Papers
6
Total Citations
146
H-Index
5
About
Shixiong Chen is a leading researcher in the fields of myoelectric control, rehabilitation robotics, and human-robot interaction. His work focuses on advancing electromyogram (EMG) pattern-recognition systems to create more intuitive and robust prosthetic and rehabilitation devices. Chen’s major contributions include developing a robust sparse representation-based approach for myoelectric control that effectively mitigates interference from white Gaussian noise—a critical challenge for long-term EMG recordings. He has also pioneered the use of muscle shape change signals for upper-limb movement identification, offering a novel alternative to traditional EMG sensors that are prone to electromagnetic artifacts. His spatio-temporal descriptor for limb movement-intent characterization has further refined feature extraction in EMG pattern-recognition systems, enhancing multi-degree-of-freedom control. With his most-cited paper garnering 52 citations, Chen’s work has significant impact, particularly in improving active motor training for stroke survivors. His research bridges the gap between signal processing and practical rehabilitation, enabling more intuitive and adaptive robotic systems that respond to user intent.
Research Focus
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Top Papers
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