Soft self-healing resistive-based sensors inspired by sensory transduction in biological systems

Georgopoulou, Antonia; Brancart, Joost; Terryn, Seppe; Bosman, Anton W.; Norvez, Sophie; Assche, Guy Van; Iida, Fumiya; Vanderborght, Bram; Clemens, Frank

doi:10.1016/j.apmt.2022.101638

Cited by 11 publications

(12 citation statements)

References 301 publications

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“…The sensitivity factor is typically reported to have values between 0.5 and 3 for piezoresistive strain sensors. [ 11 ] The strain sensor with a sensitivity factor of 4 was slightly above the average of reported values.…”

Section: Resultsmentioning

confidence: 86%

A Prosthetic Hand with Integrated Sensing Elements for Selective Detection of Mechanical and Thermal Stimuli

Georgopoulou

Eckey

Clemens

2023

Advanced Intelligent Systems

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Flexible electronics have gained popularity because of their capability to combine softness and functionality. Soft resistive sensors are susceptible to mechanical stimuli and detecting temperature selectively remains a challenge. In this study, soft flexible thermistors are developed for detecting temperature changes based on the positive temperature coefficient (PTC) effect, selectively. By thermomechanical analysis and differential scanning calorimetry, it is observed that thermoplastic elastomers with higher thermal expansion and semicrystalline morphology result in a sensitive thermistor response. To achieve a high sensitivity in temperature and low sensitivity in the detection of mechanical stimulus, a low‐carbon filler content is required. The opposite trend is seen for the piezoresistive sensors for mechanical strain detection. Both sensory material types are compatible with thermoplastic material extrusion‐based additive manufacturing. The method is used for the fabrication of the sensing elements and an open‐source prosthetic hand. The strain sensor detects the bending of the fingers and the temperature sensor detects the temperature when in contact with a heated surface, successfully. In addition, the temperature sensor is used as a tactile sensor to detect contact with a non‐heated surface. Combining selective multisensory capabilities will significantly affect the future development of sensorized prosthetic devices and wearable electronics.

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Section: Resultsmentioning

confidence: 86%

A Prosthetic Hand with Integrated Sensing Elements for Selective Detection of Mechanical and Thermal Stimuli

Georgopoulou

Eckey

Clemens

2023

Advanced Intelligent Systems

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“…Selective sensors exhibit high sensitivity to one stimulus and minimal response to other interfering stimuli. [ 50 ] The thermoreceptive elements 2t and 6t produced relative signal changes of 4% and 1.8%, respectively. This response was significantly smaller than the one seen during the deep pressure test.…”

Section: Resultsmentioning

confidence: 99%

Sensorized Skin With Biomimetic Tactility Features Based on Artificial Cross‐Talk of Bimodal Resistive Sensory Inputs

Georgopoulou,

Hardman,

Thuruthel

et al. 2023

Advanced Science

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Tactility in biological organisms is a faculty that relies on a variety of specialized receptors. The bimodal sensorized skin, featured in this study, combines soft resistive composites that attribute the skin with mechano‐ and thermoreceptive capabilities. Mimicking the position of the different natural receptors in different depths of the skin layers, a multi‐layer arrangement of the soft resistive composites is achieved. However, the magnitude of the signal response and the localization ability of the stimulus change with lighter presses of the bimodal skin. Hence, a learning‐based approach is employed that can help achieve predictions about the stimulus using 4500 probes. Similar to the cognitive functions in the human brain, the cross‐talk of sensory information between the two types of sensory information allows the learning architecture to make more accurate predictions of localization, depth, and temperature of the stimulus contiguously. Localization accuracies of 1.8 mm, depth errors of 0.22 mm, and temperature errors of 8.2 °C using 8 mechanoreceptive and 8 thermoreceptive sensing elements are achieved for the smaller inter‐element distances. Combining the bimodal sensing multilayer skins with the neural network learning approach brings the artificial tactile interface one step closer to imitating the sensory capabilities of biological skin.

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“…The development of e-skin comprising various flexible sensors such as strain, pressure, shear force, and temperature sensors, integrated with sophisticated circuits, has emerged to mimic the sensing functionalities of human skin [ 128 ]. These tactile stimuli are typically converted into electrical signals, such as changes in resistance or capacitance, which can be collected and processed by computers [ 32 ]. Tactile sensors are primarily classified into strain, pressure, and shear force sensors, depending on the targeted stimuli.…”

Section: Integrated Actuation and Sensingmentioning

confidence: 99%

“…Pressure-driven [ 28 ], electrically driven [ 29 ], and specialized material like shape memory materials (SMMs) driven strategies [ 30 ] endow soft robots with the potential for complex movements such as rotation, rolling, crawling, and bending [ 31 ]. Simultaneously, sensing strategies based on electrical [ 32 ], magnetic [ 33 , 34 ], and specialized material responses [ 35 ] confer upon soft robots self-perceptive and adaptive characteristics, enabling them to explore the external environment and perceive changes in their form [ 36 , 37 ]. These elements are indispensable for the realization of intelligent soft robots.…”

Section: Introductionmentioning

confidence: 99%

Integrated Actuation and Sensing: Toward Intelligent Soft Robots

Zhou,

Li,

Wang

et al. 2024

Cyborg Bionic Syst

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Soft robotics has received substantial attention due to its remarkable deformability, making it well-suited for a wide range of applications in complex environments, such as medicine, rescue operations, and exploration. Within this domain, the interaction of actuation and sensing is of utmost importance for controlling the movements and functions of soft robots. Nonetheless, current research predominantly focuses on isolated actuation and sensing capabilities, often neglecting the critical integration of these 2 domains to achieve intelligent functionality. In this review, we present a comprehensive survey of fundamental actuation strategies and multimodal actuation while also delving into advancements in proprioceptive and haptic sensing and their fusion. We emphasize the importance of integrating actuation and sensing in soft robotics, presenting 3 integration methodologies, namely, sensor surface integration, sensor internal integration, and closed-loop system integration based on sensor feedback. Furthermore, we highlight the challenges in the field and suggest compelling directions for future research. Through this comprehensive synthesis, we aim to stimulate further curiosity among researchers and contribute to the development of genuinely intelligent soft robots.

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Soft self-healing resistive-based sensors inspired by sensory transduction in biological systems

Cited by 11 publications

References 301 publications

A Prosthetic Hand with Integrated Sensing Elements for Selective Detection of Mechanical and Thermal Stimuli

A Prosthetic Hand with Integrated Sensing Elements for Selective Detection of Mechanical and Thermal Stimuli

Sensorized Skin With Biomimetic Tactility Features Based on Artificial Cross‐Talk of Bimodal Resistive Sensory Inputs

Integrated Actuation and Sensing: Toward Intelligent Soft Robots

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