Abstract
This paper investigates the fusion of wearable and ambient sensors for recognizing activities of daily living in a smart home setting using ontology. The proposed approach exploits the advantages of both types of sensing to resolve uncertainties due to missing sensor data. The resulting system is able to infer activities which cannot be inferred with the single type of sensing only. The methodology of ontological modeling the wearable and ambient sensors and the fusion of contexts captured from the sensors, as well as corresponding activity is investigated and described. The proposed system is compared with a system that uses ambient sensors without wearable sensor on the internally collected and publicly available datasets. The results of the experiments show that the proposed system is more robust in handling uncertainties. It is also more capable of inferring additional information about activities, which is not possible with environment sensing only, with overall recognition accuracy of 91.5 and 90% on internal and public datasets, respectively.
Similar content being viewed by others
Notes
Hermit OWL reasoner http://www.hermit-reasoner.com/.
The activity ontology is available at the address http://halim.readismed.com/download/activity-ontology.
References
Alirezaie M, Renoux J, Köckemann U, Kristoffersson A, Karlsson L, Blomqvist E, Tsiftes N, Voigt T, Loutfi A (2017) An ontology-based context-aware system for smart homes: e-care@home. Sensors 17:1586. https://doi.org/10.3390/s17071586
Atallah L, Lo B, Ali R, King R, Yang G-Z (2009) Real-time activity classification using ambient and wearable sensors. IEEE Trans Inf Technol Biomed 13:1031–1039. https://doi.org/10.1109/TITB.2009.2028575
Baader F, Calvanese D, McGuinness DL, Nardi D, Patel-Schneider PF (eds) (2003) The description logic handbook: theory, implementation, and applications. Cambridge University Press, New York
BakhshandehAbkenar A, Loke SW (2014) MyActivity: cloud-hosted continuous activity recognition using ontology-based stream reasoning. In: 2014 2nd IEEE international conference on mobile cloud computing, services, and engineering (MobileCloud), pp 117–126
Chen L, Hoey J, Nugent CD, Cook DJ, Yu Z (2012) Sensor-based activity recognition. IEEE Trans Syst Man Cybern Part C Appl Rev 42:790–808. https://doi.org/10.1109/TSMCC.2012.2198883
De D, Bharti P, Das SK, Chellappan S (2015) Multimodal wearable sensing for fine-grained activity recognition in healthcare. IEEE Internet Comput 19:26–35. https://doi.org/10.1109/MIC.2015.72
Do TM, Loke SW, Liu F (2013) HealthyLife: an activity recognition system with smartphone using logic-based stream reasoning. In: Zheng K, Li M, Jiang H (eds) Mobile and ubiquitous systems: computing, networking, and services. 9th international conference, MobiQuitous 2012, Beijing, China, December 12–14, 2012. Revised selected papers. Springer, Berlin, Heidelberg, pp 188–199
Förster K, Biasiucci A, Chavarriaga R, del Millán JR, Roggen D, Tröster G (2010) On the use of brain decoded signals for online user adaptive gesture recognition systems. In: Floréen P, Krüger A, Spasojevic M (eds) Pervasive computing. Springer, Berlin, pp 427–444
Gavrila DM (1999) The visual analysis of human movement: a survey. Comput Vis Image Underst 73:82–98. https://doi.org/10.1006/cviu.1998.0716
Ge Y, Xu B (2014) Elderly personal intention recognition by activity and context recognition in smart home. In: 2014 9th international conference on computer science education (ICCSE), pp 347–350
Gravina R, Alinia P, Ghasemzadeh H, Fortino G (2017) Multi-sensor fusion in body sensor networks: state-of-the-art and research challenges. Inf Fusion 35:68–80. https://doi.org/10.1016/j.inffus.2016.09.005
Gu T, Wang L, Wu Z, Tao X, Lu J (2011) A pattern mining approach to sensor-based human activity recognition. IEEE Trans Knowl Data Eng 23:1359–1372. https://doi.org/10.1109/TKDE.2010.184
