Hand Gesture Recognition Techniques Research Articles

Enhance the AI Virtual System Accuracy with Novel Hand Gesture Recognition Algorithm Comparing to Convolutional Neural Network

The objective of this study is to enhance the precision of AI virtual systems by implementing Novel Hand Gesture Recognition techniques in comparison to Convolutional Neural Network. Materials and Methods: To recognise hand motions, a Convolutional Neural Network with distinct training and testing stages is utilized. The average Gpower for the test is between 0.05 and 0.85, or around 85%. Sample size is determined as 27,455 for each group using G Power 3.1 software (G Power setting parameters: α=0.05 and power=0.85). Results and Discussion: Novel Hand gesture recognition 92.60% identifies between objects and boosts the observed accuracy with a statistically non-significant value of p=0.123 (p>0.05) in comparison to the convolutional neural network's 88.59%. Conclusion: Comparison of the Novel Hand gesture Recognition algorithm and Convolutional Neural Network in terms of performance that shows Hand gesture recognition has 91.62% with better accuracy.

Gesture Recognition for Enhancing Human Computer Interaction

Gesture recognition is critical in human-computer communication. As observed, a plethora of current technological developments are in the works, including biometric authentication, which we see all the time in our smartphones. Hand gesture focus, a frequent human-computer interface in which we manage our devices by presenting our hands in front of a webcam, can benefit people of different backgrounds. Some of the efforts in human-computer interface include voice assistance and virtual mouse implementation with voice commands, fingertip recognition and hand motion tracking based on an image in a live video. Human Computer Interaction (HCI), particularly vision-based gesture and object recognition, is becoming increasingly important. Hence, we focused to design and develop a system for monitoring fingers using extreme learning-based hand gesture recognition techniques. Extreme learning helps in quickly interpreting the hand gestures with improved accuracy which would be a highly useful in the domains like healthcare, financial transactions and global business

Sand Cat Swarm Optimizer with Deep Wavelet Autoencoder-based Sign Language Recognition for Hearing- and Speech-impaired Persons

Sign language is commonly used to interact with people who have speech and hearing disorders. Sign language was exploited for interacting with people having developmental impairments who have some or no communication skills. Communication using Sign language has become a fruitful means of interaction for speech- and hearing-impaired people. The hand gesture recognition technique is useful for dumb and deaf people by using convolutional neural networks (CNNs) and human–computer interface for recognizing the static indication of sign language. Therefore, this study presents a new Sand Cat Swarm Optimizer with Deep Wavelet Autoencoder-based Intelligent Sign Language Recognition (SCSO-DWAESLR) technique for hearing- and speech-impaired persons. In the presented SCSO-DWAESLR technique, computer vision and CNN concepts are utilized for identifying sign languages to aid the interaction of hearing- and speech-impaired persons. The SCSO-DWAESLR method makes use of the Inception v3 model for the feature map generation process. In addition, the DWAE classifier is utilized for the recognition and classification of different kinds of signs posed by hearing- and speech-impaired persons. Finally, the hyperparameters related to the DWAE classifier are optimally chosen by using the SCSO algorithm. For exhibiting the effectual recognition outcomes of the SCSO-DWAESLR technique, a detailed experimental analysis was performed. The comparative outcome highlights the superior recognition performance of the SCSO-DWAESLR method over existing techniques under several evaluation metrics.

Voice Conversion and Hand Gesture Recognition for Aponic People

Aphonia, a condition resulting in the loss of voice, presents significant challenges in interpersonal interactions. This project proposes a dual-pronged approach involving hand gesture recognition and voice conversion techniques to facilitate effective communication for aphonic individuals. The integration of real-time hand gesture recognition provides an alternative means of expressing ideas and emotions. By capturing and translating hand gestures into textual or auditory output, this approach offers a versatile mode of communication. Additionally, advanced voice conversion algorithms are employed to synthesize natural and intelligible speech from typed or selected text. This innovative coupling of technologies empowers aphonic individuals to engage in fluid conversations, fostering improved social interactions and enhancing their overall quality of life. A webcam is used to communicate with deaf and aphonic people. When there are modalities of communication, such as speech, that are unavailable, the human hand is the preferred option. Hand gestures that transmit concepts utilizing diverse forms and finger alignment enable human-machine interaction. The purpose of this work is to develop a hand gesture detection model and translate the results to text and audio formats. The model also responds to user voice commands and displays hand signs from the database.

