Search Results (221)

Tactile perception plays a crucial role in the perception of products and consumer preferences. This perception process is structured in hierarchical layers comprising a sensory layer (soft and smooth) and an affective layer (comfort and luxury). In this study, we attempted to predict the evaluation score of sensory and affective tactile perceptions of materials using a biomimetic multimodal tactile sensor that mimics the active touch behavior of humans and measures physical parameters such as force, vibration, and temperature. We conducted sensory and affective descriptor evaluations on 32 materials, including cosmetics, textiles, and leather. Using the physical parameters obtained by the biomimetic multimodal tactile sensor as explanatory variables, we predicted the scores of the sensory and affective descriptors in 10 regression models. The bagging regressor demonstrated the best performance, achieving a coefficient of determination (R²) of >0.6 for fourteen of nineteen sensory and eight of twelve affective descriptors. The present model exhibited particularly high prediction accuracy for sensory descriptors such as “moist” and “elastic”, and for affective descriptors such as “pleasant” and “like”. These findings suggest a method to support efficient tactile design in product development across various industries by predicting tactile descriptor scores using physical parameters from a biomimetic tactile sensor. Full article

The design of street spaces significantly influences public behavior and quality of life. Understanding how various urban street spatial characteristics affect public behavior, alongside the role of multi-sensory perception, enables designers and planners to create more human-centered urban environments. Grounded in landsenses ecology, this study employs correlation analysis, regression analysis, and Partial Least-Squares Structural Equation Modeling (PLS-SEM) to examine the effects of different urban street spatial characteristics on public behavior and the mediating role of multi-sensory perception. The findings reveal that street spatial characteristics, particularly the Water Surface Ratio (WSR) and Waterfront Density (WD), have a pronounced impact on behavioral traits, with higher public activity frequencies in areas with elevated WSR and WD. Notably, WSR significantly affects static behaviors, such as sunbathing (β = 0.371, p < 0.001), and dynamic behaviors, such as walking (β = 0.279, p < 0.001). While road and water characteristics directly influence behavior, buildings and green spaces mainly affect public behavior through multi-sensory perception. Different sensory perceptions show varying effects, with olfactory perception playing a significant role in these experiences, alongside a notable chain-mediated effect between tactile perception and psychological cognition. These results provide valuable insights for integrating multi-sensory experiences into urban design. Full article

Electronic skin is widely employed in multiple applications such as health monitoring, robot tactile perception, and bionic prosthetics. In this study, we fabricated millimeter-scale electronic skin featuring compact sensing units using the Boston Micro Fabrication S130 (a high-precision additive manufacturing device) and the template removal method. We used a gallium-based liquid metal and achieved an inner channel diameter of 0.1 mm. The size of the sensing unit was 3 × 3 mm². This unit exhibited a wide linear sensing range (10–22,000 Pa) and high-pressure resolution (10 Pa) even on an ultra-curved surface (radius of curvature was 6 mm). Sliding was successfully detected at speeds of 8–54 mm/s. An artificial nose with nine sensing units was fabricated, and it exhibited excellent multitouch and sliding trajectory recognition capabilities. This confirmed that the electronic skin functioned normally, even on an ultra-curved surface. Full article

This paper introduces the TacFR-Gripper, a novel reconfigurable soft robotic gripper inspired by the Fin-Ray effect and equipped with tactile skin. The gripper incorporates a four-bar mechanism for accurate finger bending and a reconfigurable design to change the relative positions between the fingers and palm, enabling precise and adaptable object grasping. This 5-Degree-of-Freedom (DOF) soft gripper can facilitate dexterous manipulation of objects with diverse shapes and stiffness and is beneficial to the safe and efficient grasping of delicate objects. An array of Force Sensitive Resistor (FSR) sensors is embedded within each robotic fingertip to serve as the tactile skin, enabling the robot to perceive contact information during manipulation. Moreover, we implemented a threshold-based tactile perception approach to enable reliable grasping without accidental slip or excessive force. To verify the effectiveness of the TacFR-Gripper, we provide detailed workspace analysis to evaluate its grasping performance and conducted three experiments, including (i) assessing the grasp success rate across various everyday objects through different finger configurations, (ii) verifying the effectiveness of tactile skin with different control strategies in grasping, and (iii) evaluating the in-hand manipulation capabilities through object pose control. The experimental results indicate that the TacFR-Gripper can grasp a wide range of complex-shaped objects with a high success rate and deliver dexterous in-hand manipulation. Additionally, the integration of tactile skin is demonstrated to enhance grasp stability by incorporating tactile feedback during manipulations. Full article

