The current research provides a perceptually transparent rendering algorithm for an ultrasound-based mid-air haptic device. In a series of experiments, we derive a systematic mapping function relating from the device command value to final user's perceived magnitude of a mid-air vibration feedback. The algorithm is designed for the ultrasonic mid-air haptic interface that is capable of displaying vibro-tactile feedback at a certain focal point in mid-air through ultrasound phased array technique. The perceived magnitude at the focal point is dependent on input parameters such as input command intensity, modulation frequency, and position of the focal point in the work-space. This algorithm automatically tunes these parameters to ensure that the desired perceived output at the user's hand is precisely controlled. Through a series of experiments, the effect of the aforementioned parameters on the physical output pressure are mapped, and the effect of this output pressure to the final perceived magnitude is formulated, resulting in the mapping from the different parameters to the perceived magnitude. Finally, the overall transparent rendering algorithm was evaluated, showing better perceptual quality than rendering with simple intensity command.

This paper presents a novel finger-worn actuator capable of generating three distinctive haptic effects: static pressure, high-frequency vibration, and an impact. Our new design makes this multi-mode feedback with a single actuator possible in a small form factor. A ring-shaped air bladder made with soft silicone is inflated or deflated to generate feedback on the finger. The airflow is controlled by a pair of air valves connected to a compressed air tank. Besides static pressure, the actuator can generate high-frequency vibration with high acceleration and crisp impact feedback due to fast controllability of the valves with a strong air source and a lightweight bladder membrane. In addition, our special design and fabrication of the bladder – a combination of stretchable and non-stretchable membrane layers in the ring – allows for stronger feedback. Rendering algorithms for three kinds of feedback are also presented. The performance of the system and the characteristics of the feedback are thoroughly examined using a series of measurement experiments, revealing that the system can generate a static force up to 6.3 N, the vibration up to 2.2 g magnitude and 250 Hz frequency, and an impact with less than 5 ms of latency. The whole system weighs only 255 grams (4.5 grams for the actuator and 250 grams for the controller). Finally, we demonstrate the wearability of the system by integrating the modules into a self-contained haptic device in the form of a wristlet.

Data-driven approaches are commonly used to model and render haptic textures for rigid stylus-based interaction. Current state-of-the-art data-driven methodologies synthesize acceleration signals through the interpolation of samples with different input parameters based on neural networks or parametric spectral estimation methods. In this paper, we see the potential of emerging deep learning methods in this area. To this end, we designed a complete end-to-end data-driven framework to synthesize acceleration profiles based on the proposed deep spatio-temporal network. The network is trained using contact acceleration data collected through our manual scanning stylus and interaction parameters, i.e., scanning velocities, directions, and forces. The proposed network is composed of attention-aware 1D CNNs and attention-aware encoder-decoder networks to adequately capture both the local spatial features and the temporal dynamics of the acceleration signals, which are further augmented with attention mechanisms that assign weights to the features according to their contributions. For rendering, the trained network generates synthesized signals in real-time in accordance with the user's input parameters. The whole framework was numerically compared with existing state-of-the-art approaches, showing the effectiveness of the approach. Additionally, a pilot user study is conducted to demonstrate subjective similarity.

The acceptance and usability of context-aware systems have given them the edge of wide use in various domains and has also attracted the attention of researchers in the area of context-aware computing. Making user context information available to such systems is the center of attention. However, there is very little emphasis given to the process of context representation and context fusion which are integral parts of context-aware systems. Context representation and fusion facilitate in recognizing the dependency/relationship of one data source on another to extract a better understanding of user context. The problem is more critical when data is emerging from heterogeneous sources of diverse nature like sensors, user profiles, and social interactions and also at different timestamps. Both the processes of context representation and fusion are followed in one way or another; however, they are not discussed explicitly for the realization of context-aware systems. In other words most of the context-aware systems underestimate the importance context representation and fusion. This research has explicitly focused on the importance of both the processes of context representation and fusion and has streamlined their existence in the overall architecture of context-aware systems’ design and development. Various applications of context representation and fusion in context-aware systems are also highlighted in this research. A detailed review on both the processes is provided in this research with their applications. Future research directions (challenges) are also highlighted which needs proper attention for the purpose of achieving the goal of realizing context-aware systems.

This paper presents a novel haptic texture authoring algorithm. The main goal of this algorithm is to synthesize new virtual textures by manipulating affective properties of real life textures. To this end, two different spaces are established: two-dimensional “affective space” built from a series of psychophysical experiments where textures are arranged according to affective properties (hard-soft, rough-smooth) and two-dimensional “haptic model space” where textures are placed based on features from tool-surface contact acceleration (movement-velocity, normal-force). Another space, called “authoring space” is formed to merge the two spaces; correlating changes in affective properties of real textures to changes in haptic signals in haptic space. The authoring space is constructed such that features of haptic model space that were highly correlated with affective space become axes of the space. As a result, new texture signal corresponding to any point in authoring space can be synthesized based on weighted interpolation of three nearest real surfaces in perceptually correct manner. The whole procedure including selection of nearest surfaces, finding weights, and weighted interpolation of multiple textures are evaluated through a psychophysical experiment, demonstrating competence of the approach. The results of evaluation experiment show an average normalized realism score of 94% for all authored textures.