Hand Gesture Recognition Using Densely Connected Deep Residual Network and Channel Attention Module for Mobile Robot Control

Navigating a mobile robot in an indoor or outdoor environment is an interesting research area for human-robot collaboration (HRC). In such a scenario, hand gesture offers some unique abilities for HRC to provide non-verbal communication between the user and robot. This paper proposes a novel real-time hand gesture recognition (HGR) technique for mobile robot control application. A compact convolutional neural network (CNN) based HGR system denoted as densely connected residual channel attention module (DRCAM) is proposed to recognize the vision-based hand gestures effectively. Since fingers are the most vital sign for HGR; an attention mechanism using multi-scale representation is proposed, which emphasizes finger information more effectively. The proposed CNN model employs the cascading structure of residual blocks with a multi-scale channel attention module to learn low to high-level information of hand gestures. In addition, the cascading structures are connected through dense connectivity, which strengthens the feature propagation and facilitates feature reuse. Experiments are conducted on an ingenuously developed dataset and a publicly available American sign language finger-spelling (ASL-FS) benchmarked dataset to evaluate the performance of the proposed technique. The experimental result illustrates that the proposed DRCAM network outperforms the state-of-the-art methods in terms of mean accuracy using the leave-one-subject-out cross-validation test. Finally, the training model is used to develop a software-based user interface system for the control of a mobile robot in a real-time environment.

HAND GESTURE RECOGNITION SYSTEM FOR CONTROLLING MEDIA PLAYER

Gesture-based real-time gesture recognition systems received great attention in recent years because of their ability to interact with systems efficiently through human-computer interaction. In today’s world, everyone goes for fast interaction with complex systems that ensure a quick response. Therefore, with increase sing technological advances, response time and ease of operation are a major concern. This is where human computer interaction begins. This interaction is not limited, and challenges used devices such as keyboard and mouse input. Touch recognition has received a lot of attention. Touch is natural and is often used in everyday interactions. Thus, communication using touch and computers creates a new level of interaction. In this paper we have discussed a low cost system which uses hand gesture recognition technique to control media player. increases efficiency and makes interaction effortless by letting the user control his/her laptop/desktop from a distance.

FAMAID: A TOOL FOR AIDING PEOPLE WITH DISABILITY

People with disabilities suffer from discrimination and obstacles that restrict them from participating in society on an equal basis with others every day. They are deprived of their rights to be included in ordinary school systems and even in the work market. In the process of raising awareness, facilitating daily routines, and developing guidance, the idea of assisting such people with handy tools/software arose and was implemented in the FamAid tool. FamAid offers people with hearing disability the opportunity to be engaged in the society through many facilities. In this work, we implemented a web application that serves as a community for people with disability who can use sign language to access the app. The app uses hand gesture recognition technique which is considered an active research field in Human-Computer Interaction technology to perform sign language translation to text. Afterwards, the text will be provided as input to the app where the output will be generated based on the request of the user. This research presents an application which is considered a gift for people with speaking and/or hearing disability as it makes their lives easier.

Exploiting domain transformation and deep learning for hand gesture recognition using a low-cost dataglove

Hand gesture recognition is one of the most widely explored areas under the human–computer interaction domain. Although various modalities of hand gesture recognition have been explored in the last three decades, in recent years, due to the availability of hardware and deep learning algorithms, hand gesture recognition research has attained renewed momentum. In this paper, we evaluate the effectiveness of a low-cost dataglove for classifying hand gestures in the light of deep learning. We have developed a cost-effective dataglove using five flex sensors, an inertial measurement unit, and a powerful microcontroller for onboard processing and wireless connectivity. We have collected data from 25 subjects for 24 static and 16 dynamic American sign language gestures for validating our system. Moreover, we proposed a novel Spatial Projection Image-based technique for dynamic hand gesture recognition. We also explored a parallel-path neural network architecture for handling multimodal data more effectively. Our method produced an F1-score of 82.19% for static gestures and 97.35% for dynamic gestures from a leave-one-out-cross-validation approach. Overall, this study demonstrates the promising performance of a generalized hand gesture recognition technique in hand gesture recognition. The dataset used in this work has been made publicly available.

