2022
DOI: 10.1016/j.nanoen.2021.106798
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A flexible triboelectric tactile sensor for simultaneous material and texture recognition

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Cited by 96 publications
(42 citation statements)
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“…Integrating wearable devices with appropriate machine learning algorithms significantly improves the wearable sensing system in the application of biomedical diagnosis 287,288 and human-machine interaction applications. 282…”
Section: Stretchable Sensing Assisted By Machine Learningmentioning
confidence: 99%
See 1 more Smart Citation
“…Integrating wearable devices with appropriate machine learning algorithms significantly improves the wearable sensing system in the application of biomedical diagnosis 287,288 and human-machine interaction applications. 282…”
Section: Stretchable Sensing Assisted By Machine Learningmentioning
confidence: 99%
“…A 1D-CNN (Convolutional Neural Networks)-based recognition model enables the flexible sensing system with tactile sensation to recognize materials and textures, which empowers robots with multi-functional perceptions. 282 Luo et al reported a soft sensor array with high spatiotemporal resolution and multimodal sensing abilities assisted by machine learning for intelligent monitoring and stimuli response. 283 Moin et al developed a wearable surface EMG biosensing system for hand gesture recognition.…”
Section: Stretchable Sensing Assisted By Machine Learningmentioning
confidence: 99%
“…237 Sensors can serve as interfaces for the front-end of HCI systems to collect signals transmitted by human stimuli such as touch, sound, electromyographic activity, and nerve impulses, and then convert these signals into the required data inputs for the back-end. The applications of humanmachine interfaces mainly include smart keyboards, 6,238 touch screens, 211,239,240 intelligent robots, [241][242][243][244] and speech recognition systems. 245 For example, a highly sensitive paper-based piezoresistive PS was fabricated and a biodegradable and foldable keyboard was developed by Sakhuja et al (Fig.…”
Section: Human-machine Interfaces (Hmi)mentioning
confidence: 99%
“…To our best knowledge, the MMCA-based e-skin exhibits the superiorities that can not only quantitatively recognize the pressure inputs and 3D morphology in a selfpowered way but also be available to provide stable electrical responses in some harsh conditions as discussed above. [14][15][16][37][38][39][40][41][42]…”
Section: Reconstruction Of 3d Morphology Via Mmca E-skinmentioning
confidence: 99%