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About

Chenlei Xie is a researcher at the forefront of human-robot interaction and assistive robotics, with a primary focus on mechanomyography (MMG) signal processing and its application in wearable power-assisted devices. His work addresses the critical challenge of enabling intuitive, intent-driven control for exoskeletons and rehabilitation robots, particularly for elderly and disabled individuals with motor dysfunction. Xie’s major contributions include developing a long short-term memory (LSTM) neural network model for knee joint acceleration estimation from MMG signals (2019, 19 citations), which significantly advanced the field by allowing robots to anticipate user movement rather than merely react. He also pioneered the use of multivariate variational mode decomposition for MMG signal processing (2021, 3 citations), improving the accuracy of intent recognition. Notably, his 2019 study on estimating knee extension force through clothing demonstrated the practical feasibility of non-invasive, real-world MMG sensing. With a total of 25 citations across his most-cited works, Xie’s research is foundational for the next generation of wearable power-assist robots that seamlessly integrate with human physiology.