Real-time Hand Gesture Recognition Based on Deep Learning in Complex Environments

Wu, Weixin; Shi, Minjia; Wu, Tao; Zhao, Dawei; Zhang, Shuai; Li, Junxiang

doi:10.1109/ccdc.2019.8833328

Cited by 7 publications

(4 citation statements)

References 7 publications

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“…in 2019. The purpose of this research is to design and develop a real-time hand gesture recognition method from video streams with Deep Learning CNN algorithm in a complex environment with accuracy of 96% [2]. In addition, there is research conducted by Abdullah Muhajid et al in 2021 which discusses hand gesture recognition for help disabilities in communicating by proposing a lightweight model based on YOLO (You Only Look Once) v3 and CNN for gesture recognition and to determine the accuracy of the algorithm that is 97.68% [3].…”

Section: Related Studymentioning

confidence: 99%

“…Technology that involves interaction between humans and computers has increased widely and rapidly with various methods and techniques to overcome various problems in human life [4]. Human-computer interaction methods mainly include voice interaction and gesture interaction [2] because gestures contain a lot of information that can convey semantics, emotions and conform to human daily life habits [13]. The gesture can be in the form of a hand gesture that allows us to express our thoughts clearly in our everyday interactions [4].…”

Section: Introductionmentioning

confidence: 99%

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Hand Gesture Recognition as Signal for Help using Deep Neural Network

Thejowahyono¹,

Setiawan²,

Handoyo³

et al. 2022

IJETAE

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Hand gesture recognition research has increased widely and rapidly to overcome various problems in human life. When in a dangerous situation humans are required to act quickly and precisely, such as being a victim of violence, accidents, disasters, and so on. In the asking process for help, it is necessary to use an appropriate safe method, communication tools, and speed in sending messages requesting help so that something unwanted does not happen. One of the ways to overcome this problem is to use sign language such as hand gestures. So that this study designs and develops a real-time hand gesture recognition system as signal for help using Deep Neural Network. The purpose of this research is to simplify, speed up, and produce a safe way that humans can use when asking for help from others without speaking when in danger happen. Base on the experiment that real-time detection and an average accuracy of 98.79%. When the system detects a hand gesture to ask for help, a request for help message will be sent to the designated emergency email. So that the benefits derived from the design and development of this system are convenience, speed, and safety in the process of asking for help when humans are in a dangerous situation. Keywords— Hand gesture recognition, Signal for help, Deep Neural Network, Dangerous Situation, Emergency Email.

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Section: Related Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hand Gesture Recognition as Signal for Help using Deep Neural Network

Thejowahyono¹,

Setiawan²,

Handoyo³

et al. 2022

IJETAE

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show abstract

“…Audio and visual-based input modalities implemented using sensors were developed as well, such as speech recognition based on the audio signal acquired with a microphone (e.g., [ 63 , 90 , 91 ]), facial expression recognition based on processing visual data from the camera [ 92 ], human body posture recognition using data from the conventional gray level or color camera, thermal infrared sensor, depth sensor, smart vision sensor [ 66 ], user-movement recognition using Kinect [ 72 ], gesture recognition based on data from depth sensor [ 93 ] and USB camera on a helmet [ 94 ], emotion recognition with a laptop camera [ 91 ], and so on. The Kinect can also be used to implement a solution for contact-free stress recognition, where the Kinect can provide respiration signals under different breathing patterns [ 95 ].…”

Section: Backgrounds and Related Workmentioning

confidence: 99%

Sensors and Artificial Intelligence Methods and Algorithms for Human–Computer Intelligent Interaction: A Systematic Mapping Study

Šumak

Brdnik

Pušnik

2021

Sensors

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To equip computers with human communication skills and to enable natural interaction between the computer and a human, intelligent solutions are required based on artificial intelligence (AI) methods, algorithms, and sensor technology. This study aimed at identifying and analyzing the state-of-the-art AI methods and algorithms and sensors technology in existing human–computer intelligent interaction (HCII) research to explore trends in HCII research, categorize existing evidence, and identify potential directions for future research. We conduct a systematic mapping study of the HCII body of research. Four hundred fifty-four studies published in various journals and conferences between 2010 and 2021 were identified and analyzed. Studies in the HCII and IUI fields have primarily been focused on intelligent recognition of emotion, gestures, and facial expressions using sensors technology, such as the camera, EEG, Kinect, wearable sensors, eye tracker, gyroscope, and others. Researchers most often apply deep-learning and instance-based AI methods and algorithms. The support sector machine (SVM) is the most widely used algorithm for various kinds of recognition, primarily an emotion, facial expression, and gesture. The convolutional neural network (CNN) is the often-used deep-learning algorithm for emotion recognition, facial recognition, and gesture recognition solutions.

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“…Hand gestures are a critically important form of non-verbal communication. The interpretation of hand gestures with wearable sensors [1], [2], or cameras [3], [4] aims to transform the hand gestures into meaningful instructions; this interaction is also known as hand gesture recognition. The field of hand gesture recognition has seen significant improvements over the past few years [5] and, most recently, bundled with the latest advancements in computer vision, has encouraged the development of new technologies to support rehabilitation [6], [7], robot control, and home automation [8].…”

Section: Introductionmentioning

confidence: 99%

A 3DCNN-LSTM Multi-Class Temporal Segmentation for Hand Gesture Recognition

Gionfrida¹,

Rusli²,

Kedgley³

et al. 2022

Preprint

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This paper introduces a multi-class hand gesture recognition model developed to identify a set of defined hand gesture sequences in two-dimensional RGB video recordings. The work presents an action detection classifier that looks at both appearance and spatiotemporal parameters of consecutive frames. The classifier utilizes a convolutional-based network combined with a long-short-term memory unit. To leverage the need for a large-scale dataset, the model uses an available dataset to then adopt a technique known as transfer learning to fine-tune the model on the hand gestures of relevance. Validation curves performed over a batch size of 64 indicate an accuracy of 93.95% (± 0.37) with a mean Jaccard index of 0.812 (± 0.105) for 22 participants. The presented model illustrates the possibility of training a model with a small set of data (113,410 fully labelled frames). The proposed pipeline embraces a small-sized architecture that could facilitate its adoption.

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Real-time Hand Gesture Recognition Based on Deep Learning in Complex Environments

Cited by 7 publications

References 7 publications

Hand Gesture Recognition as Signal for Help using Deep Neural Network

Hand Gesture Recognition as Signal for Help using Deep Neural Network

Sensors and Artificial Intelligence Methods and Algorithms for Human–Computer Intelligent Interaction: A Systematic Mapping Study

A 3DCNN-LSTM Multi-Class Temporal Segmentation for Hand Gesture Recognition

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