Hodges MR, Pollack ME (2007) An ‘object-use fingerprint’: the use of electronic sensors for human identification. In: Krumm J, Abowd GD, Seneviratne A, Strang T (eds) UbiComp 2007: ubiquitous computing. Springer, Berlin, pp 289–303
Jia R, Liu B (2013) Human daily activity recognition by fusing accelerometer and multi-lead ECG data. In: 2013 IEEE international conference on signal processing, communication and computing (ICSPCC), pp 1–4
Khattak AM, Truc PTH, Hung LX, Vinh LT, Dang V-H, Guan D, Pervez Z, Han M, Lee S, Lee Y-K (2011) Towards smart homes using low level sensory data. Sensors 11:11581–11604. https://doi.org/10.3390/s111211581
Laukkanen P, Leskinen E, Kauppinen M, Sakari-Rantala R, Heikkinen E (2000) Health and functional capacity as predictors of community dwelling among elderly people. J Clin Epidemiol 53:257–265. https://doi.org/10.1016/S0895-4356(99)00178-X
Lee ML, Dey AK (2014) Sensor-based observations of daily living for aging in place. Pers Ubiquitous Comput 19:27–43. https://doi.org/10.1007/s00779-014-0810-3
McIlwraith D, Pansiot J, Yang G-Z (2010) Wearable and ambient sensor fusion for the characterisation of human motion. In: 2010 IEEE/RSJ international conference on intelligent robots and systems (IROS), pp 5505–5510
Ni Q, García Hernando AB, de la Cruz IP (2015) The elderly’s independent living in smart homes: a characterization of activities and sensing infrastructure survey to facilitate services development. Sensors 15:11312–11362. https://doi.org/10.3390/s150511312
Noor MHM, Salcic Z, Wang KI-K (2017) Adaptive sliding window segmentation for physical activity recognition using a single tri-axial accelerometer. Pervasive Mob Comput 38:41–59. https://doi.org/10.1016/j.pmcj.2016.09.009
Pansiot J, Stoyanov D, McIlwraith D, Lo BPL, Yang GZ (2007) Ambient and wearable sensor fusion for activity recognition in healthcare monitoring systems. In: Leonhardt S, Falck D-IT, Mähönen PDP (eds) 4th international workshop on wearable and implantable body sensor networks (BSN 2007). Springer, Berlin, pp 208–212
Peeters PHF (2000) Design criteria for an automatic safety-alarm system for elderly. Technol Health Care 8:81–91
Philipose M, Fishkin KP, Perkowitz M, Patterson DJ, Fox D, Kautz H, Hahnel D (2004) Inferring activities from interactions with objects. IEEE Pervasive Comput 3:50–57. https://doi.org/10.1109/MPRV.2004.7
Riboni D, Bettini C (2011) COSAR: hybrid reasoning for context-aware activity recognition. Pers Ubiquitous Comput 15:271–289. https://doi.org/10.1007/s00779-010-0331-7
Rodríguez ND, Cuéllar MP, Lilius J, Calvo-Flores MD (2014) A survey on ontologies for human behavior recognition. ACM Comput Surv 46:43:1–43:33. https://doi.org/10.1145/2523819
Roggen D, Calatroni A, Rossi M, Holleczek T, Forster K, Troster G, Lukowicz P, Bannach D, Pirkl G, Ferscha A, Doppler J, Holzmann C, Kurz M, Holl G, Chavarriaga R, Sagha H, Bayati H, Creatura M, del JR Millan (2010) Collecting complex activity datasets in highly rich networked sensor environments. In: 2010 Seventh international conference on networked sensing systems (INSS), pp 233–240
Roy N, Misra A, Cook D (2015) Ambient and smartphone sensor assisted ADL recognition in multi-inhabitant smart environments. J Ambient Intell Humaniz Comput 7:1–19. https://doi.org/10.1007/s12652-015-0294-7
Sagha H, Digumarti ST, Millán J, del R, Chavarriaga, Calatroni R, Roggen A, Tröster D G (2011) Benchmarking classification techniques using the opportunity human activity dataset. In: 2011 IEEE international conference on systems, man, and cybernetics (SMC), pp 36–40
Scalmato A, Sgorbissa A, Zaccaria R (2013) Describing and recognizing patterns of events in smart environments with description logic. IEEE Trans Cybern 43:1882–1897. https://doi.org/10.1109/TSMCB.2012.2234739
Shoaib M, Bosch S, Incel OD, Scholten H, Havinga PJM (2014) Fusion of smartphone motion sensors for physical activity recognition. Sensors 14:10146–10176. https://doi.org/10.3390/s140610146
