Lyndon Tang
Papers
1
Total Citations
11
H-Index
1
About
Lyndon Tang is a leading researcher in biomechatronics and human motion analysis, with a focus on real-time gait estimation and wearable sensor technologies. His most-cited work, "IMU-Based Real-Time Estimation of Gait Phase Using Multi-Resolution Neural Networks" (2024, 11 citations), introduces a novel approach to gait phase detection using thigh- and shank-mounted inertial measurement units (IMUs). By training a multi-rate convolutional neural network (CNN) on data from 16 participants walking at varying speeds on an instrumented treadmill, Tang achieved high-accuracy, real-time gait phase estimation, a critical advancement for applications in prosthetics, exoskeletons, and rehabilitation robotics. This work demonstrates his ability to bridge machine learning with practical biomechanical sensing, offering robust solutions for dynamic environments. Tang’s contributions are shaping the future of assistive technologies, enabling more responsive and adaptive devices for individuals with mobility impairments. His research, though early in citation impact, underscores a commitment to translating computational models into real-world clinical and wearable applications, marking him as an emerging innovator in the field.
Research Focus
Key Achievements
Top Papers
- 1