Search

Soft biomimetic electronic devices primarily comprise an electronic skin (e-skin) capable of implementing various wearable/implantable applications such as soft human–machine interfaces, epidermal healthcare systems, and neuroprosthetics owing to its high mechanical flexibility, tissue conformability, and multifunctionality. The conformal contact of the e-skin with living tissues enables more precise analyses of physiological signals, even in the long term, as compared to rigid electronic devices. In this regard, e-skin can be considered as a promising formfactor for developing highly sensitive and transparent pressure sensors. Specifically, to minimize the modulus mismatch at the biotic–abiotic interface, transparent-conductive hydrogels have been used as electrodes with exceptional pressing durability. However, critical issues such as dehydration and low compatibility with elastomers remain a challenge. In this paper, we propose a skin-like transparent polymer-hydrogel hybrid pressure sensor (HPS) with microstructures based on the polyacrylamide/sodium-alginate hydrogel and p-PVDF-HFP-DBP polymer. The encapsulated HPS achieves conformal contact with skin due to its intrinsically stretchable, highly transparent, widely sensitive, and anti-dehydrative properties. We believe that the HPS is a promising candidate for a robust transparent epidermal stretchable-skin device.

Skin is the largest sensory organ and receives information from external stimuli. Human body signals have been monitored using wearable devices, which are gradually being replaced by electronic skin (E-skin). We assessed the basic technologies from two points of view: sensing mechanism and material. Firstly, E-skins were fabricated using a tactile sensor. Secondly, E-skin sensors were composed of an active component performing actual functions and a flexible component that served as a substrate. Based on the above fabrication processes, the technologies that need more development were introduced. All of these techniques, which achieve high performance in different ways, are covered briefly in this paper. We expect that patients’ quality of life can be improved by the application of E-skin devices, which represent an applied advanced technology for real-time bio- and health signal monitoring. The advanced E-skins are convenient and suitable to be applied in the fields of medicine, military and environmental monitoring.

Electronic skin that is deformable, self-healable, and self-powered has high competitiveness for next-generation energy/sense/robotic applications. Herein, we fabricated a stretchable, self-healable triboelectric nanogenerator (SH-TENG) as electronic skin for energy harvesting and tactile sensing. The elongation of SH-TENG can achieve 800% (uniaxial strain) and the SH-TENG can self-heal within 2.5 min. The SH-TENG is based on the single-electrode mode, which is constructed from ion hydrogels with an area of 2 cm × 3 cm, the output of short-circuit transferred charge (Qsc), open-circuit voltage (Voc), and short-circuit current (Isc) reaches ~6 nC, ~22 V, and ~400 nA, and the corresponding output power density is ~2.9 μW × cm−2 when the matching resistance was ~140 MΩ. As a biomechanical energy harvesting device, the SH-TENG also can drive red light-emitting diodes (LEDs) bulbs. Meanwhile, SH-TENG has shown good sensitivity to low-frequency human touch and can be used as an artificial electronic skin for touch/pressure sensing. This work provides a suitable candidate for the material selection of the hydrogel-based self-powered electronic skin.

Electronic tactile sensing becomes an active research field whether for prosthetic applications, robotics, virtual reality or post stroke patients rehabilitation. To achieve such sensing, an array of sensors is used to retrieve human-skin like information, which is called Electronic skin (E-skin). Humans through their skins, are able to collect different types of information e.g. pressure, temperature, texture, etc. which are then passed to the nervous system, and finally to the brain in order to extract high level information from these sensory data. In order to make E-skin capable of such task, data acquired from E-skin should be filtered, processed, and then conveyed to the user (or robot). Processing these sensory information, should occur in real-time, taking in consideration the power limitation in such applications, especially prosthetic applications. The power consumption itself is related to different factors, one factor is the complexity of the algorithm e.g. number of FLOPs, and another is the memory consumption. In this thesis, I will focus on the processing of real tactile information, by 1)exploring different algorithms and methods for tactile data classification, 2)data organization and preprocessing of such tactile data and 3)hardware implementation. More precisely the focus will be on deep learning algorithms for tactile data processing mainly CNNs and RNNs, with energy-efficient embedded implementations. The proposed solution has proved less memory, FLOPs, and latency compared to the state of art (including tensorial SVM), applied to real tactile sensors data. Keywords: E-skin, tactile data processing, deep learning, CNN, RNN, LSTM, GRU, embedded, energy-efficient algorithms, edge computing, artificial intelligence.

