Papers
8
Total Citations
127
H-Index
6
About
Runlin Dong is an emerging researcher specializing in lower limb rehabilitation robotics, brain-computer interfaces (BCI), and human-robot interaction. His work sits at the intersection of neural signal processing, exoskeleton control systems, and intelligent rehabilitation engineering — fields of growing importance as aging populations and neurological injuries drive demand for advanced assistive technologies. Dong's most influential contribution is his comprehensive 2023 review of lower limb exoskeleton robots and cooperative control strategies, which has rapidly accumulated 66 citations, signaling its value as a foundational reference for the field. His research consistently addresses a central challenge: accurately detecting voluntary movement intention from neural and muscular signals — particularly EEG and sEMG — to enable seamless, responsive exoskeleton control. His CNN-LSTM fusion model and readiness potential-based detection approaches represent meaningful advances in decoding human intent with greater precision. Beyond signal processing, Dong has contributed practical innovations in adaptive assist-as-needed control and rehabilitation robot design, ensuring his work translates from theory to clinical application. His exploration of virtual reality as both a rehabilitation tool and a remote control interface further demonstrates his interdisciplinary reach. Collectively, his publications reflect a cohesive research vision: making rehabilitation robots smarter, more intuitive, and ultimately more effective for individuals with motor dysfunction.
Research Focus
Key Achievements
Top Papers
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- 6VR-based remote control system for rescue detection robot in coal mine6 citations · 2017
- 7RP-based Voluntary Movement Intention Detection of Lower limb using CNN4 citations · 2020
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