Papers
38
Total Citations
1,662
H-Index
23
About
Vijay Bhaskar Semwal is a prominent researcher whose work bridges human motion analysis, wearable sensing technologies, and humanoid robotics. His research spans three interconnected domains: human activity recognition, clinical gait analysis, and bipedal robot locomotion, making him a distinctive voice at the intersection of biomedical engineering and intelligent systems. Semwal's contributions have meaningfully advanced how machines perceive and replicate human movement. His early work on fuzzy logic controllers and vector field-based gait modeling laid theoretical foundations for humanoid push recovery and bipedal walking, while later investigations harnessed deep learning architectures — including CNN-GRU hybrids and ensemble methods — to classify complex motion patterns from wearable IMU sensors. His preimpact fall detection system, capable of identifying a fall within just 0.5 seconds of initiation, represents a particularly impactful achievement for elderly healthcare applications. With his ten most-cited papers collectively accumulating over 1,000 citations, Semwal's influence is both broad and enduring. His clinical gait classification research using Extreme Learning Machines and LSTM-based trajectory generation further demonstrates his ability to translate computational innovation into real-world diagnostic and robotic applications, offering valuable tools for neurological disorder detection and next-generation assistive robotics.
Research Focus
Key Achievements
Top Papers
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- 2Inception inspired CNN-GRU hybrid network for human activity recognition146 citations · 2022
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- 6Clinical Human Gait Classification: Extreme Learning Machine Approach93 citations · 2019
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