Data acquisition with wearable sensors, deep learning modelling, machine learning, signal processing, human activity recognition

Sensors and Transducers, signals and systems, control systems, digital signal processing, electronics devices and circuits, Microprocessors and Microcontrollers, digital electronics, electrical and electronics measurements, deep learning/python/machine learning, data Acquisition systems, wearable sensors, ECG, EMG and EEG signalling and processing

GATE- 2011/2013/2018

1. verma, U., Tyagi, P., & Aneja, M. K. (2023). Multi-Branch CNN GRU with attention mechanism for Human Action Recognition. Engineering Research Express.
2. Verma, U., Tyagi, P., & Kaur, M. (2023). Artificial intelligence in human activity recognition: a review. International Journal of Sensor Networks, 41(1), 1-22.
3. Verma, U., Tyagi, P., & Kaur, M. (2021, December). Smartphone-based CNN-GRU Framework for Human Activity Recognition. In 2021 IEEE 2nd International Conference On Electrical Power and Energy Systems (ICEPES) (pp. 1-4). IEEE.
4. Verma, U., Tyagi, P., & Kaur, M. (2022). Single Input Single Head CNN-GRU-LSTM Architecture for Recognition of Human Activities. Indonesian Journal of Electrical Engineering and Informatics (IJEEI), 10(2), 410-420.
5. Verma, U., Tyagi, P., & Kaur, M. (2023, January). Wearable Sensor-Based Framework for the Detection of Daily Living Activities Utilizing In-Depth Features. In Machine Learning, Image Processing, Network Security and Data Sciences: Select Proceedings of 3rd International Conference on MIND 2021 (pp. 131-155). Singapore: Springer Nature Singapore.
6. verma, updesh, Pratibha Tyagi, and Manpreet Kaur Aneja. "Multi-Head CNN-Based Activity Recognition and Its Application on Chest-Mounted Sensor-Belt." Engineering Research Express (2024).
7. Verma, U, Tyagi, & Kaur, M. (2024, August). Convolutional Neural Network Baes Human Activity Recognition. In International Conference on Science, Engineering & Technology (ICSET) (pp. 8-12).