COMP255 Human Activity Recognition

This assignment involves the following subtasks:

1. Use Agile to manage this IoT application development (e.g., develop backlog, create sprint, and monitor the sprint progress). The backlog and each sprint along with each week’s sprint progress burndown chart shall be recorded in the final submission document.

2. Based on the given workshop materials, create python code to load data and extract corresponding features from the given dataset.

3. Test and evaluate the two given machine learning models (KNN and SVM) and application in general and record the test results and evaluation summary in the final submission document.

4. Refactor the source code according to the design pattern lecture and make the code easier to understand and extensible. The code shall be managed by GitHub and will be reviewed for this along with GitHub version control history.

The sourcing data is from a public dataset (Dalia dataset [1], which contains 6 sensors’ data for 19 activities), refining that data and cleaning them up, and extracting significant features through statistical analysis for use in artificial intelligence and machine learning systems.

An example code is provided for reference. You may need to learn the use of Python libraries Numpy [2] and Pandas [3]. Machine learning modules using Scikit-learn [4] are given though having some understanding of them is recommended (we will only cover the basics of it to avoid course overlapping).