This study develops biomimetic strategies for slip prevention in prosthetic hand grasps. The biomimetic system is driven by a novel slip sensor, followed by slip perception and preventive control. Here, we show that biologically inspired sensorimotor pathways can be restored between the prosthetic hand and users. A Ruffini endings-like slip sensor is used to detect shear forces and identify slip events directly. The slip information and grip force are encoded into a bi-state sensory coding that evokes vibration and buzz tactile sensations in subjects with transcutaneous electrical nerve stimulation (TENS). Subjects perceive slip events under various conditions based on the vibration sensation and voluntarily adjust grip force to prevent further slipping. Additionally, short-latency compensation for grip force is also implemented using a neuromorphic reflex pathway. The reflex loop includes a sensory neuron and interneurons to adjust the activations of antagonistic muscles reciprocally. The slip prevention system is tested in five able-bodied subjects and two transradial amputees with and without reflex compensation. A psychophysical test for perception reveals that the slip can be detected effectively, with a success accuracy of 96.57%. A slip protection test indicates that reflex compensation yields faster grasp adjustments than voluntary action, with a median response time of 0.30 (0.08) s, a rise time of 0.26 (0.03) s, an execution time of 0.56 (0.07) s, and a slip distance of 0.39 (0.10) cm. Prosthetic grip force is highly correlated to that of an intact hand, with a correlation coefficient of 96.85% (2.73%). These results demonstrate that it is feasible to reconstruct slip biomimetic sensorimotor pathways that provide grasp stability for prosthetic users. Full article

This pilot study explored how muscle activation influences the pattern recognition of tactile cues delivered using electrical stimulation (ES) during each 10% window interval of the normal walking gait cycle (GC). Three healthy adults participated in the experiment. After identifying the appropriate threshold, ES as the haptic cue was applied to the gastrocnemius lateralis (GL) and biceps brachii (BB) of participants walking on a treadmill. Findings revealed variable recognition patterns across participants, with the BB showing more variability during walking due to its minimal activity compared to the actively engaged GL. Dynamic time warping (DTW) was used to assess the similarity between muscle activation and electro-stimulated haptic perception. The DTW distance between electromyography (EMG) signals and muscle recognition patterns was significantly smaller for the GL (4.87 ± 0.21, mean ± SD) than the BB (8.65 ± 1.36, mean ± SD), showing a 78.6% relative difference, indicating that higher muscle activation was generally associated with more consistent haptic perception. However, individual differences and variations in recognition patterns were observed, suggesting personal variability influenced the perception outcomes. The study underscores the complexity of human neuromuscular responses to artificial sensory stimuli and suggests a potential link between muscle activity and haptic perception. Full article

Obstacle contact detection is not commonly employed in autonomous robots, which mainly depend on avoidance algorithms, limiting their effectiveness in cluttered environments. Current contact-detection techniques suffer from blind spots or discretized detection points, and rigid platforms further limit performance by merely detecting the presence of a collision without providing detailed feedback. To address these challenges, we propose an innovative contact sensor design that improves autonomous navigation through physical contact detection. The system features an elastic collision platform integrated with flex sensors to measure displacements during collisions. A neural network-based contact-detection algorithm converts the flex sensor data into actionable contact information. The collision system was validated with collisions through manual flights and autonomous contact-based missions, using sensor feedback for real-time collision recovery. The experimental results demonstrated the system’s capability to accurately detect contact events and estimate collision parameters, even under dynamic conditions. The proposed solution offers a robust approach to improving autonomous navigation in complex environments and provides a solid foundation for future research on contact-based navigation systems. Full article

Tactile aspects of the urban environment may be recognised through various capacities of human sensation, including cutaneous, kinaesthetic, and proprioceptive awareness. Haptic attributes often need intentional engagement for ultimate experience and information provision, but it is exactly this imprecision that initiates challenges when dealing with tactility in a space. Whilst tactile components can be experienced when stationary or through walking an identified space (a sensewalk), there is no standard method for tactile sensewalk implementation. We therefore critically discuss common methodologies, outlining benefits, disadvantages, and suitability for each identified sensewalk method. We find that immersive sensory enquiry approaches are well suited to reveal past experience of sensory perceptions, deeper understandings and rediscovery of places; the recording of dimensional measurements is appropriate when determining linkages and correlations between physical and perceptual dimensions; and interviews and post-data collection reflections provide an opportunity to explore sensory characteristics and experience, and for realisation of local heritages. Furthermore, unusual methods such as the visualisation of urban textures and multimodal post-processing techniques are identified and discussed. Full article

