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In the development of communication devices for individuals who are Deafblind, a significant challenge is achieving a seamless transition from human-generated to technology-mediated communication. This study compares the intelligibility of the Australian Deafblind tactile fingerspelling alphabet rendered on the HaptiComm tactile communication device with the same alphabet articulated by a human signer. After a short training period, participants identified the 26 English alphabet letters in both the mediated (device) and non-mediated (human) conditions. Results indicated that while participants easily identified most letters in the non-mediated condition, the mediated condition was more difficult to decipher. Specifically, letters presented on the palm or near the index finger had significantly lower recognition rates. These findings highlight the need for further research on the tactile features of communication devices and emphasize the importance of refining these features to enhance the reliability and readability of mediated tactile communication produced through tactile fingerspelling.

Everyday experiences suggest that a container, such as a box of chocolate sprinkles, can convey pertinent information about the nature of its content. Despite the familiarity of the experience, we do not know whether people can perceive the number of objects in the container from touch alone and how accurately they can do so. In three experiments, participants handled containers holding between one and five objects and verbally estimated their number. Containers were small cardboard jewelry boxes, and objects were round beads of varying diameter and weight. Any useful visual and auditory cues were precluded. Experiment 1 demonstrated very accurate performance, provided the objects were of sufficient weight. Experiment 2 demonstrated that withholding information about the possible number of objects inside the container does not affect accuracy at a group level but does produce occasional overestimations at an individual level. Experiment 3 demonstrated that removing the weight cue leads to systematic underestimations but does not eliminate people's ability to distinguish between different numbers of objects in the container. This study contributes to a growing picture that container haptics is surprisingly capable., Competing Interests: Declaration of Conflicting InterestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Fiber-optic sensors are a powerful tool to investigate physical properties like temperature, strain, and pressure. Such properties make these sensors interesting for many applications including biomedical applications. Fiber sensors are also a great platform for distributed sensing by using the principles of optical frequency domain reflectometry. Distributed sensing is becoming more and more used to achieve high-resolution measurements and to map physical properties of biomaterials at small scale, thus obtaining 2D and 3D mapping of a particular area of interest. This work aims at building and investigating a 2D sensing carpet based on a distributed fiber sensing technique, to map local pressure applied to the carpet. The two-dimensional mapping is obtained by embedding a single-mode optical fiber inside a soft silicone carpet. The fiber has been bent and arranged in a specific configuration characterized by several parallel lines. Different fiber fixation methods have been investigated by means of a comparative analysis to perform better characterization and to achieve a more precise response of the carpet. The best pressure sensitivity coefficient (0.373 pm/kPa or considering our setup 1.165 nm/kg) was detected when the fiber was fully embedded inside the silicone carpet. This paper demonstrates the possibility of mapping a 2D distributed pressure over a surface with a resolution of 2 mm by 2 mm. The surface of investigation is 2 cm by 6 cm, containing 310 sensing points. The sensing carpet has been validated selecting several preferential positions, by testing the consistency of the results over different portions of the carpet., Competing Interests: The authors declare no conflict of interest.

The sense of touch is fundamental for a one-to-one mapping between the environment and a robot that physically interacts with the environment. Herein, we describe a tactile fingertip design that can robustly detect interaction forces given data collected from a camera. This design is based on the photoelastic effect observed in silicone matter. Under the force applied to the silicone rubber, owing to the stress-induced birefringence, the light propagating within the silicone rubber is subjected to the angular phase shift, where the latter is proportional to the increase in the image brightness in the camera frames. We present the calibration and test results of the photoelastic sensor design on a bench using a robot arm and with a certified industrial force torque sensor. We also discuss the applications of this sensor design and its potential relationship with human mechano-transduction receptors. We achieved a force sensing range of up to 8 N with a force resolution of around 0.5 N. The photoelastic tactile fingertip is suitable for robot grasping and might lead to further progress in robust tactile sensing.

Accurate and fast contact detection between a robot manipulator and objects is crucial for safe robot-object and human-robot interactions. Traditional collision detection techniques relied on force-torque sensors and Columb friction cone estimation. However, the strain gauges used in the conventional force sensors require low-noise and high-precision electronics to deliver the signal to the final user. The Signal-to-Noise Ratio (SNR) in these devices is still an issue in light contact detection. On the other hand, the Eccentric Rotating Mass (ERM) motors are very sensitive to subtle touch as their vibrating resonant state loses immediately. The vibration, in this case, plays a core role in triggering the tactile event. This project's primary goal is to use generated and received vibrations to establish the scope of object properties that can be obtained through low-frequency generation on one end and Fourier analysis of the accelerometer data on the other end. The main idea behind the system is the phenomenon of change in vibration propagation patterns depending on the grip properties. Moreover, the project's original aim is to gather enough information on vibration feedback on objects of various properties and compare them. These data sets are further analyzed in terms of frequency and applied grip force correlations in order to prepare the ground for pattern extraction and recognition based on the physical properties of an object.

Autonomous dexterous manipulation relies on the ability to recognize an object and detect its slippage. Dynamic tactile signals are important for object recognition and slip detection. An object can be identified based on the acquired signals generated at contact points during tactile interaction. The use of vibrotactile sensors can increase the accuracy of texture recognition and preempt the slippage of a grasped object. In this work, we present a Deep Learning (DL) based method for the simultaneous texture recognition and slip detection. The method detects non-slip and slip events, the velocity, and discriminate textures-all within 17 ms. We evaluate the method for three objects grasped using an industrial gripper with accelerometers installed on its fingertips. A comparative analysis of convolutional neural networks (CNNs), feed-forward neural networks, and long short-term memory networks confirmed that deep CNNs have a higher generalization accuracy. We also evaluated the performance of the highest accuracy method for different signal bandwidths, which showed that a bandwidth of 125 Hz is enough to classify textures with 80% accuracy.