A Method for Calculating Lower Extremity Anatomical Landmark Trajectories Based on Inertial Motion Capture Data

Wang, Zhengtao; Gao, Fei; Wu, Zihao; Wang, Dongmei; Guo, Xin; Yu, Shuhui

doi:10.1109/tnsre.2023.3285924

Cited by 3 publications

(1 citation statement)

References 32 publications

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“…In recent years, human motion capture has garnered considerable attention, owing to its diverse applications in entertainment, healthcare, and sports industries [1,2]. Accurate motion capture is essential for realistic animation, immersive virtual reality experiences, and a precise biomechanical analysis of human movements [3,4].…”

Section: Introductionmentioning

confidence: 99%

Extreme Learning Machine/Finite Impulse Response Filter and Vision Data-Assisted Inertial Navigation System-Based Human Motion Capture

Xu,

Gao,

Yang

et al. 2023

Micromachines

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To obtain accurate position information, herein, a one-assistant method involving the fusion of extreme learning machine (ELM)/finite impulse response (FIR) filters and vision data is proposed for inertial navigation system (INS)-based human motion capture. In the proposed method, when vision is available, the vision-based human position is considered as input to an FIR filter that accurately outputs the human position. Meanwhile, another FIR filter outputs the human position using INS data. ELM is used to build mapping between the output of the FIR filter and the corresponding error. When vision data are unavailable, FIR is used to provide the human posture and ELM is used to provide its estimation error built in the abovementioned stage. In the right-arm elbow, the proposed method can improve the cumulative distribution functions (CDFs) of the position errors by about 12.71%, which shows the effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Extreme Learning Machine/Finite Impulse Response Filter and Vision Data-Assisted Inertial Navigation System-Based Human Motion Capture

Xu,

Gao,

Yang

et al. 2023

Micromachines

View full text Add to dashboard Cite

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SR-POSE: A Novel Non-Contact Real-Time Rehabilitation Evaluation Method Using Lightweight Technology

Zhang,

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Rehabilitation movement assessment often requires patients to wear expensive and inconvenient sensors or optical markers. To address this issue, we propose a non-contact and real-time approach using a lightweight pose detection algorithm-Sports Rehabilitation-Pose(SR-Pose), and a depth camera for accurate assessment of rehabilitation movement. Our approach utilizes an E-Shufflenet network to extract underlying features of the target, a RLE-Decoder module to directly regress the coordinate values of 16 key points, and a Weight Fusion Unit (WFU) module to output optimal human posture detection results. By combining the detected human pose information with depth information, we accurately calculate the angle between each joint in three-dimensional space. Furthermore, we apply the DTW algorithm to solve the distance measurement and matching problem of video sequences with different lengths in rehabilitation evaluation tasks. Experimental results show that our method can detect human joint nodes with an average detection speed of 14.32ms and an average detection accuracy for pose of 91.2%, demonstrating its computational efficiency and effectiveness for practical application. Our proposed approach provides a low-cost and user-friendly alternative to traditional sensor-based methods, making it a promising solution for rehabilitation movement assessment.

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Kinematic Calibration Under the Expectation Maximization Framework for Exoskeletal Inertial Motion Capture System

Qin,

Guo,

et al. 2024

J. of Syst. Eng. Electron.

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A Method for Calculating Lower Extremity Anatomical Landmark Trajectories Based on Inertial Motion Capture Data

Cited by 3 publications

References 32 publications

Extreme Learning Machine/Finite Impulse Response Filter and Vision Data-Assisted Inertial Navigation System-Based Human Motion Capture

Extreme Learning Machine/Finite Impulse Response Filter and Vision Data-Assisted Inertial Navigation System-Based Human Motion Capture

SR-POSE: A Novel Non-Contact Real-Time Rehabilitation Evaluation Method Using Lightweight Technology

Kinematic Calibration Under the Expectation Maximization Framework for Exoskeletal Inertial Motion Capture System

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