International audience; Sensory feedback from touch is critical for many tasks carried out by robots and humans, such as grasping objects or identifying materials. Electronic skin (e-skin) is a crucial technology for these purposes. Artificial tactile skin that can play the roles of human skin remains a distant possibility because of hard issues in resilience, manufacturing, mechanics , sensorics, electronics, energetics, information processing, and transport. Taken together, these issues make it difficult to bestow robots, or prosthetic devices, with effective tactile skins. Nonetheless, progress over the past few years in relation with the above issues has been encouraging, and we have achieved close to providing some of the abilities of biological skin with the advent of deformable sensors and flexible electronics. The naive imitation of skin morphology and sensing an impoverished set of mechanical and thermal quantities are not sufficient. There is a need to find more efficient ways to extract tactile information from mechanical contact than those previously available. Renewed interest in neuromorphic tactile skin is expected to bring some fresh ideas in this field. This article reviews these new developments, particularly related to the handling of tactile data, energy autonomy, and large-area manufacturing. The challenges in relation with these advances for tactile sensing and haptics in robotics and prosthetics are discussed along with potential solutions.

Flexible pressure sensors are attracting great interest from researchers and are widely applied in various new electronic equipment because of their distinct characteristics with high flexibility, high sensitivity, and light weight; examples include electronic skin (E-skin) and wearable flexible sensing devices. This review summarizes the research progress of flexible pressure sensors, including three kinds of transduction mechanisms and their respective research developments, and applications in the fields of E-skin and wearable devices. Furthermore, the challenges and development trends of E-skin and wearable flexible sensors are also briefly discussed. Challenges of developing high extensibility, high sensitivity, and flexible multi-function equipment still exist at present. Exploring new sensing mechanisms, seeking new functional materials, and developing novel integration technology of flexible devices will be the key directions in the sensors field in future.

Due to its very peculiar features, the development of e-skin can be effectively tackled using a holistic approach. Starting from the definition of system specifications, the mechanical arrangement of the skin itself needs to be designed and fabricated together with the electronic embedded system, to move towards aspects such as tactile data processing algorithms and the communication channel interface. In this paper we present the design, the implementation and the results on the way of the development of an electronic skin (e-skin) system based on arrays of piezopolymer transducers. Focus of the paper is on both the development of innovative approaches for tactile information processing and electronic system embedding into the e-skin structure. In particular, Machine Learning technologies can provide a powerful tool to tackle the pattern-recognition problems involved in the tactile sensing framework and the ability of processing data represented as N-th order tensor is the key aspect of the presented research, which can be seen as an application of an existing method (Signoretto et al., 2011). The experimental session compares two different implementations of the ML-based framework, which differ in the learning paradigm adopted, namely SVM and ELM (K-ELM). The effectiveness of the adopted pattern-recognition technologies in the classification of touch modalities has been confirmed by addressing two different binary classification problems in an experiment involving 70 participants. The computational requirements for the hardware implementation of the proposed algorithm together with an overview of what exists in the existing literature are finally discussed.

Das größte Organ des Menschen, die Haut, besitzt eine außergewöhnliche Bandbreite an Eigenschaften. Einerseits ist sie so empfindlich, dass sie einen leichten Lufthauch oder verschiedenste Oberflächen erkennen kann, andererseits ist sie so widerstandsfähig, dass sie den Körper vor Schäden oder Erregern schützt. Hinzu kommen weitere Aufgaben wie das Messen der aufgebrachten Kraft beim Fassen von Gegenständen oder die Überwachung der Körperbewegungen. Anhand dieses biologischen Vorbilds wird nun vermehrt an Haut-inspirierten Oberflächen mit integrierter Elektronik geforscht, die solche, aber auch weitere Eigenschaften aufweisen können (z. B. chemische Sensoren). Dabei sind die Einsatzbereiche für Electronic Skin, auch E-Skin genannt, mit engmaschigen Sensornetzwerken relativ vielfältig, insbesondere in den Bereichen Medizin und Robotik. In dem vorliegenden Artikel wird ein allgemeiner Überblick über die Technologie gegeben und zukünftige Entwicklungen abgeschätzt.