Abstract
In this work, a mobile learning tool for the Lorm-alphabet is developed. A person who is deaf-blind is lorming by finger spelling on another person’s palm and fingers. We aim to provide an easy and anywhere to use Lorm trainer for caregivers, companions, and the general public. A robust gesture recognition utilizing zoning techniques and matching of symbol sequences has been developed for touch sensitive mobile devices. Tests with three users of the target group were conducted and qualitative evaluation of three experts was obtained. Overall, our development got positive feedback and a broad demand for the application was communicated. It is promising not only to support students of Lorm in their training process, but to widen the application of Lorm, therefore, diminishing social isolation of deaf-blind.
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Notes
- 1.
An analysis of pixel density may lead to further improvements of classification accuracy.
- 2.
The app’s acronym works in English and German and means LEarning LORM.
- 3.
Letter frequencies of phrases designed for tests - and used in evaluation - correlate at \(r=0.96\) with that in German language.
- 4.
An Asus Transformer Pad TF300TG (NVIDIA Tegra 3, 1,3 GHz, 1 GB RAM, Android 4.2.1) with 10,1” screen with capacitive 10 finger multi-touch.
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Acknowledgments
The authors kindly thank all participants of our tests for their help, constructive input, and valuable feedback. We thank Michael Jobst for final modifications to the software for publishing it on the app store.
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Schmidt, M., Bank, C., Weber, G. (2016). Zoning-Based Gesture Recognition to Enable a Mobile Lorm Trainer. In: Miesenberger, K., Bühler, C., Penaz, P. (eds) Computers Helping People with Special Needs. ICCHP 2016. Lecture Notes in Computer Science(), vol 9759. Springer, Cham. https://doi.org/10.1007/978-3-319-41267-2_67
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DOI: https://doi.org/10.1007/978-3-319-41267-2_67
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