Abstract
We describe a systematic, quantitative study of the benefits using context recognition (specifically task tracking) for a wearable maintenance assistance system. A key objective of the work is to do the evaluation in an environment that is as close as possible to a real world setting. To this end, we use actual maintenance tasks on a complex piece of machinery at an industrial site. Subjects for our study are active Zeiss technicians who have an average of 10 years job experience.
In a within subject Wizard of Oz study with the interaction modality as the independent variable we compare three interaction modalities: (1) paper based documentation (2) speech controlled head mounted display (HMD) documentation, and context assisted HMD documentation. The study shows that the paper documentation is 50% and the speech only controlled system 30% slower then context. The statistical significance of 99% and 95% respectively (one sided ANOVA test). We also present results of two questionnaires (custom design and standard NASA TLX) that show a clear majority of subjects considered context to be beneficial in one way or the other. At the same time, the questionnaires reveal a certain level of uneasiness with the new modality.
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Kunze, K., Wagner, F., Kartal, E., Morales Kluge, E., Lukowicz, P. (2009). Does Context Matter ? - A Quantitative Evaluation in a Real World Maintenance Scenario. In: Tokuda, H., Beigl, M., Friday, A., Brush, A.J.B., Tobe, Y. (eds) Pervasive Computing. Pervasive 2009. Lecture Notes in Computer Science, vol 5538. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01516-8_25
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DOI: https://doi.org/10.1007/978-3-642-01516-8_25
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