The wearable technology market has exploded in recent years, coinciding with the rise of the Internet of Things, and this trend shows no signs of slowing down. Due to its widespread use in a wide range of application areas such as healthcare, sports monitoring, fitness, game console design, and smart homes, human movement analysis utilising wearable inertial sensors has become a new research hotspot. These devices' benefits on people's health have gotten a lot of attention. Several studies have shown its effectiveness in controlling people's weight, promoting physical activity adherence, particularly for those who have suffered from heart failure, assessing rehabilitation activities, lowering sedentary behaviour in the elderly, and so on.

Wearable sensors with motion-sensitive elements and specialised circuits that measure small physiological changes in bio-electrical signals induced by changes in the emotional state may be required for stress and emotion recognition. Thus we are modelling Explainable AI model to detect stress.

Students:

Ms. Ruchi Jain

Mr. Ochid Chetia Phukan

This Project can detect the people having Diabetic retinopathy eye disease and the level of Diabetic retinopathy with the help of Deep Learning Models .On the scale of Success this Model gives accuracy of around 91% .

Students:

Sagar Prajapati (BT19ECE007)

Sanyam Jain (BT19ECE020)

Praveen Kumar Singh (BT19ECE012)

A project was developed for detecting COVID-19 cases using Xray and CT scan. We tried to implement CNN-LSTM on both CT-Scan images (2482 images)and X-ray lung images(4626 images) to find out how our CNN-LSTM model is responding to these datasets. A combined method is developed to automatically detect COVID-19 cases using 2 types of X-ray and CT-scan images. The structure of this architecture was designed by combining CNN and LSTM networks, where the CNN is used to extract complex features from images, and LSTM is used as a classifier. In result we got 84% test accuracy using CT-scan images and 91% test accuracy using X-ray images.

Students:

NIKHIL KUMAR SINGH (BT19ECE017)

DUDEKULA RESHMA (BT19ECE021)