Raw basketball data
Basketball data aligned with acceleration maxima
Learn how to effectively throw a ball.
What it does
The Kine-Fanatic allows us to monitor the accelerations of different critical points on the body in movements like throwing, punching, or shooting a basketball. After analyzing multiple actions, we could classify newly recorded actions based on the data we collected.
How we built it
We accomplished data collection and transmission with BBC Micro:bit programmed in C++. Three sensor micro:bits sent acceleration data to the main hub micro:bit which had a serial link to a computer. We then imported the data into Matlab for analysis and classification.
Challenges we ran into
- Eliminating gravity from the accelerometer readings.
- Transmitting data to the central hub from three active micro:bits.
- Finding the same critical points in different recordings.
Accomplishments that we're proud of
- Collecting consistent data that was usable for classification.
- Collecting all data with a single serial link.
- Classifying new recordings as one of three actions.
What we learned
- How to filter data based on imperfect sensors.
- How to utilize low energy Bluetooth connectivity to interconnect our sensors.
- How to use Matlab classification of data.
What's next for Kine-Fanatic
Using this same method on a much larger sample size would have interesting results. Additionally, attempting to classify a movement as "good" and "bad" could lead us to give the user feedback on their action.