Papers
5
Total Citations
110
H-Index
4
About
Anish C. Turlapaty is a biomedical signal processing researcher whose work centers on surface electromyography (sEMG) analysis, human activity recognition, and assistive robotics. His research addresses a critical challenge in rehabilitation engineering: enabling accurate, data-driven control of prosthetics and exoskeletons through intelligent classification of muscle signals. Turlapaty has made significant contributions to the field by developing and benchmarking machine learning frameworks — including SVM classifiers, energy-based feature extraction, and transformer-based transfer learning — to decode hand gestures and activities of daily living from sEMG data. A landmark achievement is his development of EMAHA-DB1, a publicly available multi-channel sEMG dataset capturing 22 activities across 25 subjects, providing the research community with a valuable resource for advancing human-machine interaction studies. His most-cited works, including papers on energy feature classification (42 citations) and physical action recognition (41 citations), reflect strong community uptake of his methodologies. With applications spanning robot-assisted rehabilitation, arm exoskeletons, and prosthetic control, Turlapaty's research directly supports improving quality of life for individuals living with limb disabilities and gait disorders.
Research Focus
Key Achievements
Top Papers
- 1Classification of sEMG signals of hand gestures based on energy features42 citations · 2021
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