Gestural flick input-based non-touch interface for character input

Rahim, Abdur; Shin, Jungpil; Islam, Rashedul

doi:10.1007/s00371-019-01758-8

Cited by 8 publications

(5 citation statements)

References 17 publications

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“…To obtain the cepstrum coefficient C i , we applied a Fourier transform to the logarithm of the power spectrum, therefore, we obtain the signal cepstrum by following Equation (9).…”

Section: Feature Extraction Of Emg Sensorsmentioning

confidence: 99%

“…Human-computer interaction (HCI) is an ever-evolving advancement in the development of technology as a new method of communication between people and computers in the modern world [1,2]. Several new assistive methods, such as virtual reality [3], sign language recognition [4,5], speech recognition [6], visual analysis [7], brain activity [8], touch-free writing [9], have emerged in recent years to achieve this goal. Hand gesture recognition implies the importance of performing various visual tasks and working in an unobtrusive environment.…”

Section: Introductionmentioning

confidence: 99%

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Hand Movement Activity-Based Character Input System on a Virtual Keyboard

Rahim

Shin

2020

Electronics

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Nowadays, gesture-based technology is revolutionizing the world and lifestyles, and the users are comfortable and care about their needs, for example, in communication, information security, the convenience of day-to-day operations and so forth. In this case, hand movement information provides an alternative way for users to interact with people, machines or robots. Therefore, this paper presents a character input system using a virtual keyboard based on the analysis of hand movements. We analyzed the signals of the accelerometer, gyroscope, and electromyography (EMG) for movement activity. We explored potential features of removing noise from input signals through the wavelet denoising technique. The envelope spectrum is used for the analysis of the accelerometer and gyroscope and cepstrum for the EMG signal. Furthermore, the support vector machine (SVM) is used to train and detect the signal to perform character input. In order to validate the proposed model, signal information is obtained from predefined gestures, that is, “double-tap”, “hold-fist”, “wave-left”, “wave-right” and “spread-finger” of different respondents for different input actions such as “input a character”, “change character”, “delete a character”, “line break”, “space character”. The experimental results show the superiority of hand gesture recognition and accuracy of character input compared to state-of-the-art systems.

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“…To obtain the cepstrum coefficient C i , we applied a Fourier transform to the logarithm of the power spectrum, therefore, we obtain the signal cepstrum by following Equation (9).…”

Section: Feature Extraction Of Emg Sensorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hand Movement Activity-Based Character Input System on a Virtual Keyboard

Rahim

Shin

2020

Electronics

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“…Moreover, handwriting is vital in identifying patients in medical fields, like identifying Parkinson's disease and autistic children [6]. Researchers are investigating handwritten characters from many sources, such as paper documents [7], images, touch and non-touch screens [8], and other devices. It is easy to collect, less stressful for humans, and suitable for classification.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Hybrid Model Based on CNN and BiLSTM for Identifying Individuals via Handwriting Analysis

Rahim,

Farid,

Miah

et al. 2024

CMES

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Handwriting is a unique and significant human feature that distinguishes them from one another. There are many researchers have endeavored to develop writing recognition systems utilizing specific signatures or symbols for person identification through verification. However, such systems are susceptible to forgery, posing security risks. In response to these challenges, we propose an innovative hybrid technique for individual identification based on independent handwriting, eliminating the reliance on specific signatures or symbols. In response to these challenges, we propose an innovative hybrid technique for individual identification based on independent handwriting, eliminating the reliance on specific signatures or symbols. Our innovative method is intricately designed, encompassing five distinct phases: data collection, preprocessing, feature extraction, significant feature selection, and classification. One key advancement lies in the creation of a novel dataset specifically tailored for Bengali handwriting (BHW), setting the foundation for our comprehensive approach. Post-preprocessing, we embarked on an exhaustive feature extraction process, encompassing integration with kinematic, statistical, spatial, and composite features. This meticulous amalgamation resulted in a robust set of 91 features. To enhance the efficiency of our system, we employed an analysis of variance (ANOVA) F test and mutual information scores approach, meticulously selecting the most pertinent features. In the identification phase, we harnessed the power of cutting-edge deep learning models, notably the Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM). These models underwent rigorous training and testing to accurately discern individuals based on their handwriting characteristics. Moreover, our methodology introduces a groundbreaking hybrid model that synergizes CNN and BiLSTM, capitalizing on fine motor features for enhanced individual classifications. Crucially, our experimental results underscore the superiority of our approach. The CNN, BiLSTM, and hybrid models exhibited superior performance in individual classification when compared to prevailing stateof-the-art techniques. This validates our method's efficacy and underscores its potential to outperform existing 1690 CMES, 2024, vol.140, no.2 technologies, marking a significant stride forward in the realm of individual identification through handwriting analysis.

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“…However, Kinect can only locate joint hand points, making capturing hand details difficult. After using the depth image information obtained by Kinect to locate the hand, it has to segment the human hand area, select and extract effective features, etc., to complete the recognition of hand gestures [10][11]. The gesture recognition using the data source obtained from Kienct data will improve the recognition accuracy, robustness, and naturalness of the interaction compared to the use of peripheral equipment, such as a general depth camera.…”

Section: Introductionmentioning

confidence: 99%

Kinect-based gesture interaction design method for smart terminal APP interface

Wang,

Cao,

Gao

et al. 2023

Applied Mathematics and Nonlinear Sciences

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In this paper, a static gesture classification model based on a convolutional neural network is proposed to achieve the goal of efficient interaction control and fast response time for gesture recognition, and hand feature extraction is achieved by using alternating convolution and pooling. In the convolutional neural network, image data is obtained by preprocessing operations such as adaptive thresholding and functions such as Gaussian filtering to provide data for the convolutional neural network structure to build the recognition model, which is used to quantify the edge characteristics of the image and construct the image edges with gradient amplitude to build the static gesture recognition algorithm for Kinect. In the accuracy rate for gesture interaction, the average recognition rate for slow movement is 96.5%, while when the interactive gesture is recognized at an accelerated speed, its recognition rate slips to 93.1%, and the total average recognition rate reaches 95.2%.

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Gestural flick input-based non-touch interface for character input

Cited by 8 publications

References 17 publications

Hand Movement Activity-Based Character Input System on a Virtual Keyboard

Hand Movement Activity-Based Character Input System on a Virtual Keyboard

An Enhanced Hybrid Model Based on CNN and BiLSTM for Identifying Individuals via Handwriting Analysis

Kinect-based gesture interaction design method for smart terminal APP interface

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