Table of Contents Author Guidelines Submit a Manuscript
Mobile Information Systems
Volume 2017, Article ID 7261958, 17 pages
https://doi.org/10.1155/2017/7261958
Research Article

DAFIESKU: A System for Acquiring Mobile Physiological Data

1Egokituz Laboratory, Informatika Fakultatea (UPV/EHU), Manuel Lardizabal Pasealekua 1, Donostia, 20018 Gipuzkoa, Spain
2WimbiTek, Tolosa Hiribidea 72, Donostia, 20018 Gipuzkoa, Spain

Correspondence should be addressed to Nestor Garay-Vitoria; sue.uhe@yarag.rotsen

Received 23 June 2017; Revised 7 September 2017; Accepted 28 September 2017; Published 1 November 2017

Academic Editor: Pino Caballero-Gil

Copyright © 2017 Maider Simón et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. The Encyclopedia of Human-Computer Interaction, 2nd Ed., https://www.interaction-design.org/literature/book/the-encyclopedia-of-human-computer-interaction-2nd-ed.
  2. J. A. Jacko, Human Computer Interaction Handbook: Fundamentals, Evolving Technologies, and Emerging Applications, CRC Press, 3rd edition, 2012.
  3. K. Blashkin and P. Isaias, Emerging Research and Trends in Interactivity and the Human-Computer Interface, IGI Global, 2014.
  4. J. Holland, Wearable Technology and Mobile Innovations for Next-Generation Education, IGI Global, 2016.
  5. UCI Machine Learning Repository, http://archive.ics.uci.edu/ml/.
  6. A. L. Goldberger, L. A. Amaral, L. Glass et al., “PhysioBank, physioToolkit, and physioNet: components of a new research resource for complex physiologic signals,” Circulation, vol. 101, no. 23, pp. E215–E220, 2000. View at Publisher · View at Google Scholar · View at Scopus
  7. H. Blockeel and J. Vanschoren, Experiment databases: towards an improved experimental methodology in machine learning, Knowledge Discovery in Databases: PKDD 2007, Springer, Berlin Heidelberg, pp. 6-17, 2007.
  8. D. J. Albers, N. Elhadad, E. Tabak, A. Perotte, and G. Hripcsak, “Dynamical phenotyping: Using temporal analysis of clinically collected physiologic data to stratify populations,” PLoS ONE, vol. 9, no. 6, Article ID e96443, 2014. View at Publisher · View at Google Scholar · View at Scopus
  9. X. Valencia, A Web Transcoding Framework Based on User Behaviour Evaluation [Ph.D. thesis], University of the Basque Country (UPV/EHU, Faculty of Informatics), 2017.
  10. M. Vigo and S. Harper, “Real-time detection of navigation problems on the World ‘Wild’ Web,” International Journal of Human-Computer Studies, vol. 101, pp. 1–9, 2017. View at Publisher · View at Google Scholar · View at Scopus
  11. UCAmI 2016 Conference Web site: http://mami.uclm.es/ucami-2016/.
  12. E. Sarasua, M. Simón, B. Gamecho, E. Larraza-Mendiluze, and N. Garay-Vitoria, “Physiological data acquisition system based on mobile computing,” in Ubiquitous Computing and Ambient Intelligence, C. García, P. Caballero-Gil, M. Burmester, and A. Quesada-Arencibia, Eds., vol. 10070 of LNCS, pp. 46–51, 2016. View at Publisher · View at Google Scholar · View at Scopus
  13. “IDEO: Design Kit: The Field Guide to Human-Centered Design,” 2015, https://www.ideo.com/work/human-centered-design-toolkit/.
  14. F. Paternò, “Tools for remote web usability evaluation,” HCI International, pp. 828-832, 2003.
  15. F. Christos, K. Christos, P. Eleftherios, T. Nikolaos, and A. Nikolaos, “Remote usability evaluation methods and tools: A survey,” in Proceedings of the 11th Panhellenic Conference in Informatics (PCI, pp. 151–162, 2007.
  16. X. Valencia, J. E. Pérez, U. Muñoz, M. Arrue, and J. Abascal, “Assisted interaction data analysis of web-based user studies,” in Human-Computer Interaction - INTERACT, J. Abascal, S. Diniz Junqueira Barbosa, M. Fetter, T. Gross, P. Palanque, and M. Winkler, Eds., vol. 9296 of LNCS, pp. 1–19, 2015. View at Publisher · View at Google Scholar · View at Scopus
  17. N. Aharony, W. Pan, C. Ip, I. Khayal, and A. Pentland, “Social fMRI: investigating and shaping social mechanisms in the real world,” Pervasive and Mobile Computing, vol. 7, no. 6, pp. 643–659, 2011. View at Publisher · View at Google Scholar · View at Scopus
