Papers
9
Total Citations
359
H-Index
6
About
Vishwanath Bijalwan’s research sits at the dynamic intersection of human gait analysis, rehabilitation robotics, and smart actuation systems. His most impactful work focuses on decoding human walking patterns using inertial measurement unit (IMU) sensors and machine learning, with his 2021 paper on pattern identification of different human joints for walking styles accumulating 182 citations. He has pioneered optimized feature selection techniques using bio-geography optimization for activity recognition, and developed heterogeneous computing models to restore post-injury walking patterns and assess postural stability. Bijalwan’s contributions extend to deep learning, where he created LSTM models and universal polynomial equations to model all sub-phases of human gait. In robotics, he has designed dexterous robotic hands and caterpillar robots using shape memory alloy (SMA) actuators, and explored EEG-based motion intention detection for rehabilitation. His recent review on exoskeleton actuation mechanisms underscores his forward-looking vision. With over 350 total citations across his most-cited works, Bijalwan is a rising figure in biomechatronics, bridging sensor-driven gait analysis with soft, compliant robotic systems for rehabilitation and assistive technologies.
Research Focus
Key Achievements
Top Papers
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- 7SMA-based caterpillar robot using antagonistic actuation6 citations · 2023
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