Max Robotics — Global Service Robot Directory + US Certification

About

Yachun Wang is a pioneering researcher at the intersection of rehabilitation robotics, deep learning, and surgical automation. Her work centers on developing intelligent systems that restore mobility and enhance surgical precision, with a particular focus on assistive technologies for patients with neurological impairments. Wang’s most impactful contribution is her depth-aware pose estimation framework for exoskeleton gait analysis (2023, 12 citations), which uses deep learning to provide real-time gait monitoring during rehabilitation—a critical innovation for preventing falls and secondary injuries in patients using lower-limb exoskeletons. She has also advanced brain-computer interfaces with her deep learning approach to recognizing hand movement intention from EEG signals in spinal cord injury patients (2020, 6 citations), addressing the longstanding challenge of low accuracy in decoding motor intent. Her recent work extends to surgical robotics, where she developed a hybrid control system combining model-predictive control and fractional-order admittance control for automated internal limiting membrane peeling surgery (2025), tackling the critical challenges of limited force feedback and depth perception in ophthalmic procedures. With additional contributions in stair recognition (StairNetV3, 2024), Wang’s research consistently bridges the gap between algorithmic innovation and real-world clinical application, making her a rising leader in rehabilitation and surgical robotics.