Design and prototyping of a robotic hand for sign language using locally-sourced materials

被引:1
|
作者
Adeyanju, Ibrahim A. [1 ]
Alabi, Sheriffdeen O. [1 ]
Esan, Adebimpe O. [1 ]
Omodunbi, Bolaji A. [1 ]
Bello, Oluwaseyi O. [2 ]
Fanijo, Samuel [3 ]
机构
[1] Fed Univ, Dept Comp Engn, Ekiti, Nigeria
[2] Ekiti State Univ, Dept Comp Engn, Ekiti, Nigeria
[3] Iowa State Univ, Dept Comp Sci, Ames, IA USA
关键词
Android; Communication; Deaf; Disability; Dumb; Hardware; Mobile app; Robotics; Sign language; Speech recognition;
D O I
10.1016/j.sciaf.2022.e01533
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
People living with disability constitute a significant percentage of the world population. For many people with disabilities, assistance and support are prerequisites for participating in societal activities. This research work developed a hardware prototype of a robotic hand forfor sign language communication with persons living with hard-of-hearing disabilities (deaf and/or dumb). The prototype has three basic modules: the input unit, the control unit, and the robotic hand. The input unit is designed as an Android-based mobile application with speech recognition capabilities while the control unit is ATMEGA 2560 microcontroller board. The robotic hand is constructed using locally available materials (bathroom Slippers, expandable rubber, straw pipe, and tiny rope) together with three servo motors and is designed to look and perform movements similar to a human hand. The prototype was evaluated quantitatively in terms of empirical accuracy and response time. It was also evaluated qualitatively by thirty-five (35) users which included fifteen (15) experience ASL users, eighteen (18) non-experience ASL users, and two (2) ASL experts, who completed questionnaires to rate the prototype on a 5-point Likert scale in terms of five parameters: functionality, reliability, ease of use, efficiency, and portability. An accuracy of 78.43% with an average response time of 2 s was obtained from empirical experiments. Statistical analysis of user responses showed that 97%, 68%, 77%, 80%, and 83% of users rated the system as above average for functionality, reliability, ease of use, efficiency, and portability, respectively. The robotic hand effectively communicates American Sign Language which includes English Alphabets, numbers (1-9), and some selected common words, which can be demonstrated with a single hand for hard of hearing persons. To the best of our knowledge, this work is the first ASL robotic hand that is based on locally sourced cost-effective materials, and we build on flaws from existing literature, most of which are either template-based, not real-time, or expensive. In terms of future work, the prototype can be improved by extending the single robotic hand to a fully robotic body with two hands. (c) 2022 The Authors. Published by Elsevier B.V.
引用
收藏
页数:15
相关论文
共 50 条
  • [31] Isolated sign language recognition using Convolutional Neural Network hand modelling and Hand Energy Image
    Kian Ming Lim
    Alan Wee Chiat Tan
    Chin Poo Lee
    Shing Chiang Tan
    Multimedia Tools and Applications, 2019, 78 : 19917 - 19944
  • [32] Sign Language Recognition Using Image Based Hand Gesture Recognition Techniques
    Nikam, Ashish S.
    Ambekar, Aarti G.
    PROCEEDINGS OF 2016 ONLINE INTERNATIONAL CONFERENCE ON GREEN ENGINEERING AND TECHNOLOGIES (IC-GET), 2016,
  • [33] Recognizing Hand Configurations of Brazilian Sign Language Using Convolutional Neural Networks
    Oliveria, A. S.
    Costa Filho, C. F. F.
    Costa, M. G. F.
    XXVI BRAZILIAN CONGRESS ON BIOMEDICAL ENGINEERING, CBEB 2018, VOL. 2, 2019, 70 (02): : 421 - 427
  • [34] Classification of Hand Gesture in Indonesian Sign Language System using Naive Bayes
    Pramunanto, Eko
    Sumpeno, Surya
    Legowo, Rafiidha Selyna
    2017 INTERNATIONAL SEMINAR ON SENSORS, INSTRUMENTATION, MEASUREMENT AND METROLOGY (ISSIMM), 2017, : 187 - 191
  • [35] Arabic sign language recognition using vision and hand tracking features with HMM
    Sidig A.A.I.
    Luqman H.
    Mahmoud S.A.
    International Journal of Intelligent Systems Technologies and Applications, 2019, 18 (05) : 430 - 447
  • [36] Static hand gesture recognition for American sign language using neuromorphic hardware
    Mohammadi, Mohammadreza
    Chandarana, Peyton
    Seekings, James
    Hendrix, Sara
    Zand, Ramtin
    NEUROMORPHIC COMPUTING AND ENGINEERING, 2022, 2 (04):
  • [37] Design of a Sign Language-to-Natural Language Translator Using Artificial Intelligence
    Gonzalez, Hernando
    Hernandez, Silvia
    Calderon, Oscar
    INTERNATIONAL JOURNAL OF ONLINE AND BIOMEDICAL ENGINEERING, 2024, 20 (03) : 89 - 98
  • [38] Laser Scribed Graphene Biosensor for Detection of Biogenic Amines in Food Samples Using Locally Sourced Materials
    Vanegas, Diana C.
    Patino, Laksmi
    Mendez, Connie
    de Oliveira, Daniela Alves
    Torres, Alba M.
    Gomes, Carmen L.
    McLamore, Eric S.
    BIOSENSORS-BASEL, 2018, 8 (02):
  • [39] The design of hand gestures for human-computer interaction: Lessons from sign language interpreters
    Rempel, David
    Camilleri, Matt J.
    Lee, David L.
    INTERNATIONAL JOURNAL OF HUMAN-COMPUTER STUDIES, 2014, 72 (10-11) : 728 - 735
  • [40] Development of a Robotic Hand Using Bioinspired Optimization for Mechanical and Control Design: UnB-Hand
    Pertuz, Sergio A.
    Llanos, Carlos H.
    Munoz, Daniel M.
    IEEE ACCESS, 2021, 9 (09): : 61010 - 61023