Vibrotactile and friction texture displays are good options for artificially presenting roughness and frictional properties of textures, respectively. These two types of displays are compatible with touch panels and exhibit complementary characteristics. We combine vibrotactile and electrostatic friction texture displays to improve the quality of virtual textures, considering actual textured surfaces are composed of both properties. We investigate their composition ratios when displaying roughness textures. Grating roughness scales of six surface wavelength values are generated under 11 display conditions, and in nine of which, vibrotactile and friction stimuli are combined with different composition ratios. A forced-choice experiment regarding subjective realism indicates that vibrotactile stimulus with a slight variable-friction stimulus is effective for presenting quality textures.
The Supernumerary Hand Illusion in Augmented Reality
Cognitive load assessment is crucial for user studies and human-computer interaction designs. As a noninvasive and easy to-use category of measures, current photoplethysmogram(PPG)-based assessment methods rely on single or small-scale predefined features to recognize responses induced by people?s cognitive load, which are not stable in assessment accuracy. In this study, we propose a machine-learning method by using 46 kinds of PPG features together to improve the measurement accuracy for cognitive load. We test the method on 16 participants through the classical n-back tasks (0-back, 1-back, and 2-back). The accuracy of the machine-learning method in di?erentiating di?erent levels of cognitive loads induced by task difficulties can reach 100% in 0-back vs. 2-back tasks, which outperformed the traditional HRV-based and single-PPG-feature based methods by 12% - 55%. When using "leave-one-participant-out" subject-independent cross validation, 87.5% binary classification accuracy was reached which is at the state-of-the-art level. The proposed method can also support real-time cognitive load assessment by beat-to-beat classifications with better performance than the traditional single-feature-based real-time evaluation method.
Recent progress in digital photography and storage availability has significantly changed our approach to photo creation. While in the era of film cameras a careful forethought would usually precede the photography action, nowadays a large number of pictures can be taken immediately, placing a larger importance on actions taken after the photo capture. One of the consequences is the creation of numerous photos depicting the same moment in slightly different ways, which makes the process of organizing taken photos very laborious for a photographer. Photo collection organization is important both for album browsing and for simplifying the ultimate task of the best photos selection. In this work, we conduct a user study to explore how users tend to cluster similar photos in albums, to what extent different users agree in their clustering decisions, and to investigate how the clustering-defined photo context affects the photo selection process. We also propose an automatic hierarchical clustering solution, which is able to model users' decisions to a large extent. In addition, we demonstrate a possible application of our clustering solution that improves an automatic photo evaluation within photo albums by the clustering-based context adaptation.