The aim of this project is to get you acquainted with machine learning
classification of Human Activity using MATLAB.
You should complete
this project in your groups of 3 or 4 students.
The GROUP will
submit 1 project with all team members listed.
You may discuss the projects with other classmates or on
InstructAssist but each group will
submit their own code for the project.
Step 1: Watch the Machine Learning Made Easy Video from MathWorks: This video provides an overview of how to do classification using the MATLAB machine learner app (a graphical user interface). MATLAB is installed on the zoolab machines. Click [ Here ] to go to the video. I created the following slides, which summarize the webinar. To access these slides, click [ Here ]
Step 2: Download the code from the Webinar:
MathWorks provided the code from the MATLAB webinar. The following
zip file is the same zip file provided by MathWorks.
Download it [ Here ]
Step 3: Run the Webinar Code and study it:
Try to understand the code and all the steps. The code contains
2 examples:
Step 4: Implement new features:
3 features (Mean, Principal Component Analysis and Standard Deviation)
are already implemented. You are required to implement additional
features from the Activity Recognition Paper
Activity Recognition using Cell Phone Accelerometers by
Jennifer R. Kwapisz et al, which we previously studied.
You can find the paper [ Here ]
Specifically, implement the following features in separate MATLAB files:
Step 5: Answer the following questions:
Create a README file and answer the following questions.
Classify the activity using all classifier types that are
available in the classification learner app. What is the
1) most accurate type of classifier and 2) Percentage accuracy
when you use the following features: