Hand Posture Recognition Using Skeletal Data and Distance Descriptor

Kapuściński, Tomasz; Warchoł, Dawid

doi:10.3390/app10062132

Cited by 10 publications

(3 citation statements)

References 43 publications

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“…Another research utilized a deep neural network on RGB images with a squeezenet architecture to make it suitable for mobile devices and achieved an overall accuracy of 83.28% [ 40 ]. Skeletal data and distance descriptors with TreeBag and neural network (NN) classifiers have been utilized to achieve 90.7% accuracy [ 41 ]. Another work proposed a recognition system for the sign language alphabet that utilizes geometrical features with an artificial neural network and achieved 96.78% accuracy [ 42 ].…”

Section: Literature Reviewmentioning

confidence: 99%

American Sign Language Alphabet Recognition by Extracting Feature from Hand Pose Estimation

Shin

Matsuoka

Hasan

et al. 2021

Sensors

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Sign language is designed to assist the deaf and hard of hearing community to convey messages and connect with society. Sign language recognition has been an important domain of research for a long time. Previously, sensor-based approaches have obtained higher accuracy than vision-based approaches. Due to the cost-effectiveness of vision-based approaches, researchers have been conducted here also despite the accuracy drop. The purpose of this research is to recognize American sign characters using hand images obtained from a web camera. In this work, the media-pipe hands algorithm was used for estimating hand joints from RGB images of hands obtained from a web camera and two types of features were generated from the estimated coordinates of the joints obtained for classification: one is the distances between the joint points and the other one is the angles between vectors and 3D axes. The classifiers utilized to classify the characters were support vector machine (SVM) and light gradient boosting machine (GBM). Three character datasets were used for recognition: the ASL Alphabet dataset, the Massey dataset, and the finger spelling A dataset. The results obtained were 99.39% for the Massey dataset, 87.60% for the ASL Alphabet dataset, and 98.45% for Finger Spelling A dataset. The proposed design for automatic American sign language recognition is cost-effective, computationally inexpensive, does not require any special sensors or devices, and has outperformed previous studies.

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Section: Literature Reviewmentioning

confidence: 99%

American Sign Language Alphabet Recognition by Extracting Feature from Hand Pose Estimation

Shin

Matsuoka

Hasan

et al. 2021

Sensors

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show abstract

“…A wide range of fields, from the human posture classification field considering simple postures such as sitting, standing, and lying down [45,46] to the medical [47,48], industrial [49], and sports [50] fields, have focused on the definition and recognition of human postures. As hardware for collecting posture recognition data, three-dimensional depth cameras [51], smartphones [52,53], and inertial measurement unit sensors [17] are used. In addition, machine learning algorithms such as support vector machine (SVM) [54], CNN [19], and When the welding arc and the welder's body overlap, it is difficult to capture motion accurately because the depth hole obscures the body in the depth image captured by an RGB-D camera.…”

Section: Machine Learning Technique For Posture Recognitionmentioning

confidence: 99%

Recognition of Manual Welding Positions from Depth Hole Image Remotely Sensed by RGB-D Camera

Kim

Nam

2021

Applied Sciences

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The proportion of welding work in total man-hours required for shipbuilding processes has been perceived to be significant, and welding man-hours are greatly affected by working posture. Continuous research has been conducted to identify the posture in welding by utilizing the relationship between man-hours and working posture. However, the results that reflect the effect of the welding posture on man-hours are not available. Although studies on posture recognition based on depth image analysis are being positively reviewed, welding operation has difficulties in image interpretation because an external obstacle caused by arcs exists. Therefore, any obstacle element must be removed in advance. This study proposes a method to acquire work postures using a low-cost RGB-D camera and recognize the welding position through image analysis. It removes obstacles that appear as depth holes in the depth image and restores the removed part to the desired state. The welder’s body joints are extracted, and a convolution neural network is used to determine the corresponding welding position. The restored image showed significantly improved recognition accuracy. The proposed method acquires, analyzes, and automates the recognition of welding positions in real-time. It can be applied to all areas where image interpretation is difficult due to obstacles.

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“…Automatic hand posture recognition is an important research topic in computer science and there are a large number of * Corresponding author's e-mail: akaraci@kastamonu.edu.tr applications in this area. Some of them include driving support via manually-controlled cockpit instruments, gesture-based consumer electronics and house automation, gaming industry applications, interaction with virtual objects and supporting technologies for disabled individuals [4].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Turkish Sign Language Recognition Using Cascade Voting Approach with Handcrafted Features

Karacı

Akyol

Turut

2021

Applied Computer Systems

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In this study, a machine learning-based system, which recognises the Turkish sign language person-independent in real-time, was developed. A leap motion sensor was used to obtain raw data from individuals. Then, handcraft features were extracted by using Euclidean distance on the raw data. Handcraft features include finger-to-finger, finger -to-palm, finger -to-wrist bone, palm-to-palm and wrist-to-wrist distances. LR, k-NN, RF, DNN, ANN single classifiers were trained using the handcraft features. Cascade voting approach was applied with two-step voting. The first voting was applied for each classifier’s final prediction. Then, the second voting, which voted the prediction of all classifiers at the final decision stage, was applied to improve the performance of the proposed system. The proposed system was tested in real-time by an individual whose hand data were not involved in the training dataset. According to the results, the proposed system presents 100 % value of accuracy in the classification of one hand letters. Besides, the recognition accuracy ratio of the system is 100 % on the two hands letters, except “J” and “H” letters. The recognition accuracy rates were 80 % and 90 %, respectively for “J” and “H” letters. Overall, the cascade voting approach presented a high average classification performance with 98.97 % value of accuracy. The proposed system enables Turkish sign language recognition with high accuracy rates in real time.

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Hand Posture Recognition Using Skeletal Data and Distance Descriptor

Cited by 10 publications

References 43 publications

American Sign Language Alphabet Recognition by Extracting Feature from Hand Pose Estimation

American Sign Language Alphabet Recognition by Extracting Feature from Hand Pose Estimation

Recognition of Manual Welding Positions from Depth Hole Image Remotely Sensed by RGB-D Camera

Real-Time Turkish Sign Language Recognition Using Cascade Voting Approach with Handcrafted Features

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