scholarly journals In-Air Ultrasonic 3D-Touchscreen with Gesture Recognition Using Existing Hardware for Smart Devices

2016 ◽  
Author(s):  
Bert Van Dam ◽  
Yuri Murillo ◽  
Min Li ◽  
Sofie Pollin
Keyword(s):  
Gesture Recognition ◽  
Smart Devices

A 260µW infrared gesture recognition system-on-chip for smart devices

2016 ◽  
Author(s):  
Sechang Oh ◽  
Ngoc Le Ba ◽  
Suyoung Bang ◽  
Junwon Jeong ◽  
David Blaauw ◽  
...  
Keyword(s):  
Gesture Recognition ◽  
Recognition System ◽  
System On Chip ◽  
Smart Devices ◽  
On Chip

A 137-μW 1.78-mm2 30-Frames/s Real-Time Gesture Recognition SoC for Smart Devices

2020 ◽  
Vol 28 (8) ◽  
pp. 1909-1919
Author(s):  
Van Loi Le ◽  
Taegeun Yoo ◽  
Ju Eon Kim ◽  
Ngoc Le Ba ◽  
Kwang-Hyun Baek ◽  
...  
Keyword(s):  
Real Time ◽  
Gesture Recognition ◽  
Smart Devices

3D Gesture Recognition Based on Handheld Smart Terminals

2018 ◽  
Vol 9 (4) ◽  
pp. 96-111 ◽  
Author(s):  
Yunhe Li ◽  
Yi Xie ◽  
Qinyu Zhang
Keyword(s):  
Gesture Recognition ◽  
Template Matching ◽  
Three Dimensional ◽  
Recognition System ◽  
Smart Devices ◽  
Motion Sensors ◽  
Human Computer Interactions ◽  
Computational Load ◽  
Dynamic Template ◽  
Computer Interactions

With the popularity of smart devices, it has become impossible for traditional human-computer interaction techniques to accommodate people's needs. This article proposes an iOS-based three dimensional (3D) gesture recognition system, gathering users' specific gestures from their handheld smart terminals to judge implications of these gestures, so to control other smart terminals with more natural human-computer interactions. In this article, gestures were recognized by reading data about corresponding 3D gesture data with motion sensors of smart terminals using optimized dynamic time warping (DTW) algorithm. As to this algorithm, curve paths were delimited via slope based on features of mobile devices and dynamic programming. Meanwhile, this algorithm reduced computational load for template matching and costs of gesture recognition by preliminarily storing upper and lower boundaries of delimited areas with linked lists or setting distortion thresholds. In this article, efficiency and precision of recognition schemes were tested and verified on cellphones. The results suggested that the improved algorithm was less time-consuming than classical algorithms, and required less time for computational load for template matching. Furthermore, it was demonstrated that the gesture recognition based on dynamic template matching algorithms, with higher recognition efficiency and precision, could bring better experiences of human-computer interactions.

Hand Gesture Recognition for Smart Devices by Classifying Deterministic Doppler Signals

2021 ◽  
Vol 69 (1) ◽  
pp. 365-377
Author(s):  
Yi Zhang ◽  
Shuqin Dong ◽  
Chengkai Zhu ◽  
Marcel Balle ◽  
Bin Zhang ◽  
...  
Keyword(s):  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Smart Devices ◽  
Hand Gesture ◽  
Doppler Signals

scholarly journals A 256 pixel, 21.6 μW infrared gesture recognition processor for smart devices

2019 ◽  
Vol 86 ◽  
pp. 49-56 ◽  
Author(s):  
Ngoc Le Ba ◽  
Sechang Oh ◽  
Dennis Sylvester ◽  
Tony Tae-Hyoung Kim
Keyword(s):  
Gesture Recognition ◽  
Smart Devices

Gesture Recognition Based on Acceleration Sensor

2014 ◽  
Vol 602-605 ◽  
pp. 1598-1601 ◽  
Author(s):  
Zhi Gang Wu ◽  
Hong Bin Dai
Keyword(s):  
Feature Extraction ◽  
Computational Complexity ◽  
Gesture Recognition ◽  
Rapid Development ◽  
Wearable Devices ◽  
Smart Devices ◽  
Acceleration Sensor ◽  
High Complexity ◽  
Matching Method ◽  
Acceleration Sensors

With the rapid development of smart devices and wearable devices, gesture recognition based on the acceleration sensors is becoming one of the current hot research topic.In order to resolve the high complexity of time and space issues in gesture recognition based on acceleration sensor,this paper presents a feature extraction and matching method based on the key points.Experiments show that the method could greatly reducing the computational complexity.

A Review on Big Data Analytics in Business

2018 ◽  
pp. 210-214
Author(s):  
Manbir Sandhu ◽  
Purnima, Anuradha Saini
Keyword(s):  
Big Data ◽  
Big Data Analytics ◽  
Smart Devices ◽  
Data Streaming ◽  
Huge Amount ◽  
Business Units ◽  
Enormous Amount ◽  
Tools And Techniques ◽  
Heath Care ◽  
Data Analytic

Big data is a fast-growing technology that has the scope to mine huge amount of data to be used in various analytic applications. With large amount of data streaming in from a myriad of sources: social media, online transactions and ubiquity of smart devices, Big Data is practically garnering attention across all stakeholders from academics, banking, government, heath care, manufacturing and retail. Big Data refers to an enormous amount of data generated from disparate sources along with data analytic techniques to examine this voluminous data for predictive trends and patterns, to exploit new growth opportunities, to gain insight, to make informed decisions and optimize processes. Data-driven decision making is the essence of business establishments. The explosive growth of data is steering the business units to tap the potential of Big Data to achieve fueling growth and to achieve a cutting edge over their competitors. The overwhelming generation of data brings with it, its share of concerns. This paper discusses the concept of Big Data, its characteristics, the tools and techniques deployed by organizations to harness the power of Big Data and the daunting issues that hinder the adoption of Business Intelligence in Big Data strategies in organizations.

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