The high/low frequency balance drives the perception of noisy vibrations

Noisy vibrotactile signals transmitted during tactile explorations of an object provide precious information on the nature of its surface. Linking the properties of such vibrotactile signals to the way they are interpreted by the haptic sensory system remains challenging. In this study, we investigated humans' perception of noisy, stationary vibrations recorded during exploration of textures and reproduced using a vibrotactile actuator. Since intensity is a well-established essential perceptual attribute, an intensity equalization was first conducted, providing a model for its estimation. The equalized stimuli were further used to identify the most salient spectral features in a second experiment using dissimilarity estimations between pairs of vibrations. Based on dimensionally reduced spectral representations, linear models of dissimilarity prediction showed that the balance between low and high frequencies was the most important cue. Formal validation of this result was achieved through a Mushra experiment, where participants assessed the fidelity of resynthesized vibrations with various distorted frequency balances. These findings offer valuable insights into human vibrotactile perception and establish a computational framework for analyzing vibrations as humans do. Moreover, they pave the way for signal synthesis and compression based on sparse representations, holding significance for applications involving complex vibratory feedback

Visual Content Characterization Based on Encoding Rate-Distortion Analysis

Visual content characterization is a fundamentally important but under exploited step in dataset construction, which is essential in solving many image processing and computer vision problems. In the era of machine learning, this has become ever more important, because with the explosion of image and video content nowadays, scrutinizing all potential content is impossible and source content selection has become increasingly difficult. In particular, in the area of image/video coding and quality assessment, it is highly desirable to characterize/select source content and subsequently construct image/video datasets that demonstrate strong representativeness and diversity of the visual world, such that the visual coding and quality assessment methods developed from and validated using such datasets exhibit strong generalizability. Encoding Rate-Distortion (RD) analysis is essential for many multimedia applications. Examples of applications that explicitly use RD analysis include image encoder RD optimization, video quality assessment (VQA), and Quality of Experience (QoE) optimization of streaming videos etc. However, encoding RD analysis has not been well investigated in the context of visual content characterization. This thesis focuses on applying encoding RD analysis as a visual source content characterization method with image/video coding and quality assessment applications in mind. We first conduct a video quality subjective evaluation experiment for state-of-the-art video encoder performance analysis and comparison, where our observations reveal severe problems that motivate the needs of better source content characterization and selection methods. Then the effectiveness of RD analysis in visual source content characterization is demonstrated through a proposed quality control mechanism for video coding by eigen analysis in the space of General Quality Parameter (GQP) functions. Finally, by combining encoding RD analysis with submodular set function optimization, we propose a novel method for automating the process of representative source content selection, which helps boost the RD performance of visual encoders trained with the selected visual contents

Designing Tactile Interfaces for Abstract Interpersonal Communication, Pedestrian Navigation and Motorcyclists Navigation

The tactile medium of communication with users is appropriate for displaying information in situations where auditory and visual mediums are saturated. There are situations where a subject's ability to receive information through either of these channels is severely restricted by the environment they are in or through any physical impairments that the subject may have. In this project, we have focused on two groups of users who need sustained visual and auditory focus in their task: Soldiers on the battle field and motorcyclists. Soldiers on the battle field use their visual and auditory capabilities to maintain awareness of their environment to guard themselves from enemy assault. One of the major challenges to coordination in a hazardous environment is maintaining communication between team members while mitigating cognitive load. Compromise in communication between team members may result in mistakes that can adversely affect the outcome of a mission. We have built two vibrotactile displays, Tactor I and Tactor II, each with nine actuators arranged in a three-by-three matrix with differing contact areas that can represent a total of 511 shapes. We used two dimensions of tactile medium, shapes and waveforms, to represent verb phrases and evaluated ability of users to perceive verb phrases the tactile code. We evaluated the effectiveness of communicating verb phrases while the users were performing two tasks simultaneously. The results showed that performing additional visual task did not affect the accuracy or the time taken to perceive tactile codes. Another challenge in coordinating Soldiers on a battle field is navigating them to respective assembly areas. We have developed HaptiGo, a lightweight haptic vest that provides pedestrians both navigational intelligence and obstacle detection capabilities. HaptiGo consists of optimally-placed vibro-tactile sensors that utilize natural and small form factor interaction cues, thus emulating the sensation of being passively guided towards the intended direction. We evaluated HaptiGo and found that it was able to successfully navigate users with timely alerts of incoming obstacles without increasing cognitive load, thereby increasing their environmental awareness. Additionally, we show that users are able to respond to directional information without training. The needs of motorcyclists are di erent from those of Soldiers. Motorcyclists' need to maintain visual and auditory situational awareness at all times is crucial since they are highly exposed on the road. Route guidance systems, such as the Garmin, have been well tested on automobilists, but remain much less safe for use by motorcyclists. Audio/visual routing systems decrease motorcyclists' situational awareness and vehicle control, and thus increase the chances of an accident. To enable motorcyclists to take advantage of route guidance while maintaining situational awareness, we created HaptiMoto, a wearable haptic route guidance system. HaptiMoto uses tactile signals to encode the distance and direction of approaching turns, thus avoiding interference with audio/visual awareness. Evaluations show that HaptiMoto is intuitive for motorcyclists, and a safer alternative to existing solutions

