Evaluation of localization and activity recognition systems for ambient assisted living: The experience of the 2012 EvAAL competition

Journal of Ambient Intelligence and Smart Environments

Volumn 5, Issue 1, 2013, Pages 119-132

  Álvarez García, Juan Antonio ^a   Barsocchi, Paolo ^b   Chessa, Stefano ^b,d   Salvi, Dario ^c  

^a UNIVERSITY OF SEVILLE   (Spain)

^b ISTI CNR   (Italy)

^c UNIVERSIDAD POLITÉCNICA DE MADRID   (Spain)

^d UNIVERSITY OF PISA   (Italy)

Author keywords

AAL; activity recognition; Indoor location

Indexed keywords

AAL; ACTIVITY RECOGNITION; AMBIENT ASSISTED LIVING; END USERS; INDEPENDENT LIVING; INDOOR LOCALIZATION; INDOOR LOCATIONS; INTERNATIONAL COMPETITIONS; QUALITY OF LIFE; USER ACCEPTANCE;

COMPETITION; INTEROPERABILITY;

QUALITY CONTROL;

EID: 84872339991     PISSN: 18761364     EISSN: None     Source Type: Journal    
DOI: 10.3233/AIS-120192     Document Type: Article

References (34)

1. J. Augusto, M. Huch, A. Kameas, J. Maitland, P. McCullagh, J. Roberts, A. Sixsmith, and R.Wichert, eds, Handbook of Ambient Assisted Living. Technology for Healthcare, Rehabilitation and Well-being, IOS Press, 2012.
2. Ambient Assisted Living roadmap, 2009. [Online]. Available: http://www.aaliance.eu/public/documents/aaliance-road map/.
3. AAL Forum-Ambient Assisted Living Forum. [Online]. Available: http://www.aalforum.eu/.
4. K. Connelly, K. Siek, I. Mulder, S. Neely, G. Stevenson, and C. Kray, Evaluating pervasive and ubiquitous systems, IEEE Pervasive Computing 7(3) (2008), 85-88.
5. EvAAL competition. [Online]. Available: http://evaal.aaloa.org.
6. AALOA association. [Online]. Available: http://aaloa.org.
7. Smarthouse Living Lab UPM. [Online]. Available: http://smarthouse.lst. tfo.upm.es/.
8. A. Martínez, M.A. Llorente, and J.P. Lázaro, Ciami living lab: an economically sustainable technological tool for open innovation, in: I ENoLL Living Lab Summer School, 2010. [Online]. Available: http://www. ictusagelab.org/ecoleLL/sites/default/files/Position-paper-Summer-School-TSB- 2010-v2. doc.
9. Trading Agent Competition, 2010, http://www.sics.se/tac/.
10. DARPA Grand Challenge, 2007, http://www.darpa.mil/grandchallenge/index. asp.
11. N.V. Shilov and K. Yi, Engaging students with theory through acm collegiate programming contest, Commun. ACM 45(9) (2002), 98-101. [Online]. Available: http://doi.acm.org/10.1145/567498.567506.
12. M. Mukund, International olympiad in informatics, in: Resonance, Vol. 7, 2002, pp. 102-107 doi:10.1007/BF02836177. [Online]. Available: http://dx.doi.org/10.1007/BF02836177
13. C. Wolf, J. Mille, L. Lombardi, O. Celiktutan, M. Jiu, M. Baccouche, E. Dellandrea, C.-E. Bichot, C. Garcia, and B. Sankur, The liris human activities dataset and the icpr 2012 human activities recognition and localization competition, LIRIS Laboratory, Tech. Rep. RR-LIRIS-2012-004, March 2012.
14. D. Roggen, Collecting complex activity data sets in highly rich networked environments, in: Seventh International Conference on Networked Sensing Systems (INSS10), 2010.
15. H. Sagha, S.T. Digumarti, J. del R. Millán, R. Chavarriaga, A. Calatroni, D. Roggen, and G. Tröster, Benchmarking classification techniques using the opportunity human activity dataset, in: Systems, Man, and Cybernetics (SMC), 2011 IEEE International Conference, Oct. 2011, pp. 36-40.
16. D. Roggen, Opportunity activity recognition, UCI Machine Learning Repository, June, 2012. [Online]. Available: http://archive.ics.uci.edu/ml/ datasets/OPPORTUNITY+ Activity+Recognition