Hand Gesture Recognition using Deep Learning Models

Hand Gesture Recognition (HGR) is most widely used in many applications to detect the hand movements of humans in front of the camera. HGR is the most innovative system that provides a user-friendly nature that can interact with a system that is known to humans. Applications such as machine interaction with humans, language signs, and analyzing the original hand gestures by the system is a very difficult task. HGR detection can be done based on the shapes of the four fingers and one thumb to recognize the hand and their respective location in the image. In this paper, a new HGR technique is introduced based on the shape metrics with the text-to-image conversion technique. For implementation, the Deep Convolution Neural Networks (DCNN) have been used with inception V3. The model considers the skin color and texture because the features of the image are specifically different conditions that are influenced. A webcam is used in the proposed system which is working on 20 fps with 7-megapixel intensity.

A Critical Review on Hand Gesture Recognition using sEMG: Challenges, Application, Process and Techniques

Hand gesture recognition systems are gaining popularity these days due to the ease with which humans and machines can communicate. The goal of hand gesture development is to improve interactions between humans and computers for the purpose of transmitting ideas. In a typical HGR systems, the main steps followed are, data collection, pre-processing, feature extraction and classification. For every stage, a significant number of techniques are available with various other sub steps. This study gives an overview of modern hand gesture recognition techniques, its Physiological and Anatomical Background, working and challenges faced by these systems. Moreover, the role of artificial intelligence in optimizing the performance of HGR systems is also delineated in this paper. Also, the precision and accuracy of the HGR approaches gets affected by the complexity and diversity of various hand movements, therefore, the need for implementing AI based ML and DL methods keeps on rising. Keeping this in mind, the performance of various ML algorithms in recognizing the visual and sensor-based hand gestures is investigated. Moreover, the commonly utilized framework in detecting hand gestures has been explored in numerous standard datasets.

Implementing a Hand Gesture Recognition System Based on Range-Doppler Map.

There have been several studies of hand gesture recognition for human–machine interfaces. In the early work, most solutions were vision-based and usually had privacy problems that make them unusable in some scenarios. To address the privacy issues, more and more research on non-vision-based hand gesture recognition techniques has been proposed. This paper proposes a dynamic hand gesture system based on 60 GHz FMCW radar that can be used for contactless device control. In this paper, we receive the radar signals of hand gestures and transform them into human-understandable domains such as range, velocity, and angle. With these signatures, we can customize our system to different scenarios. We proposed an end-to-end training deep learning model (neural network and long short-term memory), that extracts the transformed radar signals into features and classifies the extracted features into hand gesture labels. In our training data collecting effort, a camera is used only to support labeling hand gesture data. The accuracy of our model can reach 98%.

CONTROLLING VIDEOLAN CLIENT MEDIA USING LUCAS KANADE OPTIMAL FLOW ALGORITHM AND OPENCV

In this project we've discussed a system which uses some dynamic hand gesture recognition technique to manage the media players like VLC media player. This project consist of certain modules which segments the foreground a part of the frame using detection of skin colour and approximate median technique. the popularity of hand gesture is finished by creating a call Tree, that uses certain features extracted from the segmented part. This hand gesture recognition technique introduces a replacement, natural thanks to interact with computers and is beneficial to several folks in our day-to-day life. Hand gestures are associated with the human hands in hand gesture recognition. This project is employed for controlling certain operations (Pause, Play, Volume up, Volume Down, Mute) on video player by mere hand gestures without getting in the rigmarole of pressing buttons or tapping onto the screen. this may be directly associated with our day to life like in presentations. We have considered hand gestures and their directional motion defines a gesture for the applying. during this application image retrieving is finished employing a Webcam. Some functions in VLC media players are used most often and these functions uses some predefined gestures. Shows the defined gestures in keeping with the VLC player control operation. We created a VLC Media Player Controller using Hand Gesture Recognition System to form ‘HUMAN LIFE EASY AND BETTER’. This project is implemented using two steps: (1.) Creation of Hand Gesture Recognition System: this is often done using image processing using OpenCV library. (2.) Controlling VLC Media Player using hand gestures: during this step we controlled the player using shell commands which were recalled using python commands through OS library

Noncontact Electromagnetic Wireless Recognition for Prosthesis Based on Intelligent Metasurface.