Plant proteins are increasingly incorporated into food products to enhance their nutritional value. However, little is known about how this alters the textural perceptions of such products. This study investigated the substitution of up to 35% wheat flour with pea protein isolate (PPI) into pasta sheets to determine how this influenced texture. Furthermore, an in vivo human mastication test (n = 116 participants) was conducted to evaluate oral tactile perceptions (perceived firmness, stickiness, and brittleness) and chewing time associated with PPI-containing pasta. Cooked pasta hardness decreased from 145 to 96 N at 5% PPI substitution due to the disruption of gluten network but increased to 144 N at 15–25% PPI substitution, indicating a stronger protein network at higher PPI substitution levels. In vivo, pasta substituted with 25% PPI required a shorter chewing time and was perceived as less firm, less sticky, and more brittle than wheat flour-only pasta. Regardless of pasta samples, fast chewers (average chewing time ≤13 s) were better at recognizing differences in pasta firmness, while slow chewers (>13 s) were more sensitive to changes in stickiness and brittleness. The results obtained in this study could contribute to the design of protein-rich pasta tailored to populations with specific texture requirements (e.g., softer texture for the elderly). Full article

Vision and touch are fundamental sensory modalities that enable humans to perceive and interact with objects in their environment. Vision facilitates the perception of attributes such as shape, color, and texture from a distance, while touch provides detailed information at the contact level, including fine textures and material properties. Despite their distinct roles, the processing of visual and tactile information shares underlying similarities, presenting a unique opportunity to enhance artificial systems that integrate these modalities. However, existing methods for combining vision and touch often rely on data fusion at the decision level, requiring extensive labeled data and facing challenges in generalizing to novel situations. In this paper, we leverage contrastive learning to train a convolutional neural network on textile data using both visual and tactile inputs. Our objective is to develop a network capable of extracting unified representations from both modalities without the need for extensive labeled datasets. We explore using a contrastive loss function to optimize the learning process. Our results demonstrate that the shared representations effectively capture critical data structures and features from both sensory modalities, enabling successful differentiation between object classes based on both vision and touch. We validate our approach through a series of experiments, optimizing hyperparameters to maximize performance. The findings suggest that extracting shared representations for vision and touch not only enhances the integration of visual and tactile information but also provides a robust framework for multimodal perception in artificial systems. Full article

Immersive human–machine interaction relies on comprehensive sensing and feedback systems, which enable transmission of multiple pieces of information. However, the integration of increasing numbers of feedback actuators and sensors causes a severe issue in terms of system complexity. In this work, we propose a pressure-sensing and feedback glove that enables multi-dimensional pressure sensing and feedback with a minimalist design of the functional units. The proposed glove consists of modular strain and pressure sensors based on films of liquid metal microchannels and coin vibrators. Strain sensors located at the finger joints can simultaneously project the bending motion of the individual joint into the virtual space or robotic hand. For subsequent tactile interactions, the design of two symmetrically distributed pressure sensors and vibrators at the fingertips possesses capabilities for multi-directional pressure sensing and feedback by evaluating the relationship of the signal variations between two sensors and tuning the feedback intensities of two vibrators. Consequently, both dynamic and static multi-dimensional pressure communication can be realized, and the vibrational actuation can be monitored by a liquid-metal-based sensor via a triboelectric sensing mechanism. A demonstration of object interaction indicates that the proposed glove can effectively detect dynamic force in varied directions at the fingertip while offering the reconstruction of a similar perception via the haptic feedback function. This device introduces an approach that adopts a minimalist design to achieve a multi-functional system, and it can benefit commercial applications in a more cost-effective way. Full article

