PeriSense: Ring-Based Multi-Finger Gesture Interaction Utilizing Capacitive Proximity Sensing

Wilhelm, Mathias; Krakowczyk, Daniel; Albayrak, Şahin

doi:10.3390/s20143990

Cited by 16 publications

(4 citation statements)

References 71 publications

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“…Thus, the proposed model needs to be developed for online computation so it can be implemented into smartphones to Journal of Sensors use a sign language alphabet via Bluetooth. In addition, our system could be developed as a smart ring wearable system connected wirelessly to a smartphone, which would be convenient for use in daily life [30,31]. Future studies should also consider using the proposed system via the Internet of things.…”

Section: Discussionmentioning

confidence: 99%

Classification of the Korean Sign Language Alphabet Using an Accelerometer with a Support Vector Machine

Yang

Woo

2021

Journal of Sensors

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Recognition and understanding of sign language can aid communication between nondeaf and deaf people. Recently, research groups have developed sign language recognition algorithms using multiple sensors. However, in everyday life, minimizing the number of sensors would still require the use of a sign language interpreter. In this study, a sign language classification method was developed using an accelerometer to recognize the Korean sign language alphabet. The accelerometer is worn on the proximal phalanx of the index finger of the dominant hand. Triaxial accelerometer signals were used to segment the sign gesture (i.e., the time period when a user is performing a sign) and recognize the 31 Korean sign language letters (producing a chance level of 3.2%). The vector sum of the accelerometer signals was used to segment the sign gesture with 98.9% segmentation accuracy, which is comparable to that of previous multisensor systems (99.49%). The system was able to classify the Korean sign language alphabet with 92.2% accuracy. The recognition accuracy of this approach was found to be higher than that of a previous work in the same sign language alphabet classification task. The findings demonstrate that a single-sensor accelerometer with simple features can be reliably used for Korean sign language alphabet recognition in everyday life.

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Section: Discussionmentioning

confidence: 99%

Classification of the Korean Sign Language Alphabet Using an Accelerometer with a Support Vector Machine

Yang

Woo

2021

Journal of Sensors

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“…5 shows the power transfer coefficients τ (f ), which is the fraction of the RF power that is harvested by the antenna and then transferred to the IC. The power transfer coefficient is computed by accounting for the auto-tuning according to (2) as in [16], This is the author's version of an article that has been published in IEEE Journal of Radio Frequency Identification. Changes were made to this version by the publisher prior to publication.…”

Section: A Impedance Matchingmentioning

confidence: 99%

“…Finger augmentation devices (FADs) are finger-worn electronic tools conceived to provide supplemental capabilities through an additional augmentation other than their form [1]. FADs can have different form factors, spanning from proximalphalanx rings [2] to nail-pads [3], [4], and several possible applications, like driving vehicles [5], [6] or controlling aerial drones [7]. The most ambitious goal of FADs is the achievement of a sensorial ultrability, namely providing the user with an artificial sensation to either restore a lost or degraded natural ability or even to add a new sense, going beyond the natural physical feelings of humans [8].…”

Section: Introductionmentioning

confidence: 99%

Sensing Performance of Multi-Channel RFID-Based Finger Augmentation Devices for Tactile Internet

Naccarata

Bianco

Marrocco

2022

IEEE J. Radio Freq. Identif.

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Radiofrequency finger augmentation devices (R-FADs) are a recently introduced class of epidermal radiofrequency identification (RFID) sensor-tags attached to the fingers, communicating with a body-worn reader. These devices are promising candidates to enable Tactile Internet (TI) applications in the short term. R-FAD based on auto-tuning RFID microchips can be used as dielectric probes for the material of touched objects. However, due to the nearly unpredictable intrinsic variability of finger-object interaction, a single sensorized finger (single-channel device) is not enough to guarantee reliable data sampling. These limitations can be overcome by exploiting a multi-channel R-FAD sensorizing multiple fingers of the hand. In this paper, the dielectric-sensing performance of a multi-channel R-FAD, composed of sensors encapsulated into soft elastomers, is numerically and experimentally characterized, involving a set of volunteers. The inter-sensor coupling is negligible, thus enabling simultaneous independent dielectric measurements. The multi-sensor configuration allows for 100% reliability of the onhand communication link for touched objects in a wide range of permittivity. Experiments moreover demonstrate that multichannel measurements can halve the measurement uncertainty of the single-channel case. The achievable precision is suitable to discriminate among low-, medium-, and high-permittivity materials.

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“…However, their recognition and perception are still based on the transmission of wires. There are still certain difficulties in popularization and use of wearable devices [8,9]. For a more convenient and natural user experience, the gesture recognition based on the machine vision has developed rapidly.…”

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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PeriSense: Ring-Based Multi-Finger Gesture Interaction Utilizing Capacitive Proximity Sensing

Cited by 16 publications

References 71 publications

Classification of the Korean Sign Language Alphabet Using an Accelerometer with a Support Vector Machine

Classification of the Korean Sign Language Alphabet Using an Accelerometer with a Support Vector Machine

Sensing Performance of Multi-Channel RFID-Based Finger Augmentation Devices for Tactile Internet

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

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