Haptic Exploration Patterns in Virtual Line-Graph Comprehension

  • Özge Alaçam
  • Cengiz Acartürk
  • Christopher Habel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9187)

Abstract

Multi-modal interfaces that provide haptic access to statistical line graphs combined with verbal assistance are proposed as an effective tool to fulfill the needs of visually impaired people. Graphs do not only present data, they also provide and elicit the extraction of second order entities (such as maxima or trends), which are closely linked to shape properties of the graphs. In an experimental study, we investigated collaborative joint activities between haptic explorers of graphs and verbal assistants who helped haptic explorers to conceptualize local and non-local second-order concepts. The assistants have not only to decide what to say but in particular when to say it. Based on the empirical data of this experiment, we describe in the present paper the design of a feature set for describing patterns of haptic exploration, which is able to characterize the need for verbal assistance during the course of haptic exploration. We employed a (supervised) classification algorithm, namely the J48 decision tree. The constructed features within the range from basic (low-level) user-action features to complex (high-level) conceptual were categorized into four feature sets. All feature set combinations achieved high accuracy level. The best results in terms of sensitivity and specificity were achieved by adding the low-level graphical features.

Keywords

A classifier for haptic exploration patterns Haptic graph comprehension Verbal assistance 

Notes

Acknowledgment

The research reported in this paper has been supported by DFG (German Science Foundation) in ITRG 1247 ‘Cross-modal Interaction in Natural and Artificial Cognitive Systems’, by Marie Curie Actions IRIS (ref. 610986, FP7-PEOPLE-2013-IAPP), and by METU Scientific Research Project scheme BAP–08-11-2012-121 ‘Investigation of Cognitive Processes in Multimodal Communication.’

References

  1. 1.
    Loomis, J.M., Klatzky, R.L., Lederman, S.J.: Similarity of tactual and visual picture recognition with limited field of view. Perception 20, 167–177 (1991)CrossRefGoogle Scholar
  2. 2.
    Yu, W., Brewster, S.A.: Evaluation of multimodal graphs for blind people. J. Univers. Access Inf. Soc. 2, 105–124 (2003)CrossRefGoogle Scholar
  3. 3.
    Alaçam, Ö., Habel, C., Acartürk, C.: Towards designing audio assistance for comprehending haptic graphs: a multimodal perspective. In: Stephanidis, C., Antona, M. (eds.) UAHCI 2013, Part I. LNCS, vol. 8009, pp. 409–418. Springer, Heidelberg (2013)Google Scholar
  4. 4.
    Acartürk, C., Alaçam, Ö., Habel, C.: Developing a verbal assistance system for line graph comprehension. In: Marcus, A. (ed.) DUXU 2014, Part II. LNCS, vol. 8518, pp. 373–382. Springer, Heidelberg (2014)Google Scholar
  5. 5.
    Alaçam, Ö., Acartürk, C., Habel, C.: Referring expressions in discourse about haptic line graphs . In: Rieser, V., Muller, P. (Eds.) Proceedings of the 18th Workshop on the Semantics and Pragmatics of Dialogue. SemDial 2014 – DialWatt, pp. 7–16 (2014)Google Scholar
  6. 6.
    Garrod, S., Pickering, M.J.: Why is conversation so easy? Trends Cogn. Sci. 8, 8–11 (2004)CrossRefGoogle Scholar
  7. 7.
    Kerzel, M., Alaçam, Ö., Habel, C., Acartürk, C.: Producing verbal descriptions for haptic line-graph explorations [Poster abstract]. In Rieser, V., Muller, P. (Eds.) Proceedings of the 18th Workshop on the Semantics and Pragmatics of Dialogue. SemDial 2014 – DialWatt, pp. 205–207 (2014)Google Scholar
  8. 8.
    Hoffman, D.D., Richards, W.A.: Parts of recognition. Cognition 8, 65–96 (1984)CrossRefGoogle Scholar
  9. 9.
    Eschenbach, C., Habel, C., Kulik, L., Leßmöllmann, A.: Shape nouns and shape concepts: a geometry for ‘corner’. In: Freksa, C., Habel, C., Wender, K.F. (eds.) Spatial Cognition 1998. LNCS (LNAI), vol. 1404, pp. 177–201. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  10. 10.
    Cohen, E.H., Singh, M.: Geometric determinants of shape segmentation: tests using segment identification. Vis. Res. 47(22), 2825–2840 (2007)CrossRefGoogle Scholar
  11. 11.
    Kosslyn, S.M.: Understanding charts and graphs. Appl. Cogn. Psychol. 3(3), 185–226 (1989)CrossRefGoogle Scholar
  12. 12.
    Pinker, S.: A theory of graph comprehension. In: Freedle, R., (eds.) Artificial Intelligence and the Future of Testing, pp. 73–126. Erlbaum, Hillsdale (1998)Google Scholar
  13. 13.
    PRBO. Waterbird Census at Bolinas Lagoon, CA, Marin County. Public report by Wetlands Ecology Division, Point Reyes Bird Observatory (PRBO) Conservation Science (2012). http://www.prbo.org/cms/366. Retrieved on 29 January 2012
  14. 14.
    Grunwald, M., Muniyandi, M., Kim, H., Kim, J., Krause, F., Mueller, S., Srinivasan, M.A.: Human haptic perception is interrupted by explorative stops of milliseconds. Front. Psychol. 5, 1–14 (2014)CrossRefGoogle Scholar
  15. 15.
    Weka 3: data mining software in java. http://www.cs.waikato.ac.nz/ml/weka

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Özge Alaçam
    • 1
  • Cengiz Acartürk
    • 2
  • Christopher Habel
    • 1
  1. 1.Department of InformaticsUniversity of HamburgHamburgGermany
  2. 2.Informatics InstituteMiddle East Technical UniversityAnkaraTurkey

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