In this project, input video streams from warehouse CCTV cameras are processed to provide real-time notifications and alerts. It involves detection and tracking of multiple objects and several other use cases such as obstruction detection, detection of violations, to streamline work process and ensure safety.

Using inertial sensors (accelerometer, gyroscope, and magnetometer) embedded in smartphones, the goal of the project is to identify the current physical activity of an individual. The objective is to use a less obtrusive and more pervasive approach unlike video-based and wearable sensor-based technologies for the same use case. The application extends to Ambient-Assisted Living (AAL) for the elderly, where a mobile app is capable of detecting sudden falls and notify emergency-response teams promptly.

This project is for a health-monitoring app by a start up company in Asia. The app seeks to personalize the users' emotional, mental, and physical needs by monitoring food intake, provide coaching from actual psychologists and tracking health goals.

Application of machine learning and deep learning techniques to perform Natural Language Processing tasks such as Text Classification, Text Summarization and Topic Modelling.

A mobile app to automate urinalysis testing using machine vision.