Abstract:
Accurate sensing equipment for capturing human hand data is crucial for robot hand teleoperation. Individual calibration processes to reflect individual anatomical differ...Show MoreMetadata
Abstract:
Accurate sensing equipment for capturing human hand data is crucial for robot hand teleoperation. Individual calibration processes to reflect individual anatomical differences in human hands are complex and time-consuming. Additionally, handling noise caused by slipping or impact while wearing sensing equipment is challenging. This paper proposes an efficient calibration system integrating Kernel Principal Component Analysis (Kernel PCA) with hand synergy to overcome these limitations. By utilizing Kernel PCA, the proposed approach enables the reconstruction of both object-grasping and non-grasping hand postures based on human hand synergy. The accuracy of the reconstructed hand postures is evaluated using four principal components (PCs) and comparing data from five different users. The proposed method improves the accuracy and reduces the standard deviation compared to traditional hand synergy using Principal Component Analysis (PCA). Kernel PCA demonstrates high robustness to noise from sensing equipment, ensuring reliable hand posture reproduction under various conditions. The application of this system to an actual robot hand verifies its practical utility, providing reliable control across various users and scenarios.
Date of Conference: 22-24 November 2024
Date Added to IEEE Xplore: 03 December 2024
ISBN Information: