|Title:||IMU-based Estimation of Lower Limb Motion Trajectory with Graph Convolution Network||Authors:||Chen Y
|Keywords:||deep learning;Kinematics;Legged locomotion;lower limb joint trajectory estimation;Pollution measurement;posture estimation;posture tracking trajectory;Sensors;Three-dimensional displays;Tracking;Trajectory;Convolution;Deep learning;Joints (anatomy);Medical applications;Motion estimation;Patient rehabilitation;Three dimensional displays;Trajectories;Deep learning;Joint trajectories;Legged locomotion;Low limb joint trajectory estimation;Lower limb;Pollution measurement;Posture estimation;Posture tracking;Posture tracking trajectory;Three-dimensional display;Tracking;Tracking trajectory;Trajectory estimation;Inertial navigation systems||Issue Date:||2021||Source:||IEEE Sensors Journal||Abstract:||
In recent years, the motion capture system has received a lot of attention because of its wide application, such as movie animation, sport and medical applications. In the field of rehabilitation, motion capture systems are often used to collect the motion information of the patient when he/she is performing rehabilitation tasks. It can be used to quantify the patient’s rehabilitation effectiveness and provide the physician with more objective data as a reference. Using inertial sensors is one of motion capture methods. However, the major challenge to reconstruct accurate human posture with sensor measurements are signal noise, bias and inaccurate gyroscope estimation during long-term wearing. Based on the reason mentioned above, this paper proposes a graph convolutional network (GCN)-based deep learning architecture that uses effective information collected by inertial sensors to predict a sequence of position of each joint of a human’s lower limbs in the 3D space throughout the entire motion. Such motion prediction result based on the deep learning model is to reduce the joint’s tracking error relying only on direct calculations based on measurements of inertial sensors. In the final experiment, the lower limb motion trajectory during walking is used to verify that the method proposed in this work can actually outperform the traditional ones as just mentioned by achieving 2.9 mm drop in mean per joint position error (MPJPE) in 3.6M dataset, resulting in 12.2 mm in MPJPE, and mitigate the drift in real scenario. In our future work, we plan to verify the effectiveness of the proposed model and system for rehabilitation through clinical studies. IEEE
|Appears in Collections:||資訊工程學系|
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