Like all great things, a plan proceeds it. I chose to use flowcharts as my primary method of planning. This is because I could compartmentalize each segment of the plan to troubleshoot and rethink ideas. I both needed and wanted to understand how my raw data collected would result in something useful. How could I read and dissect patterns? By creating an algorithm of course!
Enter Python, stage left.
By taking raw data and finding an approximate average range for motion, I could determine how successful motion has been. For instance, if raising one's arm has lots of motion from side to side, something is a miss. I could use this basic idea, tailored to different ADLs (for info on these, read week 1!) in order to determine the smoothness of motion.
As previously, I chose to focus on the drinking motion. By mapping the arm using 8 sensors, as well as a sensor on the cup, I was able to collect position data for each point on the test subject arm, in this case, mine.
Not all plans go according to...plan. Excuse the wording here but I am right. The algorithm worked for measuring the effectiveness of motion. But the aim of the research project is to compile possible motion capture techniques. This threw a curveball as different systems use different programming languages as standard.
Most problems have solutions. Mine was this: To use different capture devices for different actions and properties of motion. In this case the Optitrak system for coordinate data and the Azure Kinect DK for acceleration and jerk.
Setting up the Kinect system will be the task and topic in week 3 so stay tuned!
Arron J Thompson