2023
DOI: 10.1038/s41565-023-01383-6
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Hierarchically resistive skins as specific and multimetric on-throat wearable biosensors

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Cited by 73 publications
(31 citation statements)
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“…A proper encapsulation is necessary to improve the durability. 160 To monitor the complex movements of the human body, ultrasensitive 515 and specific 516 strain sensors based on nanocracks were also proposed, and it is possible to distinguish multiple types of movements with the help of machine learning.…”
Section: Strain Sensorsmentioning
confidence: 99%
See 1 more Smart Citation
“…A proper encapsulation is necessary to improve the durability. 160 To monitor the complex movements of the human body, ultrasensitive 515 and specific 516 strain sensors based on nanocracks were also proposed, and it is possible to distinguish multiple types of movements with the help of machine learning.…”
Section: Strain Sensorsmentioning
confidence: 99%
“…675 Machine learning algorithms offer promising solutions to extract valuable insights from these signals, enabling accurate recognition, classification, and decision making. A number of biophysical and physiological signals, including ECG, 676 heart rate, 677 blood pressure, 260 hand gestures, 177 speech, 519 body motion, 516 and more, have been powered with machine learning techniques for automated analysis, recognition, and classification purposes. ECG signals are widely used for cardiac monitoring and analysis.…”
Section: Machine Learning and Algorithm Developmentmentioning
confidence: 99%
“…The plasticity of these synapses, meaning their ability to change their strength and connectivity over time, is crucial for learning and memory. − Neuromorphic electronic systems, proposed by Carver Mead in the late 1980s to early 1990s, aim to design electronic systems that mimic the structure, function, and plasticity of biological neural networks (Figure ). While neuromorphic circuits based on silicon complementary metal-oxide-semiconductor (CMOS) technology have been developed to replicate synaptic functionalities, classical computing systems traditionally relied on the von Neumann computing architecture and suffered from limitations due to the separation of memory from processing, leading to issues such as speed latency, high energy consumption, and limited communication bandwidth. ,,, To overcome these drawbacks, researchers have developed novel artificial synapses based on a variety of materials and structures, typically implemented with 2-terminal memristors or 3-terminal transistors. ,,, These devices are capable of achieving neuromorphic functions, such as short-term and long-term plasticity (STP and LTP), similar to the synapses in biological systems. ,, In recent years, there has been growing interest in using flexible electronics for the development of artificial neuron devices. ,, Flexible electronics refer to electronic devices and systems that can bend, stretch, and conform to their surroundings without breaking or losing their functionality. ,− Flexible electronics possess mechanical properties similar to human organs and tissues, showing great advantages for the development of artificial neuron devices. ,,− They can be easily integrated with biological tissues and structures, allowing for seamless interaction with the nervous system and the development of biointerfaces and biohybrid systems. − …”
Section: Introductionmentioning
confidence: 99%
“…With the rapid development of artificial intelligence (AI) and the Internet of things (IoT) on a global scale, wearable electronics have significantly changed our daily life and extensively applied in biomimetic skin, , human-computer interaction, healthcare management, soft robotics, and medical diagnosis . One of the paramount challenges in the development of next-generation wearable devices is to fabricate advanced flexible sensors capable of transforming weak or imperceptible external stimuli into easily recognizable electronic signals .…”
Section: Introductionmentioning
confidence: 99%