Practical sEMG-based motion prediction for rehabilitation robotics faces two critical challenges: low robustness caused by inter-subject variability and signal non-stationarity, and mechanical safety hazards due to jittery control signals. Conventional Temporal Convolutional Network(TCN)-based methods often prioritize...
Junlin Jiang, Pengcheng Li, Jun Leng et al.· 2026 IEEE International Conf...· 0 citations
Accurate vascular segmentation plays a critical role in medical image analysis, supporting disease diagnosis, surgical navigation, and treatment planning. However, existing methods often struggle to preserve fine vascular structures and maintain topological continuity due to low contrast, complex backgrounds, and scale...
Huiyin Xu, Shu-Xiang Guo, Pengcheng Li et al.· 2026 IEEE International Conf...· 0 citations
To address the demand for lightweight intention recognition algorithms in home-based portable upper limb rehabilitation robots, this paper presents a lightweight upper limb motion intention recognition scheme based on bimodal fusion of surface electromyography (sEMG) and inertial measurement unit (IMU) signals. Time-do...
Qian Yang, Shuxiang Guo, Hengrui Li et al.· 2026 IEEE International Conf...· 0 citations
Home-based rehabilitation exoskeletons often suffer from control instability due to low-cost force sensors. This paper presents a robust, sensorless Composite Variable Impedance Control architecture that separates trajectory tracking (virtual stiffness K) from active assistance (adaptive feedforward torque τassist). By...
Jun Leng, Pengcheng Li, Hanze Wang et al.· 2026 IEEE International Conf...· 0 citations
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