Jiandong Han

Nanjing University of Science and Technology

Papers

Total Citations

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About

Jiandong Han is a leading researcher in biomedical signal processing and human–machine interaction, with a core focus on decoding motor intent from surface electromyography (sEMG). His most cited work, “Continuous limb joint angle prediction from sEMG using SA-FAWT and Conv-BiLSTM” (2024, 13 citations), introduces a novel hybrid framework that combines signal-adaptive feature extraction with deep learning to achieve accurate, real-time joint angle estimation. This contribution is pivotal for advancing prosthetic control, rehabilitation robotics, and exoskeleton systems, enabling more natural and responsive assistive devices. By integrating spectral analysis and convolutional–bidirectional LSTM networks, Han’s approach addresses long-standing challenges in non-stationary sEMG interpretation, demonstrating robust performance across varied movement conditions. His work has already garnered attention for its potential to bridge the gap between neural signals and fluid mechanical actuation. As a rising scholar, Han continues to push the boundaries of intelligent biosignal analysis, with his research serving as a foundation for next-generation wearable technologies that restore and augment human motor function.