Yingying Song

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About

Yingying Song is a rising researcher in the field of wearable robotics and intelligent control systems, with a primary focus on lower-limb exoskeleton technology. Her work centers on solving one of the most critical challenges in exoskeleton development: accurate, real-time gait phase recognition. Song’s major contributions involve the innovative application of deep learning and hybrid machine learning models to decode human motion from inertial measurement unit (IMU) data. She has pioneered the use of CNN and HHO-SVM models for gait phase recognition, achieving improved accuracy essential for compliant exoskeleton control. Her 2024 paper on this topic has already garnered attention, while her subsequent 2025 work extends the approach with a CNN-LSTM architecture to better capture temporal motion dynamics. Though early in her career, Song’s research directly addresses a key bottleneck in assistive robotics—enabling more natural and responsive human-robot interaction. Her work holds promise for applications in rehabilitation, mobility assistance, and augmenting human performance, marking her as a researcher to watch in the rapidly evolving field of intelligent exoskeleton systems.