoperability and exchange. The proposed model is validated with a real life dataset containing sensor observations of 60 users with more than 300,000 (three hundred thousand) samples to illustrate the functionalities in the task of human activity recognition.

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References

  • Ali S, Khusro S, Rauf A, Mahfooz S (2014) Sensors and mobile phones: evolution and state-of-the-art. Pak J Sci 66:385

    Google Scholar 

  • Baldauf M, Dustdar S, Rosenberg F (2007) A survey on context-aware systems. Int J Ad Hoc Ubiquitous Comput 2:263–277

    Article  Google Scholar 

  • Bettini C, Brdiczka O, Henricksen K, Indulska J, Nicklas D, Ranganathan A, Riboni D (2010) A survey of context modelling and reasoning techniques. Pervasive Mob Comput 6:161–180

    Article  Google Scholar 

  • Chen H, Finin T, Joshi A (2003) An ontology for context-aware pervasive computing environments. Knowl Eng Rev 18:197–207

    Article  Google Scholar 

  • Chen H, Finin T, Joshi A (2005) The SOUPA ontology for pervasive computing”. Ontologies for agents: theory and experiences. BirkHauser, Basel, pp 233–258

    Chapter  Google Scholar 

  • Compton M, Barnaghi P, Bermudez L, GarcíA-Castro R, Corcho O, Cox S, Huang V (2012) The SSN ontology of the W3C semantic sensor network incubator group. Web Semant 17:25–32

    Article  Google Scholar 

  • Eid M, Liscano R, El SA (2007) A universal ontology for sensor networks data. In Proceedings of the 2007 IEEE international conference on computational intelligence for measurement systems and applications, Ostuni, Italy, pp. 59–62

  • Evans D (2011) The internet of things how the next evolution of the internet is changing everything. Cisco IBSG (Internet Business Solutions Group). CISCO White Pap 1:1–11

    Google Scholar 

  • Gruber TR (1995) Toward principles for the design of ontologies used for knowledge sharing? Int J Hum Comput Stud 43:907–928

    Article  Google Scholar 

  • Gu T, Pung HK, Zhang DQ (2004) A middleware for building context-aware mobile services. In: Proceedings of IEEE vehicular technology conference (VTC), Milan, IEEE, pp. 2656–2660

  • Helaoui R, Riboni D, Stuckenschmidt H (2013) A probabilistic ontological framework for the recognition of multilevel human activities. In: Proceedings of the 2013 ACM international joint conference on pervasive and ubiquitous computing (UbiComp ’13), New York, NY, ACM, pp. 345–354

  • Henson CA, Pschorr JK, Sheth AP, Thirunarayam K (2009) SemSOS: semantic sensor observation service. In: Smari WW (eds) Proceedings of the international symposium on collaborative technologies and systems (CTS ’09), Baltimore, Maryland, IEEE, pp. 44–53

  • Khaleghi B, Khamis A, Karray FO, Razavi SN (2013) Multisensor data fusion: a review of the state-of-the-art. Inf Fusion 14:28–44

    Article  Google Scholar 

  • Khattak AM, Truc PTH, Hung LX, Vinh LT, Dang VH, Guan D, Pervez Z, Han M, Lee S, Lee YK (2011) Towards smart homes using low level sensory data. Sensors 11:11581–11604

    Article  Google Scholar 

  • Kim JH, Kwon H, Kim DH, Kwak HY, Lee SJ (2008) Building a service-oriented ontology for wireless sensor networks. In: Proceedings of the 7th IEEE/ACIS international conference on computer and information science (IEEE/ACIS ICIS ’08), Portland, Oregon, IEEE, pp. 649–654

  • Korpipää P, Mäntyjärvi J (2003) An ontology for mobile device sensor-based context awareness. In: Blackburn P, Ghidini C, Turner RM, Giunchiglia F (eds) Proceedings of the 4th international and interdisciplinary conference (CONTEXT ’03), Stanford, Springer, Berlin, Heidelberg, pp. 451–458

  • Neuhaus H, Compton M (2009) The semantic sensor network ontology. In: Proceedings of the AGILE 2009 pre-conference workshop challenges in geospatial data harmonisation, Hannover, Germany, pp 1–33

  • Noy NF, McGuinness DL (2001) Ontology development 101: a guide to creating your first ontology. Knowledge Systems Laboratory, Stanford University, California

