Graphene‐based dual‐function acoustic transducers for machine learning‐assisted human–robot interfaces

Sun, Hao; Gao, Xin; Guo, Liang‐Yan; Tao, Lu‐Qi; Guo, Zi Hao; Shao, Yangshi; Cui, Tianrui; Yang, Yi; Pu, Xiong; Ren, Tian‐Ling

doi:10.1002/inf2.12385

Cited by 23 publications

(27 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…7f ). 111 Artificial ear is implemented by the triboelectric acoustic sensing mechanism where the artificial mouth uses the thermoacoustic sound emission mechanism. And both of them used the key multifunctional laser-induced graphene for different purposes.…”

Section: Voice Characteristicsmentioning

confidence: 99%

Triboelectric nanogenerators as self-powered sensors for biometric authentication

et al. 2023

View full text Add to dashboard Cite

show abstract

Section: Voice Characteristicsmentioning

confidence: 99%

Triboelectric nanogenerators as self-powered sensors for biometric authentication

et al. 2023

View full text Add to dashboard Cite

show abstract

“…In recent years, TENG-based self-powered acoustic sensors have attracted much attention. As shown in Figure 7d, Sun et al [325] proposed a human-robot interface using a dual-function acoustic transducer. The acoustic transducer is manufactured based on TENG and used to iden-tify identity information, emotional state, and specific content in speech.…”

Section: Self-powered Wearable Devices: Above Shouldermentioning

confidence: 99%

Recent Progress in Application‐Oriented Self‐Powered Microelectronics

Qi,

Kong,

Wang

et al. 2023

Advanced Energy Materials

View full text Add to dashboard Cite

With the rapid development of the Internet of Things (IoTs), numerous distributed wide‐area low‐power electronic devices have been utilized in various fields, such as wireless monitoring sensors and wearable electronics. Due to the dispersion and mobility of microelectronic devices, their energy supply faces serious challenges. The inconvenience and non‐environmental friendliness of using traditional centralized low entropy energy and chemical batteries to power distributed microelectronic devices are becoming increasingly prominent. Environmental energy harvesting technology with high entropy characteristics is considered an effective solution for low‐power electronic devices. This paper comprehensively reviews the recent progress in microelectronic technologies based on energy harvesting and signal sensing. First, state‐of‐the‐art micro‐power electronic devices in humans, animals, and the environment are introduced. Secondly, the available micro‐energy sources in the environmentare elaborated and summarized. Then, the principles and characteristics of ambient microenergy harvesting technologies based on different mechanisms are classified, summarized, and analyzed. In addition, this work comprehensively summarizes the applications of self‐powered micro‐electronics technology in 11 different fields, including human, animal, and environment. Finally, research challenges, technical difficulties, and research gaps in self‐powered microelectronics based on micro‐energy harvesting technology are discussed and summarized.

show abstract

“…[42,43] Among many sensing mechanisms, triboelectric sensing is an excellent technology due to its low energy consumption, high sensitivity, and strong adaptability. [44,45] Furthermore, in low-frequency environments (human motion, [46] seawater fluctuations, [47] etc. ), triboelectric sensor exhibits significant advantages such as higher output voltage and higher power density.…”

Section: Introductionmentioning

confidence: 99%

A Self‐Powered and Self‐Sensing Lower‐Limb System for Smart Healthcare

Kong

Fang

Zhang

et al. 2023

Advanced Energy Materials

View full text Add to dashboard Cite

In the age of the artificial intelligence of things (AIoT), wearable devices have been extensively developed for smart healthcare. This paper proposes a self‐powered and self‐sensing lower‐limb system (SS‐LS) with negative energy harvesting and motion capture for smart healthcare. The SS‐LS achieves self‐sustainability via a half‐wave electromagnetic generator (HW‐EMG) that recovers negative work from walking with a low cost of harvesting. Additionally, the motion capture function of the system is achieved by the three‐channel triboelectric nanogenerator (TC‐TENG) based on binary code, which can accurately detect the angle and direction of the knee joint rotation. The bench test experiment indicates that the HW‐EMG has an average output power of 11.2 mW, sufficient to power a wireless sensor. The three‐channel voltage signal of TC‐TENG fits well with the binary signal, which can precisely detect the angle and direction of rotation. Furthermore, the SS‐LS demonstrates an identification accuracy of 99.68% and a motion detection accuracy of 99.96% based on an LSTM deep learning model. Demonstrations of Parkinson's disease and fall detection and monitoring of three training modes (sit‐and‐stand, balance, and walking training) are also performed, which exhibit outstanding sensing capabilities. The SS‐LS is highly promising in sports rehabilitation medicine and can contribute to the development of smart healthcare.

show abstract

Graphene‐based dual‐function acoustic transducers for machine learning‐assisted human–robot interfaces

Cited by 23 publications

References 68 publications

Triboelectric nanogenerators as self-powered sensors for biometric authentication

Triboelectric nanogenerators as self-powered sensors for biometric authentication

Recent Progress in Application‐Oriented Self‐Powered Microelectronics

A Self‐Powered and Self‐Sensing Lower‐Limb System for Smart Healthcare

Contact Info

Product

Resources

About