Institute of Nuclear Medicine and Allied Sciences-DRDO, Ministry of Defence · Machine Learning Research Intern
- Achieved 98% accuracy by developing a ResNet, CNNs to classify surface electromyography (sEMG) stress measurements.
- Improved classification performance from 91% to 97.98% through architecture redesign and training, showing raw sEMG inputs outperform PyEMGPipeline preprocessing.
- Implemented AlexNet alongside ResNet experiments to further refine accuracy and generalization across sEMG datasets.
- Selected to present this research on stress-level classification at the SUMMIT 2.0 Conference.