Papers
10
Total Citations
86
H-Index
5
About
Lifu Gao is a robotics and human-machine interaction researcher whose work spans industrial robot kinematics, wearable assistive robotics, and intelligent fault diagnosis. His early contributions established rigorous foundations in robot mechanics, notably developing a geometric approach for inverse kinematics analysis of six-degree-of-freedom serial robots using Denavit-Hartenberg parameters (2015, 21 citations) and a geometric constraint-based self-calibration method for industrial robotic systems. Gao's research subsequently pivoted toward biomechanical signal processing and assistive technologies for the elderly and motor-impaired, producing influential work on knee joint acceleration estimation using long short-term memory neural networks with mechanomyography signals (2020, 19 citations) and continuous knee angle estimation through CNN-SVM models. His investigations into electromyography-based force prediction, particularly elbow flexion force forecasting using the Informer model (2021, 11 citations), demonstrate a sustained commitment to enhancing wearable power-assisted robot control. More recently, Gao has addressed industrial automation challenges, developing an EEMD-MPA-KELM model for rotate vector reducer fault diagnosis (2023, 14 citations) and advancing variable impedance control strategies for robotic polishing of unmodeled workpieces. His cumulative body of work bridges theoretical robotics with practical biomedical and manufacturing applications.
Research Focus
Key Achievements
Top Papers
- 1Geometric approach for inverse kinematics analysis of 6-Dof serial robot21 citations · 2015
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- 3Rotate Vector Reducer Fault Diagnosis Model Based on EEMD-MPA-KELM14 citations · 2023
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- 9A self-calibration method for robot based on geometric constraints3 citations · 2016
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