Nagaswathi Amancherla

Indian Institute of Information Technology Sri City

Papers

Total Citations

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About

Nagaswathi Amancherla’s research sits at the vital intersection of biomedical signal processing and assistive robotics, with a sharp focus on enhancing human-machine interaction. Her most cited work, “SVM based Classification Of sEMG Signals using Time Domain Features for the Applications towards Arm Exoskeletons” (2019), demonstrates her core contribution: developing robust machine learning methods to decode surface electromyography (sEMG) signals for precise control of exoskeleton robots. By systematically comparing time-domain and time-frequency features, she established a reliable classification framework for hand movements, directly improving the responsiveness and accuracy of assistive devices. Though early in her career, this foundational paper has already garnered 5 citations, signaling growing interest in her practical approach to wearable robotics. Amancherla’s work is particularly notable for bridging the gap between raw physiological data and actionable control commands, offering a scalable pathway for more intuitive exoskeleton interfaces. Her research holds promise for rehabilitation engineering and human augmentation, making her a rising voice in the field of intelligent prosthetics and assistive technologies.