Papers
17
Total Citations
338
H-Index
8
About
Siqi Cai is a biomedical engineer and rehabilitation robotics researcher whose work sits at the intersection of human-machine interfaces, motor rehabilitation, and intelligent signal processing. With a focus on upper-limb stroke recovery, Cai has made significant contributions to developing smarter, more responsive rehabilitation systems that can operate with minimal therapist supervision. Cai's most influential work explores the use of surface electromyography (sEMG) and machine learning to decode human movement intent. Their 2019 SVM-based classification study (112 citations) demonstrated how muscle signals could reliably guide rehabilitation robots, while a companion deep learning paper (55 citations) extended this to continuous, multi-dimensional joint angle estimation. Recognizing that unsupervised practice often leads to harmful compensatory movements in stroke patients, Cai pioneered real-time compensation detection systems using pressure distribution and sEMG data, a clinically meaningful advance reflected across multiple publications totaling over 100 additional citations. Their work also spans EEG-based robot control and the mechanical design of dual-arm rehabilitation platforms, underscoring a holistic approach to assistive technology. Collectively, Cai's research addresses a critical gap in autonomous, patient-adaptive rehabilitation, offering practical pathways toward more effective and accessible stroke recovery.
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