Introduction: In recent years, the integration of robotic systems into various aspects of daily life has become increasingly common. As these technologies continue to advance, ensuring user-friendly interfaces and seamless interactions becomes more essential. For social robots to genuinely provide lasting value to humans, a favourable user experience (UX) emerges as an essential prerequisite. This article aimed to evaluate the usability of the MINI robot, highlighting its strengths and areas for improvement based on user feedback and performance. Materials and Methods: In a controlled lab setting, a mixed-method qualitative study was conducted with ten individuals aged 65 and above diagnosed with mild dementia (MD) and mild cognitive impairment (MCI). Participants engaged in individual MINI robot interaction sessions, completing cognitive tasks as per written instructions. Video and audio recordings documented interactions, while post-session System Usability Scale (SUS) questionnaires quantified usability perception. Ethical guidelines were followed, ensuring informed consent, and the data underwent qualitative and quantitative analyses, contributing insights into the MINI robot’s usability for this demographic. Results: The study addresses the ongoing challenges that tasks present, especially for MD individuals, emphasizing the importance of user support. Most tasks require both verbal and physical interactions, indicating that MD individuals face challenges when switching response methods within subtasks. These complexities originate from the selection and use of response methods, including difficulties with voice recognition, tablet touch, and tactile sensors. These challenges persist across tasks, with individuals with MD struggling to comprehend task instructions and provide correct answers and individuals with MCI struggling to use response devices, often due to the limitations of the robot’s speech recognition. Technical shortcomings have been identified. The results of the SUS indicate positive perceptions, although there are lower ratings for instructor assistance and pre-use learning. The average SUS score of 68.3 places device usability in the “good” category. Conclusions: Our study examines the usability of the MINI robot, revealing strengths in quick learning, simple system and operation, and integration of features, while also highlighting areas for improvement. Careful design and modifications are essential for meaningful engagement with people with dementia. The robot could better benefit people with MD and MCI if clear, detailed instructions and instructor assistance were available. Full article

Understanding document layouts is vital for enhancing document exploration and information retrieval for sighted individuals. However, for blind and visually impaired people, it becomes challenging to have access to layout information using typical assistive technologies such as screen readers. In this paper, we examine the potential benefits of presenting documents on two-dimensional (2D) refreshable tactile displays. These displays enable the tactile perception of 2D data, offering the advantage of dynamic and interactive functionality. Despite their potential, the development of user interfaces (UIs) for such displays has not advanced significantly. Thus, we propose a design of an intelligent tactile user interface (TUI), incorporating touch and audio feedback to represent documents in a tactile format. Our exploratory study for evaluating this approach revealed satisfaction from participants with the experience of directly viewing documents in their true form, rather than relying on screen-reading interpretations. Additionally, participants offered recommendations for incorporating additional features and refining the approach in future iterations. To facilitate further research and development, we have made our dataset and models publicly available. Full article

Current XR applications move beyond audiovisual information, with haptic feedback rapidly gaining ground. However, current haptic devices are still evolving and often struggle to combine key desired features in a balanced way. In this paper, we propose the development of a high-resolution haptic (HRH) system for perception enhancement, a wearable technology designed to augment extended reality (XR) experiences through precise and localized tactile feedback. The HRH system features a modular design with 58 individually addressable actuators, enabling intricate haptic interactions within a compact wearable form. Dual ESP32-S3 microcontrollers and a custom-designed system ensure robust processing and low-latency performance, crucial for real-time applications. Integration with the Unity game engine provides developers with a user-friendly and dynamic environment for accurate, simple control and customization. The modular design, utilizing a flexible PCB, supports a wide range of actuators, enhancing its versatility for various applications. A comparison of our proposed system with existing solutions indicates that the HRH system outperforms other devices by encapsulating several key features, including adjustability, affordability, modularity, and high-resolution feedback. The HRH system not only aims to advance the field of haptic feedback but also introduces an intuitive tool for exploring new methods of human–computer and XR interactions. Future work will focus on refining and exploring the haptic feedback communication methods used to convey information and expand the system’s applications. Full article

This paper introduces a novel capacitive sensor array designed for tactile perception applications. Utilizing an all-in-one inkjet deposition printing process, the sensor array exhibited exceptional flexibility and accuracy. With a resolution of up to 32.7 dpi, the sensor array was capable of capturing the fine details of touch inputs, making it suitable for applications requiring high spatial resolution. The design incorporates two multiplexers to achieve a scanning rate of 100 Hz, ensuring the rapid and responsive data acquisition that is essential for real-time feedback in interactive applications, such as gesture recognition and haptic interfaces. To evaluate the performance of the capacitive sensor array, an experiment that involved handwritten number recognition was conducted. The results demonstrated that the sensor accurately captured fingertip inputs with a high precision. When combined with an Auxiliary Classifier Generative Adversarial Network (ACGAN) algorithm, the sensor system achieved a recognition accuracy of 98% for various handwritten numbers from “0” to “9”. These results show the potential of the capacitive sensor array for advanced human–computer interaction applications. Full article