Regular Papers

International Journal of Control, Automation, and Systems 2024; 22(6): 1761-1778

https://doi.org/10.1007/s12555-024-0298-1

© The International Journal of Control, Automation, and Systems

Hand Tracking: Survey

Jinuk Heo, Hyelim Choi, Yongseok Lee, Hyunsu Kim, Harim Ji, Hyunreal Park, Youngseon Lee, Cheongkee Jung, Hai-Nguyen Nguyen, and Dongjun Lee*

Seoul National University

Abstract

Hand tracking is relevant to such a variety of applications including human-robot interaction (HRI), human-computer interaction (HCI), virtual reality (VR), and augmented reality (AR). Accurate and robust hand tracking however is challenging due to the intricacies of dynamic motion within small space and the complex interactions with nearby objects, coupled with the hurdles in real-time hand mesh reconstruction. In this paper, we conduct a comprehensive examination and analysis of existing hand tracking technologies. Through the review of major works in the literature, we have discovered numerous studies employing a diverse array of sensors, leading us to propose their categorization into seven types: vision, soft wearable, encoder, magnetic, inertial measurement unit (IMU), electromyography (EMG), and the fusion of sensor modalities. Our findings indicate that no singular solution surpasses all others, attributing to the inherent limitations of using a single sensor modality. As a result, we assert that integrating multiple sensor modalities presents a viable path toward devising a superior hand tracking solution. Ultimately, this survey paper aims to bolster interdisciplinary research efforts across the spectrum of hand tracking technologies, thereby contributing to the advancement of the field.

Keywords Augmented reality, computer vision, data gloves, exoskeleton gloves, hand tracking, human-computer interaction, human-robot interaction, mixed reality, virtual reality, wearable devices.

Article

Regular Papers

International Journal of Control, Automation, and Systems 2024; 22(6): 1761-1778

Published online June 1, 2024 https://doi.org/10.1007/s12555-024-0298-1

Copyright © The International Journal of Control, Automation, and Systems.

Hand Tracking: Survey

Jinuk Heo, Hyelim Choi, Yongseok Lee, Hyunsu Kim, Harim Ji, Hyunreal Park, Youngseon Lee, Cheongkee Jung, Hai-Nguyen Nguyen, and Dongjun Lee*

Seoul National University

Abstract

Hand tracking is relevant to such a variety of applications including human-robot interaction (HRI), human-computer interaction (HCI), virtual reality (VR), and augmented reality (AR). Accurate and robust hand tracking however is challenging due to the intricacies of dynamic motion within small space and the complex interactions with nearby objects, coupled with the hurdles in real-time hand mesh reconstruction. In this paper, we conduct a comprehensive examination and analysis of existing hand tracking technologies. Through the review of major works in the literature, we have discovered numerous studies employing a diverse array of sensors, leading us to propose their categorization into seven types: vision, soft wearable, encoder, magnetic, inertial measurement unit (IMU), electromyography (EMG), and the fusion of sensor modalities. Our findings indicate that no singular solution surpasses all others, attributing to the inherent limitations of using a single sensor modality. As a result, we assert that integrating multiple sensor modalities presents a viable path toward devising a superior hand tracking solution. Ultimately, this survey paper aims to bolster interdisciplinary research efforts across the spectrum of hand tracking technologies, thereby contributing to the advancement of the field.

Keywords: Augmented reality, computer vision, data gloves, exoskeleton gloves, hand tracking, human-computer interaction, human-robot interaction, mixed reality, virtual reality, wearable devices.

IJCAS
June 2024

Vol. 22, No. 6, pp. 1761~2054

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