Kalman filter-based deep fused architecture for knee angle estimation
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11042-024-19483-3.pdf
Reference40 articles.
1. Li Ziyou, Zhang Daohui, Zhao Xingang, Wang Fengyan, Zhang Bi, Ye Dan, Han Jianda (2020) A temporally smoothed MLP regression scheme for continuous knee/ankle angles estimation by using multi-channel sEMG. IEEE Access 8:47433–47444. https://doi.org/10.1109/ACCESS.2020.2979008
2. Sun Ning, Cao Menglin, Chen Yuhao, Chen Yanni, Wang Jue, Wang Qiong, Chen Xiaocong, Liu Tian (2022) Continuous estimation of human knee joint angles by fusing kinematic and myoelectric signals. IEEE Trans Neural Syst Rehab Eng 30:2446–2455. https://doi.org/10.1109/TNSRE.2022.3200485
3. Mahdy Eslamy and Mo Rastgaar (2022) Multi-joint leg moment estimation during walking using thigh or shank angles. IEEE Trans Neural Syst Rehab Eng 31:1108–1118. https://doi.org/10.1109/TNSRE.2022.3217680
4. Saiki Y, Kabata T, Ojima T, Kajino Y, Kubo N, Tsuchiya H (2023) Reliability and validity of OpenPose for measuring hip-knee-ankle angle in patients with knee osteoarthritis. Sci Rep 13:3297. https://doi.org/10.1038/s41598-023-30352-1
5. Wang Junhong, Wang Lipeng, Miran Seyed M, Xi Xugang, AnkeXue, (2019) Surface Electromyography Based Estimation of Knee Joint Angle by Using Correlation Dimension of Wavelet Coefficient. IEEE Access 7:60522–60531. https://doi.org/10.1109/ACCESS.2019.2913959
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