With the development of artificial intelligence and Internet of Things, hand gesture recognition techniques have attracted great attention owing to their excellent applications in developing human‐machine interaction (HMI). Here, the authors propose a non‐contact hand gesture recognition method based on intelligent metasurface. Owing to the advantage of dynamically controlling the electromagnetic (EM) focusing in the wavefront engineering, a transmissive programmable metasurface is presented to illuminate the forearm with more focusing spots and obtain comprehensive echo data, which can be processed under the machine learning technology to reach the non‐contact gesture recognition with high accuracy. Compared with the traditional passive antennas, unique variations of echo coefficients resulted from near fields perturbed by finger and wrist agonist muscles can be aquired through the programmable metasurface by switching the positions of EM focusing. The authors realize the gesture recognition using support vector machine algorithm based on five individual focusing spots data and all‐five‐spot data. The influences of the focusing spots on the gesture recognition are analyzed through linear discriminant analysis algorithm and Fisher score. Experimental verifications prove that the proposed metasurface‐based non‐contact wireless design can realize the classification of hand gesture recognition with higher accuracy than traditional passive antennas, and give an HMI solution.

Human Presence Sensing and Gesture Recognition for Smart Home Applications With Moving and Stationary Clutter Suppression Using a 60-GHz Digital Beamforming FMCW Radar

With the advancement of radio frequency (RF) assisted smart home technology, it is critical for the RF sensors deployed indoors to isolate the target of interest from unwanted clutter sources. This paper presents a novel method for suppressing both moving and stationary clutters in an indoor environment to localize stationary human subjects with a millimeter-wave frequency-modulated continuous-wave (FMCW) radar. The method derives its roots from the intrinsic high-pass filter (HPF) characteristic of the exponential moving average (EMA) algorithm, a preferred approach for background stationary clutter suppression. In this work, emphasis was laid on expanding the capability to detect and suppress unwanted moving clutter sources in the indoor environment along with stationary clutters, which has not been widely explored before. The proposed method removes motion artifacts so that the characteristic respiratory signal can be identified for human-aware localization. The paper provides experimental validation of the proposed method, wherein a 60-GHz FMCW radar with digital beamforming (DBF) capability was used to identify the 2-D location of a sitting human subject, with a moving window curtain in the background acting as a strong moving clutter source along with other stationary clutters. In addition, a lateral hand gesture recognition technique is presented, wherein the EMA algorithm was used to enhance the signature of the hand motion. The instantaneous position of the hand at the beginning and end of the gesture was determined to classify the gesture as a left-to-right or right-to-left hand swipe.

A Bag-of-Words Based Feature Extraction Scheme for American Sign Language Number Recognition from Hand Gesture Images

Human Computer Interaction (HCI) focuses on the interaction between humans and machines. An extensive list of applications exists for hand gesture recognition techniques, major candidates for HCI. The list covers various fields, one of which is sign language recognition. In this field, however, high accuracy and robustness are both needed; both present a major challenge. In addition, feature extraction from hand gesture images is a tough task because of the many parameters associated with them. This paper proposes an approach based on a bag-of-words (BoW) model for automatic recognition of American Sign Language (ASL) numbers. In this method, the first step is to obtain the set of representative vocabularies by applying a K-means clustering algorithm to a few randomly chosen images. Next, the vocabularies are used as bin centers for BoW histogram construction. The proposed histograms are shown to provide distinguishable features for classification of ASL numbers. For the purpose of classification, the K-nearest neighbors (kNN) classifier is employed utilizing the BoW histogram bin frequencies as features. For validation, very large experiments are done on two large ASL number-recognition datasets; the proposed method shows superior performance in classifying the numbers, achieving an F1 score of 99.92% in the Kaggle ASL numbers dataset.

