This study basically presents a review of robotic hands and its applications based on human hand motion. In the context of the review, different kinds of articles are analyzed. There are some hand motion-sensing methods, which are noncontact and direct contact to the hand. Wearable technology like data gloves can be shown as an example of the direct-contact method and cameras are examples of the non-contact sensing method. As indicated in the articles, both methods have advantages and disadvantages. Based on the given information in the researches, the methods used for detecting the hand motion are evaluated and compared. After comparing those methods, how the obtained data is transferred to the robotic hand and the algorithms used for this purpose are expressed. Also what the role of the machine learning at this point is underlined. Furthermore, how the accuracy and robustness level changes by using machine learning are demonstrated according to given results in those articles. Additionally, application areas of these robot hands are observed. Some examples of applications such as human-robot collaboration, robot hands for physical rehabilitation and service purposes are explained. Finally, futuristic ideas and challenges of on-going studies are discussed.

Keywords - Human hand motion, contact-based sensing, non-contact sensing, robotic hand, machine-learning

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