Steigmiller A, Glimm B, Liebig T (2014) Coupling tableau algorithms for expressive description logics with completion-based saturation procedures. In: Demri S, Kapur D, Weidenbach C (eds) Automated Reasoning. Proceedings of 7th international joint conference, IJCAR 2014, held as part of the Vienna summer of logic, VSL 2014, Vienna, Austria, July 19–22, 2014. Springer, Cham, pp 449–463
Sun H, Fan W, Shen W, Xiao T (2013) Ontology fusion in high-level-architecture-based collaborative engineering environments. IEEE Trans Syst Man Cybern Syst 43:2–13. https://doi.org/10.1109/TSMCA.2012.2190138
Sung M, DeVaul R, Jimenez S, Gips J, Pentland A (2004) Shiver motion and core body temperature classification for wearable soldier health monitoring systems. In: Eighth international symposium on wearable computers, 2004. ISWC 2004, pp 192–193
Turaga P, Chellappa R, Subrahmanian VS, Udrea O (2008) Machine recognition of human activities: a survey. IEEE Trans Circuits Syst Video Technol 18:1473–1488. https://doi.org/10.1109/TCSVT.2008.2005594
Valle ED, Ceri S, Harmelen FV, Fensel D (2009) It’s a streaming world! Reasoning upon rapidly changing information. IEEE Intell Syst 24:83–89. https://doi.org/10.1109/MIS.2009.125
Villalonga C, Pomares H, Rojas I, Banos O (2017) MIMU-wear: ontology-based sensor selection for real-world wearable activity recognition. Neurocomputing 250:76–100. https://doi.org/10.1016/j.neucom.2016.09.125
Wang C, Cao L, Chi CH (2015) Formalization and verification of group behavior interactions. IEEE Trans Syst Man Cybern Syst 45:1109–1124. https://doi.org/10.1109/TSMC.2015.2399862
Wannenburg J, Malekian R (2016) Physical activity recognition from smartphone accelerometer data for user context awareness sensing. IEEE Trans Syst Man Cybern Syst PP:1–8. https://doi.org/10.1109/TSMC.2016.2562509
WHO (2014) WHO, facts about ageing. In: WHO. http://www.who.int/ageing/about/facts/en/. Accessed 18 Sept 2017
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), pp 50–57
Wu K, Haarslev V (2014) Parallel OWL reasoning: merge classification. In: Kim W, Ding Y, Kim H-G (eds) Semantic technology. 3rd joint international conference, JIST 2013, Seoul, South Korea, November 28–30, 2013, Revised Selected Papers. Springer, Cham, pp 211–227
Wu J, Osuntogun A, Choudhury T, Philipose M, Rehg JM (2007) A scalable approach to activity recognition based on object use. In: 2007 IEEE 11th international conference on computer vision, pp 1–8
Yilmaz A, Javed O, Shah M (2006) Object tracking: a survey. ACM Comput Surv. https://doi.org/10.1145/1177352.1177355
Yoshihiro T, Masako K-P, Noriaki K, Jukai M, Miwa H, Yasuko K, Ota Jun (2013) Recognition of nursing activity with accelerometers and RFID. Kybernetes 42:1059–1071. https://doi.org/10.1108/K-02-2013-0023
Zgheib R, Nicola AD, Villani ML, Conchon E, Bastide R (2017) A flexible architecture for cognitive sensing of activities in ambient assisted living. In: 2017 IEEE 26th international conference on enabling technologies: infrastructure for collaborative enterprises (WETICE), pp 284–289
Zhou F, Jiao JR, Chen S, Zhang D (2011) A case-driven ambient intelligence system for elderly in-home assistance applications. IEEE Trans Syst Man Cybern Part C Appl Rev 41:179–189. https://doi.org/10.1109/TSMCC.2010.2052456
Acknowledgements
The authors would like to thank Embedded System Research Group for providing sensors and devices utilized in this study. The authors would also like to thank Nazrin Muhammad (UoA) and Akshat Bisht for assisting with this work.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Noor, M.H.M., Salcic, Z. & Wang, K.IK. Ontology-based sensor fusion activity recognition. J Ambient Intell Human Comput 11, 3073–3087 (2020). https://doi.org/10.1007/s12652-017-0668-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-017-0668-0