Qiaoling Chen

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About

Qiaoling Chen’s research centers on rehabilitation robotics, multi-modal sensor fusion, and human-robot interaction, with a focus on improving assistive technologies for patients in exoskeleton systems. Her most cited work, “Physical Extraction and Feature Fusion for Multi-Mode Signals in a Measurement System for Patients in Rehabilitation Exoskeleton” (2018, 9 citations), introduces a novel approach that integrates inertial measurement units (IMUs) with visual measurement systems to create a robust, repeatable fusion measurement framework. This system compensates for the limitations of single-mode data acquisition, enabling more accurate real-time monitoring of patient states during rehabilitation. By combining physical signal extraction with feature-level fusion, Chen’s work enhances the reliability of exoskeleton control and feedback, directly contributing to safer, more adaptive therapy. Her contributions are particularly impactful in addressing the challenge of noisy or incomplete sensor data in clinical settings. With a growing citation record, Chen’s research is gaining recognition in the fields of biomedical engineering and rehabilitation technology, laying groundwork for next-generation exoskeletons that can better interpret and respond to patient movement.