Background With the development of today's technology, and as humans tend to naturally use hand gestures in their communication process to clarify their intentions, hand gesture recognition is considered to be an important part of Human Computer Interaction (HCI), which gives computers the ability of capturing and interpreting hand gestures, and executing commands afterwards. The aim of this study is to perform a systematic literature review for identifying the most prominent techniques, applications and challenges in hand gesture recognition. Methodology To conduct this systematic review, we have screened 560 papers retrieved from IEEE Explore published from the year 2016 to 2018, in the searching process keywords such as "hand gesture recognition" and "hand gesture techniques" have been used. However, to focus the scope of the study 465 papers have been excluded. Only the most relevant hand gesture recognition works to the research questions, and the well-organized papers have been studied. Results The results of this paper can be summarized as the following; the surface electromyography (sEMG) sensors with wearable hand gesture devices were the most acquisition tool used in the work studied, also Artificial Neural Network (ANN) was the most applied classifier, the most popular application was using hand gestures for sign language, the dominant environmental surrounding factor that affected the accuracy was the background color, and finally the problem of overfitting in the datasets was highly experienced. Conclusions The paper will discuss the gesture acquisition methods, the feature extraction process, the classification of hand gestures, the applications that were recently proposed, the challenges that face researchers in the hand gesture recognition process, and the future of hand gesture recognition. We shall also introduce the most recent research from the year 2016 to the year 2018 in the field of hand gesture recognition for the first time.

A systematic review on hand gesture recognition techniques, challenges and applications.

Mobile and ambient sensors provide a scalable platform for the integration of computing devices and smart appliances for smart home. In which mobile devices, such as smart watches and smart-phone commonly embedded with actuators and sensors i.e., accelerometers and gyroscopes, have opened up chances for the user to easily control home appliances. This paper proposes an integrated method and system that utilize several deep models and mobile sensors for hand gestures applicable for smart homes. The system consists of three components of actual smart home configurations: (i) smart-watch worn on the user’s wrist for capturing gesture patterns (ii) a recognition application that runs on the smart mobile phone and sends correspond commands to the home automation platform; and (iii) home automation platform with connected smart devices instrumented with ambient sensors. In addition, we define a simple yet easy-to-learn hand-gesture vocabulary composing of 18 gestures to the user. With the F-score of over 75%, our experiment on our self-collected data-set consisting of 18 gestures from 20 subjects, demonstrates that the feasibility of the gesture recognition for controlling home appliances.

Recognizing hand gestures for controlling home appliances with mobile sensors

Edge computing is a decentralized computing paradigm with low latency and high response time This paradigm provides services for end-devices at the network edge and brings together IoT, big data, and mobile computing into an integrated and ubiquitous computing platform The on-demand computing power and the processing ability for data from enddevices are needed at the network edge More recently, edge computing capable with artificial intelligence technologies has received much attention in the research literature This paper will review the recent research literature about using edge computing in bioinformatics and health informatics, and provide challenges, opportunities and further research directions

A Survey on Edge Computing in Bioinformatics and Health Informatics

Analysis of the 19th Presidential TV Debate Using Deep Learning Based Video Processing Algorithms : Analysis of the frequency, facial expression and gaze

Elderly Safety and Communication: An IoT-Based Smart Glove for Fall Detection and Hand Gesture Communication

To support an efficient media consumption in wearable and IoT (Internet of Things) environments, the standardiz ation of IoMT (Internet of Media-Things) is in the progress in MPEG (Moving Picture Experts Group). In this paper, we present a method to detect and to recognize hand gestures for generating hand gesture-based commands to control the media consumption in smart glasses. First, we present a detection method that utilizes depth image obtained by incoming stereo image sequences and skin color information in a combined way. Secondly, we are going to present the representation of detected hand contours based on Bezier curve as metadata to provide an interoperable interface between a detection module and a recognition module in an IoMT framework. In addition, the comparison with existing standard tools that can be used for hand gesture representation is given. In the recognition module, the detected hand contour is reconstructed by parsing delivered metadata. A set of hand gestures featured with diverse combination of open fingers and rotational angles is used for the hand gesture recognition in the proposed recognition method. Finally, the recognized hand gesture is mapped into one of the pre-defined set of gesture commands. Experiment results show that the proposed method gives quite stable performance of detection and recognition of hand gesture along with interoperable interface between both processing modules.

