Deep Learning Assisted Body Area Triboelectric Hydrogel Sensor Network for Infant Care

Guo, Rui; Fang, Yunsheng; Wang, Nengchao; Libanori, Alberto; Xiao, Xiao; Wan, Dong; Cui, Xiaojing; Sang, Shengbo; Zhang, Wendong; Zhang, Hulin; Chen, Jun

doi:10.1002/adfm.202204803

Cited by 73 publications

(37 citation statements)

References 41 publications

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“…Renewable energy harvesting from the living environment has gained increased attention due to the increasing carbon emission from conventional fossil energy in the past decade. − Self-powered techniques such as piezoelectric, pyroelectric, triboelectric, thermoelectric, and electromagnetic effects are applied to harvest energy in the ambient environment to power small electronic devices, which is of crucial importance for long-term energy needs and sustainable development. Designs of both single piezoelectric and pyroelectric nanogenerators (NGs) have been used to drive microelectronic devices by harvesting environmental energy.…”

Section: Introductionmentioning

confidence: 99%

Flexible Piezoelectric and Pyroelectric Nanogenerators Based on PAN/TMAB Nanocomposite Fiber Mats for Self-Power Multifunctional Sensors

et al. 2022

ACS Appl. Mater. Interfaces

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Self-powered wearable electronics to convert mechanical and thermal energy into electrical energy are important for biomedical monitoring, which highly require good flexibility, comfortability, signal sensitivity, and accuracy. In this work, composite nanofiber mats of polyacrylonitrile (PAN) and trimethylamine borane (TMAB) were prepared by electrospinning, which exhibited excellent piezoelectric and pyroelectric abilities in harvesting mechanical and thermal energy. The PAN/TMAB-4 nanofiber mats not only generated a high voltage of up to 2.56 V and a high power of 0.19 μW upon shape deformation but also exhibited linear voltage response to thermal gradient. The hybrid piezoelectric and pyroelectric output signals were successfully integrated together and have been applied to precisely monitor human vital signs, including elbow bending angles, foot posture, and breathing status, in real time by attaching the flexible sensors to proper human body parts. Overall, good flexibility, bifunctional sensing ability, and self-power make PAN-/TMAB-type sensors very attractive in fabricating high-performance electronics for detecting motion, monitoring health, and making portable microelectronics.

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Section: Introductionmentioning

confidence: 99%

Flexible Piezoelectric and Pyroelectric Nanogenerators Based on PAN/TMAB Nanocomposite Fiber Mats for Self-Power Multifunctional Sensors

et al. 2022

ACS Appl. Mater. Interfaces

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“…The working mechanism of TENGs is based on the conjunction of contact and electrostatic induction. The TENG can effectively extract energy in the frequency range of <5 Hz due to its distinct mechanism [18][19][20][21][22], which makes up for the shortcomings of EMG in obtaining low-frequency mechanical energy and provides a new way to effectively collect ocean energy. The TENG can be dominated by the displacement current derived from the Maxwell equation [23,24].…”

Section: Introductionmentioning

confidence: 99%

Triboelectric Nanogenerators for Harvesting Diverse Water Kinetic Energy

Cui

Wang

et al. 2022

Micromachines

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The water covering the Earth’s surface not only supports life but also contains a tremendous amount of energy. Water energy is the most important and widely used renewable energy source in the environment, and the ability to extract the mechanical energy of water is of particular interest since moving water is ubiquitous and abundant, from flowing rivers to falling rain drops. In recent years, triboelectric nanogenerators (TENGs) have been promising for applications in harvesting kinetic energy from water due to their merits of low cost, light weight, simple structure, and abundant choice of materials. Furthermore, TENGs can also be utilized as self-powered active sensors for monitoring water environments, which relies on the output signals of the TENGs caused by the movement and composition of water. Here, TENGs targeting the harvest of different water energy sources have been systematically summarized and analyzed. The TENGs for harvesting different forms of water energy are introduced and divided on the basis of their basic working principles and modes, i.e., in the cases of solid–solid and solid–liquid. A detailed review of recent important progress in TENG-based water energy harvesting is presented. At last, based on recent progresses, the existing challenges and future prospects for TENG-based water energy harvesting are also discussed.

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“…[21,23] By constructing a neural network, the deep learning method is able to learn rules from signals and automatically nurture its classification capability, and further predict the feature of new signals for improving accuracy. [22,25,26] Recently, numerous recognition systems have been demonstrated to achieve a higher level of accuracy by combining the deep learning method, such as healthcare monitoring, [27][28][29] tactile sensing, [30][31][32][33][34] wearable gesture recognition, [35][36][37] etc. For example, Fang et al developed a wearable respiratory monitoring system with the assistance of deep learning, which realized the recognition accuracy up to 100%.…”

mentioning

confidence: 99%

Deep‐Learning‐Assisted Noncontact Gesture‐Recognition System for Touchless Human‐Machine Interfaces

Zhou

Huang

Xiao

et al. 2022

Adv Funct Materials

118

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Human‐machine interfaces (HMIs) play important role in the communication between humans and robots. Touchless HMIs with high hand dexterity and hygiene hold great promise in medical applications, especially during the pandemic of coronavirus disease 2019 (COVID‐19) to reduce the spread of virus. However, current touchless HMIs are mainly restricted by limited types of gesture recognition, the requirement of wearing accessories, complex sensing platforms, light conditions, and low recognition accuracy, obstructing their practical applications. Here, an intelligent noncontact gesture‐recognition system is presented through the integration of a triboelectric touchless sensor (TTS) and deep learning technology. Combined with a deep‐learning‐based multilayer perceptron neural network, the TTS can recognize 16 different types of gestures with a high average accuracy of 96.5%. The intelligent noncontact gesture‐recognition system is further applied to control a robot for collecting throat swabs in a noncontact mode. Compared with present touchless HMIs, the proposed system can recognize diverse complex gestures by utilizing charges naturally carried on human fingers without the need of wearing accessories, complicated device structures, adequate light conditions, and achieves high recognition accuracy. This system could provide exciting opportunities to develop a new generation of touchless medical equipment, as well as touchless public facilities, smart robots, virtual reality, metaverse, etc.

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Deep Learning Assisted Body Area Triboelectric Hydrogel Sensor Network for Infant Care

Cited by 73 publications

References 41 publications

Flexible Piezoelectric and Pyroelectric Nanogenerators Based on PAN/TMAB Nanocomposite Fiber Mats for Self-Power Multifunctional Sensors

Flexible Piezoelectric and Pyroelectric Nanogenerators Based on PAN/TMAB Nanocomposite Fiber Mats for Self-Power Multifunctional Sensors

Triboelectric Nanogenerators for Harvesting Diverse Water Kinetic Energy

Deep‐Learning‐Assisted Noncontact Gesture‐Recognition System for Touchless Human‐Machine Interfaces

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