Wearable Sensors‐Enabled Human–Machine Interaction Systems: From Design to Application

Yin, Ranyu; Wang, Depeng; Zhao, Shufang; Lou, Zheng; Shen, Guozhen

doi:10.1002/adfm.202008936

Cited by 382 publications

(233 citation statements)

References 233 publications

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“…The final result selects the middle position in the range as the prediction answer. In this study, BLEU-4 and ROUGE-L [ 25 , 26 ] are used to evaluate the final prediction efficiency of the model. BLEU-4 is evaluated by analysing the frequency of the same words between multiple sentences.…”

Section: Construction Of English Multitext Reading Comprehension Modelmentioning

confidence: 99%

Analysis of English Multitext Reading Comprehension Model Based on Deep Belief Neural Network

Tang

2021

Computational Intelligence and Neuroscience

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In order to solve the problems of low accuracy and low efficiency of answer prediction in machine reading comprehension, a multitext English reading comprehension model based on the deep belief neural network is proposed. Firstly, the paragraph selector in the multitext reading comprehension model is constructed. Secondly, the text reader is designed, and the deep belief neural network is introduced to predict the question answering probability. Finally, the popular English dataset of SQuAD is used for test analysis. The final results show that, after the comparative analysis of different learning methods, it is found that the English multitext reading comprehension model has a strong reading comprehension ability. In addition, two evaluation methods are used to score the overall performance of the model, which shows that the overall score of the English multitext reading comprehension model based on the deep confidence neural network is more than 90, and the efficiency will not be reduced because of the change of the number of documents in the dataset. The above results show that the use of the deep belief neural network to improve the probability generation performance of the model can well solve the task of English multitext reading comprehension, effectively reduce the difficulty of machine reading comprehension in multitask reading, and has a good guiding significance for promoting human convenient Internet knowledge acquisition.

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Section: Construction Of English Multitext Reading Comprehension Modelmentioning

confidence: 99%

Analysis of English Multitext Reading Comprehension Model Based on Deep Belief Neural Network

Tang

2021

Computational Intelligence and Neuroscience

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“…With the rapid development of the Internet of things (IoT) and 5G communication technology in recent years, human-machine interfaces (HMIs) have gradually evolved from traditional computer peripherals, e.g., keyboard, mouse, and joystick as illustrated in Figure 1, to a more intuitive interface that directly collects human's original signals [1][2][3], such as voice and basic body motions, providing users with a more intuitive and easier interaction with computers and intelligent robots in the applications of healthcare, rehabilitation, industrial automation, smart home, virtual reality (VR) game control, etc. [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Progress in the Triboelectric Human–Machine Interfaces (HMIs)-Moving from Smart Gloves to AI/Haptic Enabled HMI in the 5G/IoT Era

Sun

Zhu

Lee

2021

Nanoenergy Advances

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Entering the 5G and internet of things (IoT) era, human–machine interfaces (HMIs) capable of providing humans with more intuitive interaction with the digitalized world have experienced a flourishing development in the past few years. Although the advanced sensing techniques based on complementary metal-oxide-semiconductor (CMOS) or microelectromechanical system (MEMS) solutions, e.g., camera, microphone, inertial measurement unit (IMU), etc., and flexible solutions, e.g., stretchable conductor, optical fiber, etc., have been widely utilized as sensing components for wearable/non-wearable HMIs development, the relatively high-power consumption of these sensors remains a concern, especially for wearable/portable scenarios. Recent progress on triboelectric nanogenerator (TENG) self-powered sensors provides a new possibility for realizing low-power/self-sustainable HMIs by directly converting biomechanical energies into valuable sensory information. Leveraging the advantages of wide material choices and diversified structural design, TENGs have been successfully developed into various forms of HMIs, including glove, glasses, touchpad, exoskeleton, electronic skin, etc., for sundry applications, e.g., collaborative operation, personal healthcare, robot perception, smart home, etc. With the evolving artificial intelligence (AI) and haptic feedback technologies, more advanced HMIs could be realized towards intelligent and immersive human–machine interactions. Hence, in this review, we systematically introduce the current TENG HMIs in the aspects of different application scenarios, i.e., wearable, robot-related and smart home, and prospective future development enabled by the AI/haptic-feedback technology. Discussion on implementing self-sustainable/zero-power/passive HMIs in this 5G/IoT era and our perspectives are also provided.

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“…Human-machine interaction (HMI) has been considered as one of the mainstreams in the future with tremendous potentials in Internet of things (IoT), soft robotics, and virtual reality (VR) system, etc. [1][2][3]. The key component of an HMI or intelligent system is the communication interface that can swiftly transmit commands from humans to machines, or provide timely and precise feedback conversely [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Magnetized Micropillar-Enabled Wearable Sensors for Touchless and Intelligent Information Communication

Zhou

Luo

et al. 2021

Nano-Micro Lett.

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The wearable sensors have recently attracted considerable attentions as communication interfaces through the information perception, decoding, and conveying process. However, it is still challenging to obtain a sensor that can convert detectable signals into multiple outputs for convenient, efficient, cryptic, and high-capacity information transmission. Herein, we present a capacitive sensor of magnetic field based on a tilted flexible micromagnet array (t-FMA) as the proposed interaction interface. With the bidirectional bending capability of t-FMA actuated by magnetic torque, the sensor can recognize both the magnitude and orientation of magnetic field in real time with non-overlapping capacitance signals. The optimized sensor exhibits the high sensitivity of over 1.3 T−1 and detection limit down to 1 mT with excellent durability. As a proof of concept, the sensor has been successfully demonstrated for convenient, efficient, and programmable interaction systems, e.g., touchless Morse code and Braille communication. The distinguishable recognition of the magnetic field orientation and magnitude further enables the sensor unit as a high-capacity transmitter for cryptic information interaction (e.g., encoded ID recognition) and multi-control instruction outputting. We believe that the proposed magnetic field sensor can open up a potential avenue for future applications including information communication, virtual reality device, and interactive robotics.

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Wearable Sensors‐Enabled Human–Machine Interaction Systems: From Design to Application

Cited by 382 publications

References 233 publications

Analysis of English Multitext Reading Comprehension Model Based on Deep Belief Neural Network

Analysis of English Multitext Reading Comprehension Model Based on Deep Belief Neural Network

Progress in the Triboelectric Human–Machine Interfaces (HMIs)-Moving from Smart Gloves to AI/Haptic Enabled HMI in the 5G/IoT Era

Magnetized Micropillar-Enabled Wearable Sensors for Touchless and Intelligent Information Communication

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