주파수 가중 MTI를 이용한 FMCW 레이다 손동작 인지 기법

This paper proposes a signal processing technique that recognizes hand motions using a 60-GHz frequency-modulated continuous- wave radar system. In the case of hand motion recognition with a radar, the recognition accuracy decreases owing to a clutter signal generated by the upper body and differences between the hand sizes of different people. To solve this problem, this paper proposes a moving target indicator filter with a weighted frequency for each frequency, which improves hand motion recognition accuracy, removes self-interference clutter signals, and uses a range profile and Doppler profile. Neural networks can recognize hand movements; hence, the proposed five hand gesture recognition techniques exhibited an 87.3% accuracy, regardless of the user.

Time Complexity of Color Camera Depth Map Hand Edge Closing Recognition Algorithm

The objective of this paper is to calculate the time complexity of the colored camera depth map hand edge closing algorithm of the hand gesture recognition technique. It has been identified as hand gesture recognition through human-computer interaction using color camera and depth map technique, which is used to find the time complexity of the algorithms using 2D minima methods, brute force, and plane sweep. Human-computer interaction is a very much essential component of most people's daily life. The goal of gesture recognition research is to establish a system that can classify specific human gestures and can make its use to convey information for the device control. These methods have different input types and different classifiers and techniques to identify hand gestures. This paper includes the algorithm of one of the hand gesture recognition “Color camera depth map hand edge recognition” algorithm and its time complexity and simulation on MATLAB.

Connectionist Temporal Classification Model for Dynamic Hand Gesture Recognition using RGB and Optical flow Data

Automatic classification of dynamic hand gesture is challenging due to the large diversity in a different class of gesture, Low resolution, and it is performed by finger. Due to a number of challenges many researchers focus on this area. Recently deep neural network can be used for implicit feature extraction and Soft Max layer is used for classification. In this paper, we propose a method based on a two-dimensional convolutional neural network that performs detection and classification of hand gesture simultaneously from multimodal Red, Green, Blue, Depth (RGBD) and Optical flow Data and passes this feature to Long-Short Term Memory (LSTM) recurrent network for frame-to-frame probability generation with Connectionist Temporal Classification (CTC) network for loss calculation. We have calculated an optical flow from Red, Green, Blue (RGB) data for getting proper motion information present in the video. CTC model is used to efficiently evaluate all possible alignment of hand gesture via dynamic programming and check consistency via frame-to-frame for the visual similarity of hand gesture in the unsegmented input stream. CTC network finds the most probable sequence of a frame for a class of gesture. The frame with the highest probability value is selected from the CTC network by max decoding. This entire CTC network is trained end-to-end with calculating CTC loss for recognition of the gesture. We have used challenging Vision for Intelligent Vehicles and Applications (VIVA) dataset for dynamic hand gesture recognition captured with RGB and Depth data. On this VIVA dataset, our proposed hand gesture recognition technique outperforms competing state-of-the-art algorithms and gets an accuracy of 86%

Hand Gesture Recognition and Voice Conversion for Hearing and Speech Aided Community

Sign language recognition (SLR) aims to interpret sign languages automatically by a computer in order to help the deaf communicate with hearing society conveniently. Our aim is to design a system to help the person who trained the hearing impaired to communicate with the rest of the world using sign language or hand gesture recognition techniques. In this system, feature detection and feature extraction of hand gesture is done with the help of Support Vector Machine (SVM), K-Neighbors-Classifier, Logistic-Regression, MLP-Classifier, Naive Bayes, Random-Forest-Classifier algorithms are using image processing.

Hand Gesture Recognition Using an IR-UWB Radar with an Inception Module-Based Classifier

The emerging integration of technology in daily lives has increased the need for more convenient methods for human–computer interaction (HCI). Given that the existing HCI approaches exhibit various limitations, hand gesture recognition-based HCI may serve as a more natural mode of man–machine interaction in many situations. Inspired by an inception module-based deep-learning network (GoogLeNet), this paper presents a novel hand gesture recognition technique for impulse-radio ultra-wideband (IR-UWB) radars which demonstrates a higher gesture recognition accuracy. First, methodology to demonstrate radar signals as three-dimensional image patterns is presented and then, the inception module-based variant of GoogLeNet is used to analyze the pattern within the images for the recognition of different hand gestures. The proposed framework is exploited for eight different hand gestures with a promising classification accuracy of 95%. To verify the robustness of the proposed algorithm, multiple human subjects were involved in data acquisition.