The objective of this project is to provide healthcare facilities with a tool to monitor physical activities of patients.
The first part of the project concerns activity recognition based on artificial intelligence techniques and the second part is related to visualization of the activity level variations during a time duration.
We consider two types of physical activities: (running and walking). The activity recognition is based on the built-in sensors of a smart phone including accelerometer and GPS sensors to acquire contextual data. After data acquisition phase, the data analysis phase follows to detect activity based on artificial intelligence techniques including machine learning and neural networks. The third phase will be training and testing data and be extracting analytical results. After detecting the type of the activity, it is required to measure its amount and the average speed (running, walking).
The second part of the project is the data visualization. It is required to visually display the analytical results related to several statistics about patient activities.
1. Visual representation of activity levels for each patient on the screen of his smart phone.
2. Visual dashboard on a PC of a caregiver to monitor the variations of activity levels for each patient.
1. The code and a readme file that explains how to compile and run the code.
2. A short report (3 pages max) that explains the theory behind the work and shows how the application works