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Hand Gesture Recognition Based on Auto-Landmark Localization and Reweighted Genetic Algorithm for Healthcare Muscle Activities

Author

Listed:
  • Hira Ansar

    (Department of Computer Science, Air University, Islamabad 44000, Pakistan)

  • Ahmad Jalal

    (Department of Computer Science, Air University, Islamabad 44000, Pakistan)

  • Munkhjargal Gochoo

    (Department of Computer Science and Software Engineering, United Arab Emirates University, Al Ain 15551, United Arab Emirates)

  • Kibum Kim

    (Department of Human-Computer Interaction, Hanyang University, Ansan 15588, Korea)

Abstract

Due to the constantly increasing demand for the automatic localization of landmarks in hand gesture recognition, there is a need for a more sustainable, intelligent, and reliable system for hand gesture recognition. The main purpose of this study was to develop an accurate hand gesture recognition system that is capable of error-free auto-landmark localization of any gesture dateable in an RGB image. In this paper, we propose a system based on landmark extraction from RGB images regardless of the environment. The extraction of gestures is performed via two methods, namely, fused and directional image methods. The fused method produced greater extracted gesture recognition accuracy. In the proposed system, hand gesture recognition (HGR) is done via several different methods, namely, (1) HGR via point-based features, which consist of (i) distance features, (ii) angular features, and (iii) geometric features; (2) HGR via full hand features, which are composed of (i) SONG mesh geometry and (ii) active model. To optimize these features, we applied gray wolf optimization. After optimization, a reweighted genetic algorithm was used for classification and gesture recognition. Experimentation was performed on five challenging datasets: Sign Word, Dexter1, Dexter + Object, STB, and NYU. Experimental results proved that auto landmark localization with the proposed feature extraction technique is an efficient approach towards developing a robust HGR system. The classification results of the reweighted genetic algorithm were compared with Artificial Neural Network (ANN) and decision tree. The developed system plays a significant role in healthcare muscle exercise.

Suggested Citation

  • Hira Ansar & Ahmad Jalal & Munkhjargal Gochoo & Kibum Kim, 2021. "Hand Gesture Recognition Based on Auto-Landmark Localization and Reweighted Genetic Algorithm for Healthcare Muscle Activities," Sustainability, MDPI, vol. 13(5), pages 1-26, March.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:5:p:2961-:d:513321
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    References listed on IDEAS

    as
    1. Ahmad Jalal & Mouazma Batool & Kibum Kim, 2020. "Sustainable Wearable System: Human Behavior Modeling for Life-Logging Activities Using K-Ary Tree Hashing Classifier," Sustainability, MDPI, vol. 12(24), pages 1-21, December.
    2. Ahmad Jalal & Israr Akhtar & Kibum Kim, 2020. "Human Posture Estimation and Sustainable Events Classification via Pseudo-2D Stick Model and K-ary Tree Hashing," Sustainability, MDPI, vol. 12(23), pages 1-24, November.
    3. Ghada Aldabbagh & Daniyal M. Alghazzawi & Syed Hamid Hasan & Mohammed Alhaddad & Areej Malibari & Li Cheng, 2020. "Optimal Learning Behavior Prediction System Based on Cognitive Style Using Adaptive Optimization-Based Neural Network," Complexity, Hindawi, vol. 2020, pages 1-13, November.
    4. Shiming Dai & Wei Liu & Wenji Yang & Lili Fan & Jihao Zhang, 2020. "Cascaded Hierarchical CNN for RGB-Based 3D Hand Pose Estimation," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-13, July.
    5. Juan Cheng & Fulin Wei & Yu Liu & Chang Li & Qiang Chen & Xun Chen, 2020. "Chinese Sign Language Recognition Based on DTW-Distance-Mapping Features," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-13, August.
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    Cited by:

    1. Mahwish Pervaiz & Yazeed Yasin Ghadi & Munkhjargal Gochoo & Ahmad Jalal & Shaharyar Kamal & Dong-Seong Kim, 2021. "A Smart Surveillance System for People Counting and Tracking Using Particle Flow and Modified SOM," Sustainability, MDPI, vol. 13(10), pages 1-20, May.

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