Patient Monitoring and Tracking System
Although depth sensors are widely utilized in automation applications, the capability of those sensors to capture data regardless of lighting conditions enables computer vision scientists to adopt those sensors in their applications. The system developed in this project can perform recognition of activities such as walking, standing,sitting, lying, falling or SOS gesture belonging to individuals in the scene with high accuracy. Relying on the face recognition api developed by Kuartis, the system can distinguish individuals day and night and record personal motion histories. Moreover, the heart rate module can report heart rates up to a certain accuracy.
Computer Vision, Image Processing, Machine Learning