Unsupervised learning of haptic material properties.

When touching the surface of an object, its spatial structure translates into a vibration on the skin. The perceptual system evolved to translate this pattern into a representation that allows to distinguish between different materials. Here, we show that perceptual haptic representation of materials emerges from efficient encoding of vibratory patterns elicited by the interaction with materials. We trained a deep neural network with unsupervised learning (Autoencoder) to reconstruct vibratory patterns elicited by human haptic exploration of different materials. The learned compressed representation (i.e., latent space) allows for classification of material categories (i.e., plastic, stone, wood, fabric, leather/wool, paper, and metal). More importantly, classification performance is higher with perceptual category labels as compared to ground truth ones, and distances between categories in the latent space resemble perceptual distances, suggesting a similar coding. Crucially, the classification performance and the similarity between the perceptual and the latent space decrease with decreasing compression level. We could further show that the temporal tuning of the emergent latent dimensions is similar to properties of human tactile receptors

Task-Oriented and Semantics-Aware 6G Networks

Upon the arrival of emerging devices, including Extended Reality (XR) and Unmanned Aerial Vehicles (UAVs), the traditional bit-oriented communication framework is approaching Shannon's physical capacity limit and fails to guarantee the massive amount of transmission within latency requirements. By jointly exploiting the context of data and its importance to the task, an emerging communication paradigm shift to semantic level and effectiveness level is envisioned to be a key revolution in Sixth Generation (6G) networks. However, an explicit and systematic communication framework incorporating both semantic level and effectiveness level has not been proposed yet. In this article, we propose a generic task-oriented and semantics-aware (TOSA) communication framework for various tasks with diverse data types, which incorporates both semantic level information and effectiveness-aware performance metrics. We first analyze the unique characteristics of all data types, and summarise the semantic information, along with corresponding extraction methods. We then propose a detailed TOSA communication framework for different time-critical and non-critical tasks. In the TOSA framework, we present the TOSA information, extraction methods, recovery methods, and effectiveness-aware performance metrics. Last but not least, we present a TOSA framework tailored for Unmanned Aerial Vehicle (UAV) control task to validate the effectiveness of the proposed TOSA communication framework

Haptics: Science, Technology, Applications

This open access book constitutes the proceedings of the 13th International Conference on Human Haptic Sensing and Touch Enabled Computer Applications, EuroHaptics 2022, held in Hamburg, Germany, in May 2022. The 36 regular papers included in this book were carefully reviewed and selected from 129 submissions. They were organized in topical sections as follows: haptic science; haptic technology; and haptic applications

Distributed Sensing and Stimulation Systems Towards Sense of Touch Restoration in Prosthetics

