Low-cost intelligent static gesture recognition system

2016 ◽  
Author(s):  
Harini Sekar ◽  
R Rajashekar ◽  
Gosakan Srinivasan ◽  
Priyanka Suresh ◽  
Vineeth Vijayaraghavan
Keyword(s):  
Gesture Recognition ◽  
Low Cost ◽  
Recognition System ◽  
Static Gesture Recognition

scholarly journals CrossGR

2021 ◽  
Vol 5 (1) ◽  
pp. 1-23
Author(s):  
Xinyi Li ◽  
Liqiong Chang ◽  
Fangfang Song ◽  
Ju Wang ◽  
Xiaojiang Chen ◽  
...  
Keyword(s):  
Gesture Recognition ◽  
Low Cost ◽  
User Involvement ◽  
Recognition System ◽  
Training Data ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Target User ◽  
Order Of Magnitude ◽  
Training Examples

This paper focuses on a fundamental question in Wi-Fi-based gesture recognition: "Can we use the knowledge learned from some users to perform gesture recognition for others?". This problem is also known as cross-target recognition. It arises in many practical deployments of Wi-Fi-based gesture recognition where it is prohibitively expensive to collect training data from every single user. We present CrossGR, a low-cost cross-target gesture recognition system. As a departure from existing approaches, CrossGR does not require prior knowledge (such as who is currently performing a gesture) of the target user. Instead, CrossGR employs a deep neural network to extract user-agnostic but gesture-related Wi-Fi signal characteristics to perform gesture recognition. To provide sufficient training data to build an effective deep learning model, CrossGR employs a generative adversarial network to automatically generate many synthetic training data from a small set of real-world examples collected from a small number of users. Such a strategy allows CrossGR to minimize the user involvement and the associated cost in collecting training examples for building an accurate gesture recognition system. We evaluate CrossGR by applying it to perform gesture recognition across 10 users and 15 gestures. Experimental results show that CrossGR achieves an accuracy of over 82.6% (up to 99.75%). We demonstrate that CrossGR delivers comparable recognition accuracy, but uses an order of magnitude less training samples collected from the end-users when compared to state-of-the-art recognition systems.

Dynamic and Static Gesture Recognition System Using Moments

2017 ◽  
Vol 4 (2) ◽  
pp. 69-72 ◽  
Author(s):  
Rajvardhan Thakare ◽  
Parvez Khan Pathan ◽  
Meghana Lokhande ◽  
Neha Waje
Keyword(s):  
Gesture Recognition ◽  
Recognition System ◽  
Static Gesture Recognition

Low-cost finger gesture recognition system for disabled and elderly people

2017 ◽  
Author(s):  
Tusher Chakraborty ◽  
Md. Nasim ◽  
Sakib Md Bin Malek ◽  
Md. Taksir Hasan Majumder ◽  
Mohammed Samiul Saeef ◽  
...  
Keyword(s):  
Elderly People ◽  
Gesture Recognition ◽  
Low Cost ◽  
Recognition System

Devising a novel visible light based low-cost ultra-low-power gesture recognition system

2017 ◽  
Author(s):  
Tusher Chakraborty ◽  
Md. Nasim ◽  
Sakib Md. Bin Malek ◽  
Md. Taksir Hasan Majumder ◽  
Md. Samiul Saeef ◽  
...  
Keyword(s):  
Low Power ◽  
Visible Light ◽  
Gesture Recognition ◽  
Low Cost ◽  
Recognition System ◽  
Ultra Low Power

scholarly journals A Novel Hand Gesture Recognition Based on High-Level Features

2018 ◽  
Vol 15 (02) ◽  
pp. 1750022 ◽  
Author(s):  
Jing Li ◽  
Jianxin Wang ◽  
Zhaojie Ju
Keyword(s):  
Human Computer Interaction ◽  
Real Time ◽  
Gesture Recognition ◽  
Feature Fusion ◽  
Recognition System ◽  
Depth Information ◽  
Static Gesture Recognition ◽  
Time Requirements ◽  
High Level ◽  
Computer Interaction

Gesture recognition plays an important role in human–computer interaction. However, most existing methods are complex and time-consuming, which limit the use of gesture recognition in real-time environments. In this paper, we propose a static gesture recognition system that combines depth information and skeleton data to classify gestures. Through feature fusion, hand digit gestures of 0–9 can be recognized accurately and efficiently. According to the experimental results, the proposed gesture recognition system is effective and robust, which is invariant to complex background, illumination changes, reversal, structural distortion, rotation, etc. We have tested the system both online and offline which proved that our system is satisfactory to real-time requirements, and therefore it can be applied to gesture recognition in real-world human–computer interaction systems.

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