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  1. Article ; Online: Recognition of Uni-Stroke Characters with Hand Movements in 3D Space Using Convolutional Neural Networks.

    Chang, Won-Du / Matsuoka, Akitaka / Kim, Kyeong-Taek / Shin, Jungpil

    Sensors (Basel, Switzerland)

    2022  Volume 22, Issue 16

    Abstract: Hand gestures are a common means of communication in daily life, and many attempts have been made to recognize them automatically. Developing systems and algorithms to recognize hand gestures is expected to enhance the experience of human-computer ... ...

    Abstract Hand gestures are a common means of communication in daily life, and many attempts have been made to recognize them automatically. Developing systems and algorithms to recognize hand gestures is expected to enhance the experience of human-computer interfaces, especially when there are difficulties in communicating vocally. A popular system for recognizing hand gestures is the air-writing method, where people write letters in the air by hand. The arm movements are tracked with a smartwatch/band with embedded acceleration and gyro sensors; a computer system then recognizes the written letters. One of the greatest difficulties in developing algorithms for air writing is the diversity of human hand/arm movements, which makes it difficult to build signal templates for air-written characters or network models. This paper proposes a method for recognizing air-written characters using an artificial neural network. We utilized uni-stroke-designed characters and presented a network model with inception modules and an ensemble structure. The proposed method was successfully evaluated using the data of air-written characters (Arabic numbers and English alphabets) from 18 people with 91.06% accuracy, which reduced the error rate of recent studies by approximately half.
    MeSH term(s) Algorithms ; Gestures ; Hand ; Humans ; Neural Networks, Computer ; Pattern Recognition, Automated/methods ; Stroke
    Language English
    Publishing date 2022-08-16
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s22166113
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: American Sign Language Alphabet Recognition by Extracting Feature from Hand Pose Estimation.

    Shin, Jungpil / Matsuoka, Akitaka / Hasan, Md Al Mehedi / Srizon, Azmain Yakin

    Sensors (Basel, Switzerland)

    2021  Volume 21, Issue 17

    Abstract: 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 ... ...

    Abstract 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.
    MeSH term(s) Algorithms ; Fingers ; Hand ; Humans ; Recognition, Psychology ; Sign Language ; United States
    Language English
    Publishing date 2021-08-31
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s21175856
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

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