Modern prostheses aim at restoring the functional and aesthetic characteristics of the lost limb. To foster prosthesis embodiment and functionality, it is necessary to restitute both volitional control and sensory feedback. Contemporary feedback interfaces presented in research use few sensors and stimulation units to feedback at most two discrete feedback variables (e.g. grasping force and aperture), whereas the human sense of touch relies on a distributed network of mechanoreceptors providing high-fidelity spatial information. To provide this type of feedback in prosthetics, it is necessary to sense tactile information from artificial skin placed on the prosthesis and transmit tactile feedback above the amputation in order to map the interaction between the prosthesis and the environment. This thesis proposes the integration of distributed sensing systems (e-skin) to acquire tactile sensation, and non-invasive multichannel electrotactile feedback and virtual reality to deliver high-bandwidth information to the user. Its core focus addresses the development and testing of close-loop sensory feedback human-machine interface, based on the latest distributed sensing and stimulation techniques for restoring the sense of touch in prosthetics. To this end, the thesis is comprised of two introductory chapters that describe the state of art in the field, the objectives and the used methodology and contributions; as well as three studies distributed over stimulation system level and sensing system level. The first study presents the development of close-loop compensatory tracking system to evaluate the usability and effectiveness of electrotactile sensory feedback in enabling real-time close-loop control in prosthetics. It examines and compares the subject\u2019s adaptive performance and tolerance to random latencies while performing the dynamic control task (i.e. position control) and simultaneously receiving either visual feedback or electrotactile feedback for communicating the momentary tracking error. Moreover, it reported the minimum time delay needed for an abrupt impairment of users\u2019 performance. The experimental results have shown that electrotactile feedback performance is less prone to changes with longer delays. However, visual feedback drops faster than electrotactile with increased time delays. This is a good indication for the effectiveness of electrotactile feedback in enabling close- loop control in prosthetics, since some delays are inevitable. The second study describes the development of a novel non-invasive compact multichannel interface for electrotactile feedback, containing 24 pads electrode matrix, with fully programmable stimulation unit, that investigates the ability of able-bodied human subjects to localize the electrotactile stimulus delivered through the electrode matrix. Furthermore, it designed a novel dual parameter -modulation (interleaved frequency and intensity) and compared it to conventional stimulation (same frequency for all pads). In addition and for the first time, it compared the electrotactile stimulation to mechanical stimulation. More, it exposes the integration of virtual prosthesis with the developed system in order to achieve better user experience and object manipulation through mapping the acquired real-time collected tactile data and feedback it simultaneously to the user. The experimental results demonstrated that the proposed interleaved coding substantially improved the spatial localization compared to same-frequency stimulation. Furthermore, it showed that same-frequency stimulation was equivalent to mechanical stimulation, whereas the performance with dual-parameter modulation was significantly better. The third study presents the realization of a novel, flexible, screen- printed e-skin based on P(VDF-TrFE) piezoelectric polymers, that would cover the fingertips and the palm of the prosthetic hand (particularly the Michelangelo hand by Ottobock) and an assistive sensorized glove for stroke patients. Moreover, it developed a new validation methodology to examine the sensors behavior while being solicited. The characterization results showed compatibility between the expected (modeled) behavior of the electrical response of each sensor to measured mechanical (normal) force at the skin surface, which in turn proved the combination of both fabrication and assembly processes was successful. This paves the way to define a practical, simplified and reproducible characterization protocol for e-skin patches In conclusion, by adopting innovative methodologies in sensing and stimulation systems, this thesis advances the overall development of close-loop sensory feedback human-machine interface used for restoration of sense of touch in prosthetics. Moreover, this research could lead to high-bandwidth high-fidelity transmission of tactile information for modern dexterous prostheses that could ameliorate the end user experience and facilitate it acceptance in the daily life

Investigating the Effects of Physiology-driven Vibro-tactile Biofeedback for Mitigating State Anxiety during Public Speaking

For some, public speaking can cause heightened moments of stress while giving a speech or presentation. These moments are quantifiable through one’s physiology and vocal characteristics, measurable through sensor-enabled smart technology. Through these measurements, we can assess the current state of the individual to determine opportune moments to deliver interventions that alleviate symptoms of stressful moments. Recent work in wrist-worn vibrotactile biofeedback suggests that it is a promising intervention towards reducing state-based anxiety for public speaking. However, since the vibrotactile stimulus is delivered constantly, adaptation could risk diminishing relieving effects. Therefore, we administer vibrotactile biofeedback as a just-in-time adaptive intervention during in-the-moment heightened levels of stress. We evaluate two types of vibrotactile feedback delivery mechanisms in a between-subjects design – one that delivers stimulus randomly and one that delivers stimulus during moments of heightened physiological reactivity, as determined by changes in electrodermal activity. The results from these interventions indicate that vibrotactile biofeedback administered during high physiological arousal appears to improve stress-related measures early on, but these effects diminish over time. However, we also observe no significant differences in self-reported state anxiety scores between experiment groups. In the latter half of this thesis, we will explore methods for personalizing machine learning models that detect the onset of heightened moments of stress in real-time. Results indicate that baseline-norming, fine-tuning on participant-specific data, and providing individual-specific trait information are all helpful techniques for improving stress detection performance