Kalman Filtering
In this paper, we propose a dual-loop control strategy to address the problems of the interference by the human-machine interaction of the lower limb exoskeleton movement. The outer ring adopts admittance control and the human-machine interaction torque is estimated by the generalized momentum observer based on Kalman filter. The inner ring adopts PID control based on DDPG.
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These data is state estimation accuracy of the proposed algorithm When the adjust factor is 1
These data includes the position estimation accuracy and velocity estimation accuracy of the algorithm.
The data are explained as follows:
rmse_ckf_1,rmse_ukf_1,rmse_vakf_1,rmse_vakfpr_1,rmse_okf_1 are the position accuracy of the CKF, UKF, the proposed IW_VACKF, VACKF_PR and CKF-TNCM, respectively.
rmse_ckf_2,rmse_ukf_2,rmse_vakf_2,rmse_vakfpr_2,rmse_okf_2 are the velocity accuracy of the CKF, UKF, the proposed IW_VACKF, VACKF_PR and CKF-TNCM, respectively.
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Human intention is an internal, mental characterization for acquiring desired information. From
interactive interfaces, containing either textual or graphical information, intention to perceive desired
information is subjective and strongly connected with eye gaze. In this work, we determine such intention by
analyzing real-time eye gaze data with a low-cost regular webcam. We extracted unique features (e.g.,
Fixation Count, Eye Movement Ratio) from the eye gaze data of 31 participants to generate the dataset
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