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A tactile image sensor employing a camera is capable of obtaining rich tactile information through image sequences with high spatial resolution. There have been many studies on the tactile image sensors from more than 30 years ago, and, recently, they have been applied in the field of robotics. Tactile image sensors can be classified into three typical categories according to the method of conversion from physical contact to light signals: Light conductive plate-based, marker displacement- based, and reflective membrane-based sensors. Other important elements of the sensor, such as the optical system, image sensor, and post-image analysis algorithm, have been developed. In this work, the literature is surveyed, and an overview of tactile image sensors employing a camera is provided with a focus on the sensing principle, typical design, and variation in the sensor configuration.

The correction affiliation is: NTT Communication Science Laboratories, NTT Corporation, Atsugi, Kanagawa, Japan. Citation: Lin I-F, Kashino M (2012) Correction: Perceptual Grouping over Time within and across Auditory and Tactile Modalities.

The artificial tactile sensory system (ATSS) contributes to tactile sensation in human–machine interaction. However, it is been a challenge for them serving as the function comparable to the human tactile perception system (HTPS). The bioinspiration from human system is an optimal solution.Biomechanisms of the HTPS are systematically interpreted to inspire the development of ATSSs.Current biomimetic strategies and technologies in the sensing, transmitting, and processing components of ATSSs are addressed as an integral system. The sense of touch events, achieved by artificial tactile sensory systems (ATSSs), is a milestone in the progress of human–machine interactions. However, it has been a challenge for ATSSs to serve functions comparable with the human tactile perception system (HTPS). The biomimetic strategies and technologies inspired by HTPS are considered an optimal solution to this challenge. Recent studies have reported bioinspired strategies for improving specific aspects of ATSS performance, such as feature collection, signal conversion, and information computation. Here, we present a systematic interpretation of biomechanisms for HTPSs, and correspondingly, address biomimetic strategies and technologies contributing to ATSSs as an integral system. This review will benefit the development and application of ATSSs in the future.

There is a steadily growing number of mobile communication systems that provide spatially encoded tactile information to the humans’ torso. However, the increased use of such hands-off displays is currently not matched with or supported by systematic perceptual characterization of tactile spatial discrimination on the torso. Furthermore, there are currently no data testing spatial discrimination for dynamic force stimuli applied to the torso. In the present study, we measured tactile point localization (LOC) and tactile direction discrimination (DIR) on the thoracic spine using two unisex torso-worn tactile vests realized with arrays of 3 × 3 vibrotactile or force feedback actuators. We aimed to, first, evaluate and compare the spatial discrimination of vibrotactile and force stimulations on the thoracic spine and, second, to investigate the relationship between the LOC and DIR results across stimulations. Thirty-four healthy participants performed both tasks with both vests. Tactile accuracies for vibrotactile and force stimulations were 60.7% and 54.6% for the LOC task; 71.0% and 67.7% for the DIR task, respectively. Performance correlated positively with both stimulations, although accuracies were higher for the vibrotactile than for the force stimulation across tasks, arguably due to specific properties of vibrotactile stimulations. We observed comparable directional anisotropies in the LOC results for both stimulations; however, anisotropies in the DIR task were only observed with vibrotactile stimulations. We discuss our findings with respect to tactile perception research as well as their implications for the design of high-resolution torso-mounted tactile displays for spatial cueing.

Skin-like sensors capable of detecting multiple stimuli simultaneously have great potential in cutting-edge human-machine interaction. However, realizing multimodal tactile recognition beyond human tactile perception still faces significant challenges. Here, an extreme environments-adaptive multimodal triboelectric sensor was developed, capable of detecting pressure/temperatures beyond the range of human perception. Based on triboelectric nanogenerator technology, an asymmetric structure capable of independently outputting dual signals was designed to improve perception sensitivity. By converting the signals and the stimuli into feature matrices, parallel perception of complex objects (with a recognition rate of 94%) and temperature at high temperatures was achieved. The proposed multimodal triboelectric tactile sensor represents progress in maximum detection range and rapid response, realizing the upper limit of human skin’s high-temperature sensing (60 °C) with a working temperature of 200 °C. The proposed self-powered multimodal sensing system offers a wider range of possibilities for human/robot/environment interaction applications. Existing tactile sensors struggle with high-temperature environments. Here, authors developed a triboelectric tactile sensor with an asymmetric structure and heat-resistant materials, enabling 94% object recognition rate, fast response times, and stable performance up to 200 °C.

