3D motion tracking display enabled by magneto-interactive electroluminescence

Lee, Seungwon; Baek, So-Yeon; Park, Sung Won; Koo, Min Ah; Kim, Eui Hyuk; Lee, Seokyeong; Jin, Wookyeong; Kang, Hansol; Park, Chanho; Kim, Gwangmook; Shin, Hyeong Cheol; Shim, Wooyoung; Yang, Sunggu; Ahn, Juhyeon

doi:10.1038/s41467-020-19523-0

Cited by 37 publications

(16 citation statements)

References 35 publications

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“…They designed a hydrogen bond cross‐linked network based on carboxylic styrene butadiene rubber (XSBR) and hydrophilic sericin (SS) noncovalently modified CNTs, and then fabricated it into multifunctional sensors to achieve high sensitivity (25.98), high stretchability (217%), and low detection limit (1%) (Figure 3D). Apart from pressure sensors 80,81 and strain sensors, 82,83 CNTs are widely used in various other devices, such as airflow sensors, 74,84 biosensors, 75,85 and magnetic sensors 86 . For example, Zhang et al 74 developed an ultrasensitive and flexible all‐textile airflow sensor based on carbonized silk fabric (CSF) with in situ grown CNTs.…”

Section: Flexible Sensormentioning

confidence: 99%

“…Apart from pressure sensors 80,81 and strain sensors, 82,83 CNTs are widely used in various other devices, such as airflow sensors, 74,84 biosensors, 75,85 and magnetic sensors. 86 For example, Zhang et al 74 developed an ultrasensitive and flexible all-textile airflow sensor based on carbonized silk fabric (CSF) with in situ grown CNTs. The fabric decorated with fluffy-like CNTs possesses an exceptionally large contact area (Figure 3E) endows the airflow sensor with superior properties, including ultralow detection limit ($0.05 m s À1 ), multiangle airflow differential response (0-90 ), and fast response time ($1.3 s).…”

Section: D Nanomaterialsmentioning

confidence: 99%

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Advances in flexible sensors for intelligent perception system enhanced by artificial intelligence

Niu

Yin

Kim

et al. 2023

InfoMat

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Intelligent perception means that with the assistance of artificial intelligence (AI)‐motivated brain, flexible sensors achieve the ability of memory, learning, judgment, and reasoning about external information like the human brain. Due to the superiority of machine learning (ML) algorithms in data processing and intelligent recognition, intelligent perception systems possess the ability to match or even surpass human perception systems. However, the built‐in flexible sensors in these systems need to work on dynamic and irregular surfaces, inevitably affecting the precision and fidelity of the acquired data. In recent years, the strategy of introducing the developed functional materials and innovative structures into flexible sensors has made some progress toward the above challenges, and with the blessing of ML algorithms, accurate perception and reasoning in various scenarios have been achieved. Here, the most representative functional materials and innovative structures for constructing flexible sensors are comprehensively reviewed, the research progress of intelligent perception systems based on flexible sensors and ML algorithms is further summarized, and the intersection of the two is expected to unlock new opportunities for next‐stage AI development.

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Section: Flexible Sensormentioning

confidence: 99%

Section: D Nanomaterialsmentioning

confidence: 99%

Advances in flexible sensors for intelligent perception system enhanced by artificial intelligence

Niu

Yin

Kim

et al. 2023

InfoMat

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“…An earlier version of this manuscript was preprinted in the arXiv [38], and several students participated to the earlier version.…”

Section: Data Availabilitymentioning

confidence: 99%

A New Approach to Estimate Concentration Levels with Filtered Neural Nets for Online Learning

