Implementation of Hand Gesture Recognition Device Applicable to Smart Watch Based on Flexible Epidermal Tactile Sensor Array

Byun, Sung-Woo; Lee, Seok-Pil

doi:10.3390/mi10100692

Cited by 26 publications

(13 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To evaluate the energy efficiency of the proposed system, we evaluate the energy consumption per classification. For this purpose, we used the hardware metrics of the μBrain chip defined in [62] and mathematically given as E c = N spikes × E spikes + δT × P leakage (9) where E c is the energy consumed per classification, N spikes is the maximum number of spikes during classification, N spikes = 2.1 pJ is the energy per spike, P leakage = 73 μW is the static leakage power, and δT is the inference time. Assume that δT = 28 ms.…”

Section: Discussionmentioning

confidence: 99%

RadarSNN: A Resource Efficient Gesture Sensing System Based on mm-Wave Radar

Arsalan

Santra

Issakov

2022

IEEE Trans. Microwave Theory Techn.

View full text Add to dashboard Cite

Radar offers a promising modality for enabling gesture recognition, which is a simple and intuitive alternative to click and touch-based human-computer interface. In this article, we propose a spiking neural network (SNN)-based hand gesture recognition with frequency-modulated continuous-wave 60-GHz radar. As preprocessing, the 2-D fast Fourier transform (FFT) is performed across fast time and slow time to generate a video of range-Doppler maps, which are then processed to generate range spectrograms, Doppler spectrograms, and angle spectrograms.The spike trains are fed into an SNN to classify the gesture that has been performed. We demonstrate that with few neurons, SNNs can achieve recognition accuracies close to 99.50% comparable to their deep learning counterparts for eight dynamic gestures. Moreover, the proposed model size is 75 kB, which is substantially smaller compared to the state-of-the-art models making it memory efficient. We also demonstrate using tSNE plots that SNNs can operate with lower embedding dimensions, implying that we can realize SNN with a small compute and memory footprint.

show abstract

Section: Discussionmentioning

confidence: 99%

RadarSNN: A Resource Efficient Gesture Sensing System Based on mm-Wave Radar

Arsalan

Santra

Issakov

2022

IEEE Trans. Microwave Theory Techn.

View full text Add to dashboard Cite

show abstract

“…Hand gesture recognition can be accomplished using two main types of sensors namely contact sensors and non-contact sensors [3]. 1) Non contact approach: On the image processing area, a camera is utilized to take images/videos, which are then processed and picture recognition performed using algorithms that generate words in the display.…”

Section: B Hand Gesture Recongnition Techniquesmentioning

confidence: 99%

Smart Hand Glove for Hearing and Speech Impaired

Jose¹

2021

IJRASET

View full text Add to dashboard Cite

With the advancement of technology, we can implement a variety of ideas to serve mankind in numerous ways. Inspired by this, we have developed a smart hand glove system which will be able to help the people having hearing and speech disabilities. In the world of sound, for those without it, sign language is a powerful tool to make their voices heard. The American Sign Language (ASL) is the most frequently used sign language in the world, with some differences depending on the nation. We created a wearable wireless gesture decoder module in this project that can transform the basic set of ASL motions into alphabets and sentences. Our project utilizes a glove that houses a series of flex sensors on the metacarpal and interphalange joints of the fingers to detect the bending of fingers, through piezoresistive (change in electrical resistance when the semiconductor or metal is subjected to mechanical strain) effect. The glove is attached with an accelerometer as well, that helps to detect the hand movements. Simple classification algorithms from machine learning are then applied to translate the gestures into alphabets or words. Keywords: Arduino; MPU6050; Flex sensor; Machine learning; SVM classifier

show abstract

“…Compared with the traditional wet electrode, semidry electrode, and hard-dry electrode, the flexible sensor has less skin stimulation. Combining the skin is more stable, which greatly expands its application scenarios [9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Accurate Flexible Temperature Sensor Based on Laser‐Induced Graphene Material

Huang

Liu

Orban

et al. 2021

Shock and Vibration

View full text Add to dashboard Cite

Body temperature is an essential physiological index reflecting human health. Accurate measurements of body temperature play a vital role in the diagnosis and treatment of diseases. In this paper, a temperature sensor manufactured by laser-induced graphene is introduced. This sensor has high measurement accuracy, simple preparation, and low production cost. The sensor is made of laser-induced graphene and is easier to fabricate and operate than traditional thermal resistance sensors. The sensor is of high accuracy, is easy to manufacture, and is of low cost. The sensor has high accuracy and is linear between 30°C and 40°C in the human body temperature ranges. Laser-induced graphene (LIG) sensor’s resistance value is correlated linearly with the temperature value, and compared with the infrared thermometer, the accuracy of the sensor is ±0.15°C while that of the infrared thermometer is ±0.30°C. The sensitivity of the LIG sensor is − 0.04145 % ° C − 1 .

show abstract

Implementation of Hand Gesture Recognition Device Applicable to Smart Watch Based on Flexible Epidermal Tactile Sensor Array

Cited by 26 publications

References 41 publications

RadarSNN: A Resource Efficient Gesture Sensing System Based on mm-Wave Radar

RadarSNN: A Resource Efficient Gesture Sensing System Based on mm-Wave Radar

Smart Hand Glove for Hearing and Speech Impaired

Accurate Flexible Temperature Sensor Based on Laser‐Induced Graphene Material

Contact Info

Product

Resources

About