In this study, the authors study a deep learning model that can convert vision into tactile information, so that different texture images can be fed back to the tactile signal close to the real tactile sensation after training and learning. This study focuses on the classification of different image visual information and its corresponding tactile feedback output mode. A training model of ensembled generative adversarial networks is proposed, which has the characteristics of simple training and stable efficiency of the result. At the same time, compared with the previous methods of judging the tactile output, in addition to subjective human perception, this study also provides an objective and quantitative evaluation system to verify the performance of the model. The experimental results show that the learning model can transform the visual information of the image into the tactile information, which is close to the real tactile sensation, and also verify the scientificity of the tactile evaluation method.

Deducing the input signal for a tactile display to present the target surface (i.e., solving the inverse problem for tactile displays) is challenging. We proposed the encoding and presentation (EP) method in our prior work, where we encoded the target surface by scanning it using an array of piezoelectric devices (encoding) and then drove the piezoelectric devices using the obtained signals to display the surface (presentation). The EP method reproduced the target texture with an accuracy of over 80% for the five samples tested, which we refer to as replicability. Machine learning is a promising method for solving inverse problems. In this study, we designed a neural network to connect the subjective evaluation of tactile sensation and the input signals to a display; these signals are described as time-domain waveforms. First, participants were asked to touch the surface presented by the mechano-tactile display based on the encoded data from the EP method. Then, the participants recorded the similarity of the surface compared to five material samples, which were used as the input. The encoded data for the material samples were used as the output to create a dataset of 500 vectors. By training a multilayer perceptron with the dataset, we deduced new inputs for the display. The results indicate that using machine learning for fine tuning leads to significantly better accuracy in deducing the input compared to that achieved using the EP method alone. The proposed method is therefore considered a good solution for the inverse problem for tactile displays.

Electronic skins are essential for real-time health monitoring and tactile perception in robots. Although the use of soft elastomers and microstructures have improved the sensitivity and pressure-sensing range of tactile sensors, the intrinsic viscoelasticity of soft polymeric materials remains a long-standing challenge resulting in cyclic hysteresis. This causes sensor data variations between contact events that negatively impact the accuracy and reliability. Here, we introduce the Tactile Resistive Annularly Cracked E-Skin (TRACE) sensor to address the inherent trade-off between sensitivity and hysteresis in tactile sensors when using soft materials. We discovered that piezoresistive sensors made using an array of three-dimensional (3D) metallic annular cracks on polymeric microstructures possess high sensitivities (> 10⁷ Ω · kPa−1), low hysteresis (2.99 ± 1.37%) over a wide pressure range (0–20 kPa), and fast response (400 Hz). We demonstrate that TRACE sensors can accurately detect and measure the pulse wave velocity (PWV) when skin mounted. Moreover, we show that these tactile sensors when arrayed enabled fast reliable one-touch surface texture classification with neuromorphic encoding and deep learning algorithms.

Ultrathin and flexible electromagnetic shielding materials hold great potential in civil and military applications. Despite tremendous research efforts, the development of advanced shielding materials is still needed to provide additional functionalities for various artificial-intelligence-driven systems, such as tactile sensing ability. Herein, a layering design strategy is proposed to fabricate ultrathin Ti 3 C 2 T x MXene-aramid nanofiber (MA) films by a layer-by-layer assembling process. Compared to that of randomly mixed films, the designed MA films exhibited a higher EMI shielding efficiency at an ultrathin thickness of 9 µm, which increased from 26.4 to 40.7 dB, owing to the additional multiple-interface scattering mechanism. Importantly, the novel MA films displayed strong EMI shielding ability even after heating/cooling treatments within a wide temperature range of −196 to 300 °C. Moreover, the same material displayed a tensile strength of 124.1 ± 2.7 MPa and a toughness of 6.3 ± 1.1 MJ·m −3 , which are approximately 9.1 times and 45 times higher than those of pure MXene films, respectively. The MA film is also capable of detecting tactile signals via the triboelectric effect. A 2 × 4 tactile sensor array was developed to achieve an accurate signal catching capability. Therefore, in addition to the shielding performance, the manifestation of tactile perception by the MA films offers exciting opportunities in the fields of soft robotics and human-machine interactions.

Vision of the body has been reported to improve tactile acuity even when vision is not informative about the actual tactile stimulation. However, it is currently unclear whether this effect is limited to body parts such as hand, forearm or foot that can be normally viewed, or it also generalizes to body locations, such as the shoulder, that are rarely before our own eyes. In this study, subjects consecutively performed a detection threshold task and a numerosity judgment task of tactile stimuli on the shoulder. Meanwhile, they watched either a real-time video showing their shoulder or simply a fixation cross as control condition. We show that non-informative vision improves tactile numerosity judgment which might involve tactile acuity, but not tactile sensitivity. Furthermore, the improvement in tactile accuracy modulated by vision seems to be due to an enhanced ability in discriminating the number of adjacent active electrodes. These results are consistent with the view that bimodal visuotactile neurons sharp tactile receptive fields in an early somatosensory map, probably via top-down modulation of lateral inhibition.