Sensorized Skin With Biomimetic Tactility Features Based on Artificial Cross‐Talk of Bimodal Resistive Sensory Inputs
Abstract 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 dif...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-10-01
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| Series: | Advanced Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/advs.202301590 |
| _version_ | 1797648659018416128 |
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| author | Antonia Georgopoulou David Hardman Thomas George Thuruthel Fumiya Iida Frank Clemens |
| author_facet | Antonia Georgopoulou David Hardman Thomas George Thuruthel Fumiya Iida Frank Clemens |
| author_sort | Antonia Georgopoulou |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-03-11T15:35:06Z |
| format | Article |
| id | doaj.art-6f0a97f39656403581ee025f2e6ee758 |
| institution | Directory Open Access Journal |
| issn | 2198-3844 |
| language | English |
| last_indexed | 2024-03-11T15:35:06Z |
| publishDate | 2023-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Science |
| spelling | doaj.art-6f0a97f39656403581ee025f2e6ee7582023-10-26T20:10:11ZengWileyAdvanced Science2198-38442023-10-011030n/an/a10.1002/advs.202301590Sensorized Skin With Biomimetic Tactility Features Based on Artificial Cross‐Talk of Bimodal Resistive Sensory InputsAntonia Georgopoulou0David Hardman1Thomas George Thuruthel2Fumiya Iida3Frank Clemens4Department of Functional Materials Empa ‐ Swiss Federal Laboratories for Materials Science and Technology 8600 SwitzerlandBio‐Inspired Robotics Lab Department of Engineering University of Cambridge CB2 1PZ UKBio‐Inspired Robotics Lab Department of Engineering University of Cambridge CB2 1PZ UKBio‐Inspired Robotics Lab Department of Engineering University of Cambridge CB2 1PZ UKDepartment of Functional Materials Empa ‐ Swiss Federal Laboratories for Materials Science and Technology 8600 SwitzerlandAbstract 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.https://doi.org/10.1002/advs.202301590deep learningmultimodal perceptionsoft sensors |
| spellingShingle | Antonia Georgopoulou David Hardman Thomas George Thuruthel Fumiya Iida Frank Clemens Sensorized Skin With Biomimetic Tactility Features Based on Artificial Cross‐Talk of Bimodal Resistive Sensory Inputs Advanced Science deep learning multimodal perception soft sensors |
| title | Sensorized Skin With Biomimetic Tactility Features Based on Artificial Cross‐Talk of Bimodal Resistive Sensory Inputs |
| title_full | Sensorized Skin With Biomimetic Tactility Features Based on Artificial Cross‐Talk of Bimodal Resistive Sensory Inputs |
| title_fullStr | Sensorized Skin With Biomimetic Tactility Features Based on Artificial Cross‐Talk of Bimodal Resistive Sensory Inputs |
| title_full_unstemmed | Sensorized Skin With Biomimetic Tactility Features Based on Artificial Cross‐Talk of Bimodal Resistive Sensory Inputs |
| title_short | Sensorized Skin With Biomimetic Tactility Features Based on Artificial Cross‐Talk of Bimodal Resistive Sensory Inputs |
| title_sort | sensorized skin with biomimetic tactility features based on artificial cross talk of bimodal resistive sensory inputs |
| topic | deep learning multimodal perception soft sensors |
| url | https://doi.org/10.1002/advs.202301590 |
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