Student Helper, Master Thesis
The use of non-verbal vocal input (NVVI) as a hand-free trigger approach has proven to be valuable in previous work [Zielasko2015]. Nevertheless, BlowClick's original detection method is vulnerable to false positives and, thus, is limited in its potential use, e.g., together with acoustic feedback for the trigger. Therefore, we extend the existing approach by adding common machine learning methods. We found that a support vector machine (SVM) with Gaussian kernel performs best for detecting blowing with at least the same latency and more precision as before. Furthermore, we added acoustic feedback to the NVVI trigger, which increases the user's confidence. To evaluate the advanced trigger technique, we conducted a user study (n=33). The results confirm that it is a reliable trigger; alone and as part of a hands-free point-and-click interface.
We extended BlowClick, a NVVI metaphor for clicking, by adding machine learning methods to more reliably classify blowing events. We found a support vector machine with Gaussian kernel performing the best with at least the same latency and more precision than before. Furthermore, we added acoustic feedback to the NVVI trigger, which increases the user's confidence. With this extended technique we conducted a user study with 33 participants and could confirm that it is possible to use NVVI as a reliable trigger as part of a hands-free point-and-click interface.