Lee

Shim

2022

Complexity

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The COVID-19 pandemic heavily influenced human life by constricting human social activity. Following the spread of the pandemic, humans did not have a choice but to change their lifestyles. There has been much change in the field of education, which has led to schools hosting online classes as an alternative to face-to-face classes. However, the concentration level is lowered in the online learning class, and the student’s learning rate decreases. We devise a framework for recognizing and estimating students’ concentration levels to help lecturers. Previous studies have a limitation in that they classified attention levels using only discrete states. Due to the partial information from discrete states, the concentration levels could not be recognized well. This research aims to estimate more subtle levels as specified states by using a minimum amount of body movement data. The deep neural network is used to continuously recognize the human concentration model, and the concentration levels can be predicted and estimated by the Kalman filter. Using our framework, we successfully extracted the concentration levels, which can aid lecturers and can be expanded to other areas. To implement the framework, we recruited participants to take online classes. Data were collected and preprocessed using pose points, and an accuracy of 90.62 % was calculated by predicting the concentration level using the framework. Furthermore, the concentration level was approximated based on the Kalman filter. We found that webcams can be used to quantitatively measure student concentration when conducting online classes. Our framework is a great help for instructors to measure concentration levels, which can increase the learning efficiency. As a future work of this study, if emotion data and skin thermal data are comprehensively considered, a student’s concentration level can be measured more precisely.

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“…Song et al designed a virtual reality interactive glove by using an actuator, which is very light and easy for users to wear [11]. Lee et al presented a skin-patchable magneto-interactive electroluminescent display, which is capable of sensing, visualizing, and storing magnetic field information to realize the 3D motion tracking [12]. Mantecón et al proposed a hand gesture recognition system by using near-infrared imagery.…”

Section: Introductionmentioning

confidence: 99%

High Inclusiveness and Accuracy Motion Blur Real-Time Gesture Recognition Based on YOLOv4 Model Combined Attention Mechanism and DeblurGanv2

et al. 2021

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The combination of gesture recognition and aerospace exploration robots can realize the efficient non-contact control of the robots. In the harsh aerospace environment, the captured gesture images are usually blurred and damaged inevitably. The motion blurred images not only cause part of the transmitted information to be lost, but also affect the effect of neural network training in the later stage. To improve the speed and accuracy of motion blurred gestures recognition, the algorithm of YOLOv4 (You Only Look Once, vision 4) is studied from the two aspects of motion blurred image processing and model optimization. The DeblurGanv2 is employed to remove the motion blur of the gestures in YOLOv4 network input pictures. In terms of model structure, the K-means++ algorithm is used to cluster the priori boxes for obtaining the more appropriate size parameters of the priori boxes. The CBAM attention mechanism and SPP (spatial pyramid pooling layer) structure are added to YOLOv4 model to improve the efficiency of network learning. The dataset for network training is designed for the human–computer interaction in the aerospace space. To reduce the redundant features of the captured images and enhance the effect of model training, the Wiener filter and bilateral filter are superimposed on the blurred images in the dataset to simply remove the motion blur. The augmentation of the model is executed by imitating different environments. A YOLOv4-gesture model is built, which collaborates with K-means++ algorithm, the CBAM and SPP mechanism. A DeblurGanv2 model is built to process the input images of the YOLOv4 target recognition. The YOLOv4-motion-blur-gesture model is composed of the YOLOv4-gesture and the DeblurGanv2. The augmented and enhanced gesture data set is used to simulate the model training. The experimental results demonstrate that the YOLOv4-motion-blur-gesture model has relatively better performance. The proposed model has the high inclusiveness and accuracy recognition effect in the real-time interaction of motion blur gestures, it improves the network training speed by 30%, the target detection accuracy by 10%, and the value of mAP by about 10%. The constructed YOLOv4-motion-blur-gesture model has a stable performance. It can not only meet the real-time human–computer interaction in aerospace space under real-time complex conditions, but also can be applied to other application environments under complex backgrounds requiring real-time detection.

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3D motion tracking display enabled by magneto-interactive electroluminescence

Cited by 37 publications

References 35 publications

Advances in flexible sensors for intelligent perception system enhanced by artificial intelligence

Advances in flexible sensors for intelligent perception system enhanced by artificial intelligence

A New Approach to Estimate Concentration Levels with Filtered Neural Nets for Online Learning

High Inclusiveness and Accuracy Motion Blur Real-Time Gesture Recognition Based on YOLOv4 Model Combined Attention Mechanism and DeblurGanv2

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