    Google Scholar 

  • Noy NF, Sintek M, Decker S, Crubézy M, Fergerson RW, Musen MA (2001) Creating semantic web contents with protege-2000. IEEE Intell Syst 16:60–71

    Article  Google Scholar 

  • Perera C, Zaslavsky A, Christen P, Georgakopoulos D (2014) Context aware computing for the internet of things: a survey. IEEE Commun Surv Tutor 16:414–454

    Article  Google Scholar 

  • Pérez J, Arenas M, Gutierrez C (2009) Semantics and complexity of SPARQL. ACM Trans Database Syst (TODS) 34:16

    Article  Google Scholar 

  • Riboni D, Bettini C (2011) COSAR: hybrid reasoning for context-aware activity recognition. Pers Ubiquitous Comput 15:271–289

    Article  Google Scholar 

  • Rodriguez ND, Cuellar MP, Lilius J, Calvo-Flores MD (2014) A fuzzy ontology for semantic modelling and recognition of human behavior. Knowl Based Syst 66:46–60

    Article  Google Scholar 

  • Russell SJ, Norvig P (2010) Artificial intelligence: a modern approach, 3rd edn. Prentice Hall, Upper Saddle River

    MATH  Google Scholar 

  • Russomanno DJ, Kothari C, Thomas O (2005a) Sensor ontologies: from shallow to deep models. In: Proceedings of the 37th southeastern symposium on system theory (SST ’05), Tuskegee, pp. 107–112

  • Russomanno DJ, Kothari CR, Thomas OA (2005b) Building a sensor ontology: a practical approach leveraging ISO and OOGC models. In: Arabnia HR, Joshua R (eds) Proceedings of the international conference on artificial intelligence (ICAI ’05), Nevada, CSREA Press, pp. 637- 643

  • Schlenoff C, Hong T, Liu C, Eastman R, Foufou S (2013) A literature review of sensor ontologies for manufacturing applications. In: Proceedings of the 11th IEEE international symposium on robotic and sensors environments (ROSE ’13), Washington, pp. 96–101

  • Subercaze J, Maret P, Dang NM, Sasaki K (2007) Context aware applications using personal sensors. In: Proceedings of the 2nd international conference on body area networks (ICST ’07), Florence, Italy, pp. 1–5

  • Vaizman Y, Ellis K, Lanckriet G (2017) Recognizing detailed human context in the wild from smartphones and smartwatches. IEEE Pervasive Comput 16:62–74

    Article  Google Scholar 

  • Vaizman Y, Katherine E, Gert L, Nadir W (2018) Extrasensory app: data collection in-the-wild with rich user interface to self-report behavior. In: Proceedings of the 2018 CHI conference on human factors in computing systems, ACM, p. 554

  • Wang XH, Zhang DQ, Gu T, Pung HK (2004) Ontology based context modeling and reasoning using OWL. In: Proceedings of the second IEEE annual conference on pervasive computing and communications workshops, Orlando, Florida, pp. 18–22

  • Wang Y, Lin J, Annavaram M, Jacobson QA, Hong J, Krishnamachari B, Sadeh N (2009) A framework of energy efficient mobile sensing for automatic user state recognition. In: Proceedings of the 7th ACM international conference on mobile systems, applications, and services (MobiSys ’09), Poland, pp. 179–192

  • Wang J, Zhang Z, Li B, Lee SY, Sherratt RS (2014) An enhanced fall detection system for elderly person monitoring using consumer home networks. IEEE Trans Consum Electron 60:23–29

    Article  Google Scholar 

  • Wongpatikaseree K, Ikeda M, Buranarach M, Supnithi T, Lim AO, Tan Y (2012) Activity recognition using context-aware infrastructure ontology in smart home domain. In: 2012 Seventh international conference on knowledge, information and creativity support systems (KICSS), IEEE, pp. 50–57

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  1. Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, India

    Diksha Hooda & Rinkle Rani

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  1. Diksha Hooda
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  2. Rinkle Rani
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Correspondence to Diksha Hooda.

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Hooda, D., Rani, R. Ontology driven human activity recognition in heterogeneous sensor measurements. J Ambient Intell Human Comput 11, 5947–5960 (2020). https://doi.org/10.1007/s12652-020-01835-0

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