  18. Funf in a box: http://inabox.funf.org/.
  19. D. Ferreira, Aware: A Mobile Context Instrumentation Middleware to Collaboratively Understand Human Behavior [Ph.D. thesis], University of Oulu, Faculty of Technology, 2013.
  20. H. P. Da Silva, A. Lourenço, A. Fred, and R. Martins, “BIT: biosignal igniter toolkit,” Computer Methods and Programs in Biomedicine, vol. 115, no. 1, pp. 20–32, 2014. View at Publisher · View at Google Scholar · View at Scopus
  21. BITalino: http://www.bitalino.com/.
  22. O. Banos, C. Villalonga, M. Damas, P. Gloesekoetter, H. Pomares, and I. Rojas, “PhysioDroid: combining wearable health sensors and mobile devices for a ubiquitous, continuous, and personal monitoring,” The Scientific World Journal, vol. 2014, Article ID 490824, 2014. View at Publisher · View at Google Scholar · View at Scopus
  23. J. A. Burke, D. Estrin, M. Hansen et al., “Participatory sensing. world sensor web workshop,” ACM Sensys, 2006. View at Google Scholar
  24. B. Guo, Z. Yu, X. Zhou, and D. Zhang, “From participatory sensing to Mobile Crowd Sensing,” in Proceedings of the 2014 IEEE International Conference on Pervasive Computing and Communication Workshops, PERCOM WORKSHOPS 2014, pp. 593–598, March 2014. View at Publisher · View at Google Scholar · View at Scopus
  25. B. Resch, M. Sudmanns, G. Sagl, A. Summa, P. Zeile, and J. Exner, “Crowdsourcing physiological conditions and subjective emotions by coupling technical and human mobile sensors,” GI_Forum, vol. 1, pp. 514–524, 2015. View at Publisher · View at Google Scholar
  26. X. Hu, T. H. S. Chu, H. C. B. Chan, and V. C. M. Leung, “Vita: a crowdsensing-oriented mobile cyber-physical system,” IEEE Transactions on Emerging Topics in Computing, vol. 1, no. 1, pp. 148–165, 2013. View at Publisher · View at Google Scholar
  27. R. W. Picard, “Future affective technology for autism and emotion communication,” Philosophical Transactions of the Royal Society B: Biological Sciences, vol. 364, no. 1535, pp. 3575–3584, 2009. View at Publisher · View at Google Scholar · View at Scopus
  28. Biosignalsplux: http://biosignalsplux.com/index.php/en/.
  29. Shimmer: http://www.shimmersensing.com/.
  30. A. Garzo and N. Garay-Vitoria, “Ethical issues for user involvement in technological research projects: Directives and recommendations,” Contemporary Ethical Issues in Engineering, pp. 251–269, 2015. View at Publisher · View at Google Scholar · View at Scopus
  31. M. Folk, G. Heber, Q. Koziol, E. Pourmal, and D. Robinson, “An overview of the HDF5 technology suite and its applications,” in Proceedings of the EDBT/ICDT 2011 Workshop on Array Databases, (AD'11), pp. 36–47, March 2011. View at Publisher · View at Google Scholar · View at Scopus
  32. JSON: JavaScript Object Notation, http://json.org/.
  33. Android Studio: Official Android IDE, https://developer.android.com/studio/index.html.
  34. “Butter Knife: Field and method binding for Android views,” http://jakewharton.github.io/butterknife/.
  35. “Graph View: Open source graph plotting library for Android,” http://www.android-graphview.org.
  36. “Android: Bluetooth API,” https://developer.android.com/guide/topics/connectivity/bluetooth.html.
  37. “Android: HttpURLConnection,” https://developer.android.com/reference/java/net/HttpURLConnection.html.
  38. Retrofit: A type-safe HTTP client for Android and Java, http://square.github.io/retrofit/.
  39. J. Brooke, “SUS: a retrospective,” Journal of Usability Studies, vol. 8, no. 2, pp. 29–40, 2013. View at Google Scholar
  40. J. Nielsen, “How Many Test Users in a Usability Study?” 2012, https://www.nngroup.com/articles/how-many-test-users/.
  41. H. P. Da Silva, A. Fred, and R. Martins, “Biosignals for everyone,” IEEE Pervasive Computing, vol. 13, no. 4, pp. 64–71, 2014. View at Publisher · View at Google Scholar · View at Scopus