Detection and recognition of hand gesture for wearable applications in IoMT

Hand gestures are attracting attention as a NUI (Natural User Interface) of wearable devices such as smart glasses. Recently, to support efficient media consumption in IoT (Internet of Things) and wearable environments, the standardization of IoMT (Internet of Media Things) is in the progress in MPEG. In IoMT, it is assumed that hand gesture detection and recognition are performed on a separate device, and thus provides an interoperable interface between these modules. Meanwhile, deep learning based hand gesture recognition techniques have been recently actively studied to improve the recognition performance. In this paper, we propose a method of hand gesture recognition based on CNN (Convolutional Neural Network) for various applications such as media consumption in wearable devices which is one of the use cases of IoMT. The proposed method detects hand contour from stereo images acquisitioned by smart glasses using depth information and color information, constructs data sets to learn CNN, and then recognizes gestures from input hand contour images. Experimental results show that the proposed method achieves the average 95% hand gesture recognition rate. Keyword : MPEG-IoMT, Hand Gesture, Hand Contour, CNN, Gesture Recognition Copyright C 2016 Korean Institute of Broadcast and Media Engineers. All rights reserved. “This is an Open-Access article distributed under the terms of the Creative Commons BY-NC-ND (http://creativecommons.org/licenses/by-nc-nd/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited and not altered.” a) 한국항공대학교 항공전자정보공학부(Korea Aerospace University, School of Electronics and Information Engineering) ‡Corresponding Author : 김재곤(Jae-Gon Kim) E-mail: jgkim@kau.ac.kr Tel: +82-2-300-0414 ORCID: http://orcid.org/0000-0003-3686-4786 ※본 연구는 산업통상자원부 국가표준기술원에서 시행한 국가표준기술력향상사업[10077958]의 지원을 받아 수행된 연구임. ※이 논문의 연구결과 중 일부는 “한국방송·미디어공학회 2017년 추계학술대회”에서 발표한 바 있음. ※This work was supported by National Standards Technology Promotion Program of Korean Agency for Technology and Standards (KATS) grant funded by the Ministry of Trade, Industry and Energy (MOTIE, Korea) (10077958). ※Parts of this work have been published in the 2017 Fall Conference of the Korean Institute of Broadcasting and Media Engineers. ・Manuscript received January 12, 2018; Revised February 5, 2018; Accepted February 14, 2018. 특집논문 (Special Paper) 방송공학회논문지 제23권 제2호, 2018년 3월 (JBE Vol. 23, No. 2, March 2018) https://doi.org/10.5909/JBE.2018.23.2.246 ISSN 2287-9137 (Online) ISSN 1226-7953 (Print) 문현철 외 2인: 웨어러블 응용을 위한 CNN 기반 손 제스처 인식 247 (Hyeon-Chul Moon et al.: CNN-Based Hand Gesture Recognition for Wearable Applications)

CNN-Based Hand Gesture Recognition for Wearable Applications

인터넷 비디오 코딩(Internet Video Coding: IVC)은 MPEG에서 개발 중인 로열티 무료 비디오 코덱이다. IVC 코덱의 부호화 효율은 지속적으로 향상되어왔으며, CD(Committee Draft) 버전의 IVC는 객관적 화질 및 주관적 화질이 H.264/AVC HP(High Profile)와 견줄 만한 수준의 성능을 낸다고 보고 되었다. 본 논문에서는 IVC 코덱 구조의 개요 및 주요 부호화 알고리즘과 함께 MPEG에서의 IVC 개발 과정 중에 부호화 효율을 향상시키기 위하여 제안된 부호화 툴을 제시한다. 부호화 툴은 비 참조 P 프레임 부호화, DC 모드 화면내 예측, 라그랑지안 승수(Lagrange Multiplier) 선택기법, 색차신호의 화면내 예측모드 확장 기법 등 표준 및 비표준 부호화기법을 포함한다. 각 부호화 툴에 대한 알고리즘과 부호화 효율 이득을 실험을 통하여 제시하였다. 실험결과 각 부호화 툴은 저지연 부호화 모드에서 각각 8.8%, 0.4%, 0.4%, 0.0%의 비트율 절감의 부호화 이득을 얻었다.

Coding Tools for Enhancing Coding Efficiency of MPEG Internet Video Coding (IVC)

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Detection and recognition of hand gesture for wearable applications in IoMT

CNN-Based Hand Gesture Recognition for Wearable Applications

Coding Tools for Enhancing Coding Efficiency of MPEG Internet Video Coding (IVC)