| In recent years,the number of individuals with lower limb motor function impairment due to spinal disorders,neurological disorders,car accidents and landslides has been increasing.This impairment has a severe impact on their daily lives and places an additional burden on their families.Therefore,addressing the urgent need to help patients recover their limb function to the greatest extent possible has become a critical issue.With the continuous advancement of technology,motion capture technology has been widely applied in the field of lower limb rehabilitation,enabling doctors to accurately capture the motion posture of patients with lower limb motor function impairment during rehabilitation training.Through in-depth analysis and judgment,doctors can adjust the rehabilitation plan to provide more effective treatment for patients.Due to the susceptibility of motion capture accuracy to factors such as environmental noise and algorithmic issues,the research focus in the field of posture capture has gradually shifted towards investigating how to obtain high-precision motion capture data using limited sensor information.This thesis focuses on the study of filtering algorithms for lower limb motion capture data of patients with motor dysfunction,using Inertial Measurement Units(IMUs)as the basis for motion capture technology.Given the complexity of measurement noise in human motion and the interruptions in wireless data transmission caused by unstable signals and frequency conflicts,this study proposes multiple attitude filtering algorithms to improve the accuracy of lower limb motion capture.The specific research content is as follows:(1)This thesis introduces the types of lower limb motor dysfunction and their rehabilitation training forms.An analysis of the current domestic and international research status of motion capture and attitude filtering algorithms is also presented.After a comprehensive consideration of the advantages and disadvantages of various posture capture technologies,it is decided to utilize IMU to detect lower limb movements in patients with impaired motor function,and a lower limb motion capture strategy based on IMUs is developed.(2)This thesis constructs a lower limb posture capture system platform based on IMU to achieve the capture of lower limb posture in patients with motor function impairment.The composition and functions of the hardware system are described in detail.Using Java language and the Intelli J IDEA platform,an upper computer for the lower limb posture capture system is designed and developed,which can perform functions such as data acquisition and storage as well as posture display.The construction of the lower limb posture capture system platform provides strong support for verifying the effectiveness of the posture filtering algorithm proposed in this thesis.(3)This thesis presents a novel lower limb joint localization method based on Quaternion Kalman Filter(QKF)to enhance the accuracy of IMU-based posture capture and meet the requirements of precise lower limb joint localization in individuals with motor function impairment.This method uses five IMUs fixed on the abdomen,thighs,shins,and other parts of the body to collect lower limb posture information.A QKF model is established to filter the data collected by each IMU,and the filtered data is used to calculate the coordinates of the lower limb joints.The experiment shows that this method can improve the accuracy of lower limb posture capture and achieve precise positioning of lower limb joints.(4)This thesis proposes a knee joint angle measurement method based on Extended Kalman Filter(EKF)to meet the measurement requirements of knee joint angle.This method constructs an EKF model based on the constrained relationship of the knee joint angle,and uses EKF to fuse the posture data of the IMUs fixed on the thigh and shin.Based on this,a knee joint angle calculation method based on IMUs is proposed,and the knee joint angle is calculated using the filtered posture data.It is demonstrated that the method can effectively improve the measurement accuracy of the knee angle compared with the Fourth Order Runge-Kutta(RK4)algorithm and QKF.(5)This thesis presents the design and improvement of the Dual Predictive Kalman Filter(DPKF)posture filtering algorithm,which aims to enhance the accuracy of IMU-based posture capture and address the issue of missing IMU data.Inertial posture acquisition systems rely on wireless communication technology for data transmission,which is susceptible to signal instability and frequency band interference,potentially leading to disruptions in IMU data transmission.This method employs the Predictive Kalman Filter(PKF)to estimate and filter the missing quaternions and angular velocities resulting from wireless transmission failures,effectively restoring the lost data and enhancing the integrity and accuracy of the dataset.At the same time,the Complementary Filter(CF)is introduced to compensate for the angular velocity error.To further improve the posture capture accuracy,the filtered angular velocity and quaternion are smoothed using Rauch-Tung-Striebel(R-T-S)backward smoothing.The experiment proves that the DPKF and its improved algorithm can effectively improve the accuracy of human lower limb posture capture,and can effectively supplement missing data when IMU data is missing.In summary,this thesis utilizes a lower limb posture capture system platform to capture the lower limb posture of patients with lower limb motor function impairment.Multiple posture filtering algorithms are designed to cater to different needs.The proposed lower limb joint positioning method based on QKF improves the accuracy of lower limb posture capture and achieves precise positioning of lower limb joints.The proposed knee joint angle measurement method based on EKF addresses the issue of joint angle measurement and exhibits higher accuracy when compared to QKF and RK4.The proposed DPKF and its improved algorithm not only enhance the lower limb posture capture accuracy.accuracy but also effectively augment missing IMU data. |
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