A 137-μW Area-Efficient Real-Time Gesture Recognition System for Smart Wearable Devices

2018 ◽  
Author(s):  
Taegeun Yoo ◽  
Van Loi Le ◽  
Ju Eon Kim ◽  
Ngoc Le Ba ◽  
Kwang-Hyun Baek ◽  
...  
Keyword(s):  
Real Time ◽  
Gesture Recognition ◽  
Recognition System ◽  
Wearable Devices ◽  
Smart Wearable ◽  
Area Efficient

scholarly journals Dynamic Gesture Recognition System with Gesture Spotting Based on Self-Organizing Maps

2021 ◽  
Vol 11 (4) ◽  
pp. 1933
Author(s):  
Hiroomi Hikawa ◽  
Yuta Ichikawa ◽  
Hidetaka Ito ◽  
Yutaka Maeda
Keyword(s):  
Real Time ◽  
Gesture Recognition ◽  
Weight Vector ◽  
Recognition System ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
Time Dynamic ◽  
System Input ◽  
Self Organizing ◽  
Gesture Spotting

In this paper, a real-time dynamic hand gesture recognition system with gesture spotting function is proposed. In the proposed system, input video frames are converted to feature vectors, and they are used to form a posture sequence vector that represents the input gesture. Then, gesture identification and gesture spotting are carried out in the self-organizing map (SOM)-Hebb classifier. The gesture spotting function detects the end of the gesture by using the vector distance between the posture sequence vector and the winner neuron’s weight vector. The proposed gesture recognition method was tested by simulation and real-time gesture recognition experiment. Results revealed that the system could recognize nine types of gesture with an accuracy of 96.6%, and it successfully outputted the recognition result at the end of gesture using the spotting result.

scholarly journals FPGA based Real Time Human Hand Gesture Recognition System

2012 ◽  
Vol 6 ◽  
pp. 98-107 ◽  
Author(s):  
Amit Gupta ◽  
Vijay Kumar Sehrawat ◽  
Mamta Khosla
Keyword(s):  
Real Time ◽  
Gesture Recognition ◽  
Recognition System ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Human Hand

scholarly journals Application of Real-Time Visual Feedback System in Balance Training of the Center of Pressure with Smart Wearable Devices

2020 ◽  
Author(s):  
I-Lin Wang ◽  
Li-I Wang ◽  
Yang Liu ◽  
Shi-Jie Xue ◽  
Rui Hu ◽  
...  
Keyword(s):  
Real Time ◽  
Visual Feedback ◽  
Postural Sway ◽  
Center Of Pressure ◽  
Balance Training ◽  
Training Group ◽  
Wearable Devices ◽  
Control Group ◽  
Feedback Training ◽  
Smart Wearable

Abstract Background: Visual feedback from the center of pressure (COP) on the benefits of standing quietly remains controversial. The study was to investigate the adaptive effect of COP real-time visual feedback training provided by smart wearable devices on standing in silence. Methods: Thirty healthy female college students were randomly divided into three groups (visual feedback balance training group (VFT), non-visual feedback balance training group (NVFT) and control group (CG)) .Two force plates were used to calculate the coordinates of COP anteroposterior (COPAP) and COP mediolateral (COPML).The motion analysis system is used to calculate the coordinates of the center of mass in two directions. Enhanced visual feedback on the screen in the form of fluctuating in different directions, VFT received real-time visual feedback from Podoon APP for training, the NVFT only performs open eye balance without receiving real-time visual feedback. The CG group did not receive any visual feedback. The training lasted 4 weeks, the training lasts 30 minutes at an interval of 1 days. Results: After four weeks of balance training, the results showed that visual feedback training can improve the stability of human posture control by one leg stance and tandem stance static balance training on VFT intelligent App. The parameters of COPML/AP max displacement, COPML/AP velocity and COP radius and COP area in the VFT were significantly increased (p＜0.05).Conclusion: The conclusion shows that COP real-time visual feedback training provided by smart wearable devices can reduce postural sway better and improve body balance ability than general training when standing quietly.

scholarly journals Real-time Hand-Gesture Recognition based on Deep Neural Network

2021 ◽  
Vol 102 ◽  
pp. 04009
Author(s):  
Naoto Ageishi ◽  
Fukuchi Tomohide ◽  
Abderazek Ben Abdallah
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Gesture Recognition ◽  
Deep Neural Network ◽  
Recognition System ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Robotic Control ◽  
Driver Assistance Systems ◽  
Fast Classification

Hand gestures are a kind of nonverbal communication in which visible bodily actions are used to communicate important messages. Recently, hand gesture recognition has received significant attention from the research community for various applications, including advanced driver assistance systems, prosthetic, and robotic control. Therefore, accurate and fast classification of hand gesture is required. In this research, we created a deep neural network as the first step to develop a real-time camera-only hand gesture recognition system without electroencephalogram (EEG) signals. We present the system software architecture in a fair amount of details. The proposed system was able to recognize hand signs with an accuracy of 97.31%.

A real-time hand gesture recognition system for daily information retrieval from the internet

2015 ◽  
Vol 11 (2) ◽  
pp. 105 ◽  
Author(s):  
Joseph C. Tsai ◽  
Shih Ming Chang ◽  
Shwu Huey Yen ◽  
Kuan Ching Li ◽  
Yung Hui Chen ◽  
...  
Keyword(s):  
Information Retrieval ◽  
Real Time ◽  
Gesture Recognition ◽  
Recognition System ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
The Internet

A Real Time Hand Gesture Recognition System Based on the Prior Facial Knowledge and SVM

2013 ◽  
Vol 8 (11) ◽  
pp. 185-193 ◽  
Author(s):  
Jiali Li ◽  
Lingxiang Zheng ◽  
Yuqi Chen ◽  
Yixiong Zhang ◽  
Peng Lu
Keyword(s):  
Real Time ◽  
Gesture Recognition ◽  
Recognition System ◽  
Hand Gesture Recognition ◽  
Hand Gesture

scholarly journals A Frame Detection Method for Real-Time Hand Gesture Recognition Systems Using CW-Radar

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2321 ◽  
Author(s):  
Myoungseok Yu ◽  
Narae Kim ◽  
Yunho Jung ◽  
Seongjoo Lee
Keyword(s):  
Real Time ◽  
Conventional Method ◽  
Gesture Recognition ◽  
Detection Method ◽  
Recognition System ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Recognition Probability ◽  
Recognition Systems ◽  
Cw Radar

In this paper, a method to detect frames was described that can be used as hand gesture data when configuring a real-time hand gesture recognition system using continuous wave (CW) radar. Detecting valid frames raises accuracy which recognizes gestures. Therefore, it is essential to detect valid frames in the real-time hand gesture recognition system using CW radar. The conventional research on hand gesture recognition systems has not been conducted on detecting valid frames. We took the R-wave on electrocardiogram (ECG) detection as the conventional method. The detection probability of the conventional method was 85.04%. It has a low accuracy to use the hand gesture recognition system. The proposal consists of 2-stages to improve accuracy. We measured the performance of the detection method of hand gestures provided by the detection probability and the recognition probability. By comparing the performance of each detection method, we proposed an optimal detection method. The proposal detects valid frames with an accuracy of 96.88%, 11.84% higher than the accuracy of the conventional method. Also, the recognition probability of the proposal method was 94.21%, which was 3.71% lower than the ideal method.

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