Naveen Kumar Karnam

Indian Institute of Information Technology Sri City, Indian Institute of Information Technology Allahabad

Papers

Total Citations

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About

Naveen Kumar Karnam is a leading researcher in biomedical signal processing and human–machine interaction, with a primary focus on surface electromyography (sEMG) for assistive and rehabilitative technologies. His work centers on developing robust, real-time classification methods for hand gestures and activities of daily living (ADL), directly advancing the fields of prosthetics and wearable robotics. Karnam’s most cited paper, “Classification of sEMG signals of hand gestures based on energy features” (2021, 42 citations), introduced a novel energy-based feature extraction approach that significantly improved gesture recognition accuracy from multi-channel sEMG signals. Building on this, he created the EMAHA-DB1 dataset (2023, 20 citations), a comprehensive, publicly available resource of multi-channel sEMG recordings from 25 able-bodied subjects performing 22 ADL tasks. This dataset has become a benchmark for evaluating ADL classification algorithms, filling a critical gap in the field. Karnam’s contributions have not only yielded high-impact publications but also provided foundational tools that enable more intuitive and responsive prosthetic control, making daily tasks more accessible for individuals with limb differences.