Logitech (Switzerland)

Pressure sensitive inputs are an important feature for many XR design and creativity applications. This is typically accomplished using the trigger input on the default platform 6DoF controller. This paper reports the findings of a pilot user study that compares the performance of the default platform 6DoF controller trigger with an isometric force sensor integrated into the barrel of a prototype stylus device. The force sensor on the prototype stylus is activated with either the thumb or the index finger. The result of the 12-person pilot study shows that mean throughput of stylus index finger interactions is higher than the stylus thumb usage and default controller trigger usage; however throughout differences were not statistically significant. Subjectively, stylus index finger pressure control was the preferred interaction technique for most participants.

3D displays are becoming available to mainstream consumers, offering the ability to view and interact with 3D content without wearing AR/VR glasses. This paper reports the findings of a user study that compares the performance of a range of different input devices for 3D creation tasks on a 3D display. The first-time use performance of a mouse, a tracked 6DoF controller, and a pen on a graphics tablet (without display) are evaluated in an office setting for a series of 3D sketching, painting and docking tasks. The main finding is that the three input modalities provide similar levels of precision, but for 3D sketching and docking, the 6DoF controller has significantly shorter execution times, fewer interactions, and is more enjoyable than other devices, at the cost of increased fatigue. For surface painting and regular UI interaction, the mouse is more accurate, without any speed differences. Many participants reported the graphics tablet pen as being confusing or frustrating to use, frequently resulting in longer task times and more errors.

Speech disorders are common symptoms among Parkinson’s disease patients and affect the speech of patients in different aspects. Currently, there are few studies that consider the phonological dimension of Parkinson’s speech. In this work, we use a recently developed method to extract phonological features from speech signals. These features are based on the Sound Patterns of English phonological model. The extraction is performed using pre-trained Deep Neural Networks to infer the probabilities of phonological features from short-time acoustic features. An i-vector extractor is trained with the phonological features. The extracted i-vectors are used to classify patients and healthy speakers and assess their neurological state and dysarthria level. This approach could be helpful to assess new specific speech aspects such as the movement of different articulators involved in the speech production process.

We present an interface for navigating digital collections based on a one-dimensional analog control and a data visualization based on old analog radios. Our system takes advantage of inertial control to browse a large data collection in a compelling way, reducing the complexity of similar interfaces present in both desktop-based and portable media players. This vintage radio interface has been used to navigate a digital music collection. We have compared the proposed interface with the current most popular hardware, the iPod. The results of user tests with 24 participants are presented and discussed. The insights gained are encouraging enough to continue the development of one-dimensional analog controls for content discovery and retrieval.