17. N. Kawaguchi, N. Ogawa, Y. Iwasaki, K. Kaji, T. Terada, K. Murao, S. Inoue, Y. Kawahara, Y. Sumi, and N. Nishio, Hasc challenge: Gathering large scale human activity corpus for the real-world activity understandings, in: ACM AH 2011, 2011.
18. N. Ogawa, K. Kaji, and N. Kawaguchi, Effects of number of subjects on activity recognition-findings from hasc2010corpus, in: Proc. of 1st International Workshop on Frontiers in Activity Recognition using Pervasive Sensing (IWFAR2011), 2011, pp. 48-51.
19. L. Vitali, G. Hassan, K. Latifur R., and J. Roozbeh, A mining technique using n-grams and motion transcripts for body sensor network data repository, in: Wireless Health'10, 2010.
20. L. Vitali and J. Roozbeh, Power aware wireless data collection for bsn data repositories, in: International Conference on Body Sensor Networks, 2011.
21. H. Liu, H. Darabi, P. Banerjee, and J. Liu, Survey of wireless indoor positioning techniques and systems, IEEE Transactions on Systems, Man, and Cybernetics, Part C (2007), 1067-1080.
22. S. Hanke, UniversAAL-an open and consolidated AAL platform, in: Ambient Assisted Living. 4. AAL-Kongress 2011, 2011, pp. 127-140.
23. A.R. Jiménez Ruiz, F. Seco Granja, J.C. Prieto Honorato, and J.I. Guevara Rosas, Accurate pedestrian indoor navigation by tightly coupling foot-mounted imu and rfid measurements, IEEE T. Instrumentation and Measurement 61(1) (2012), 178-189.
24. S. Wagner, M. Handte, M.A.Z. Zamalloa, and P.J. Marrón, On optimal tag placement for indoor localization, in: PerCom, 2012, pp. 162-170.
25. M. Cypriani, P. Canalda, and F. Spies, Owlps: A self-calibarted fingerpringing-based Wi-Fi positioning system, in: Communications in Computer and Information Science, Vol. 309, 2012, pp. 36-51.
26. J. Wilson and N. Patwari, See-through walls: Motion tracking using variance-based radio tomography networks, IEEE Trans. Mob. Comput. 10(5) (2011), 612-621.
27. A.V. Medina, J.A. Gómez, O. Rivera, E. Dorronzoro, and M. Merino, Fingerprint indoor position system based on openmac, in: WINSYS, 2011, pp. 47-52.
28. S. Knauth, L. Kaufmann, R.k. Christian Jost, and A. Klapproth, The iloc ultrasound indoor localization system at the EvAAL 2011 competition, in: Communications in Computer and Information Science, Vol. 309, 2012, pp. 52-64.
29. N.M. Boers, D. Chodos, J. Huang, P. Gburzynski, I. Nikolaidis, and E. Stroulia, The smart condo: Visualizing independent living environments in a virtual world, in: PervasiveHealth, 2009, pp. 1-8.
30. N. Noury, A. Fleury, P. Rumeau, A.K. Bourke, G. ÓLaighin, V. Rialle, and J. Lundy, Fall detection-principles and methods, in: Proc. of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS 2007), 2007, pp. 1663-1666. [Online]. Available: http://subversion.assembla.com/svn/fallD/trunk/Stage-I/Literature/ Fall%20detection.pdf.
31. M. Nergui, Y. Yoshida, J. Gonzalez, Y. Koike, M. Sekine, and Y.Wenwei, Human motion tracking and measurement by a mobile robot, in: The International Conference on Intelligent Unmanned Systems 2011-ICIUS2011, 2011.
32. J.-H. Hong, J. Ramos, and A.K. Dey, Understanding physiological responses to stressors during physical activity, in: Proc. of the 2012 ACM Conference on Ubiquitous Computing, Ser. UbiComp '12. ACM, New York, NY, USA, 2012, pp. 270-279. [Online]. Available: http://doi.acm.org/10.1145/2370216.2370260.
33. N. Li, M. Crane, and H.J. Ruskin, Automatically detecting significant events on sensecam, Ercim News 87 (2011), 30-31.
34. L.G. Abril, F.J. Cuberos, F. Velasco, and J.A. Ortega, Ameva: An autonomous discretization algorithm. Expert Syst. Appl. 36(3) (2009), 5327-5332. [Online]. Available: http://dblp.uni-trier.de/db/journals/eswa/eswa36.html# AbrilCVO09.