Machine Learning on Microcontrollers is the most efficient, cheap, and reliable way of developing Edge Computing applications with low energy consumption. ‘Arduino Nano 33 BLE Sense board’ is used as a microcontroller to capture and classify 5 human gestures - “Squat”, “Jump”, “Walk”, “Run”, “Other”. Built Deep Learning model using captured gestures data to classify human gestures.
- Python
- C
- TensoFlow Lite
- ArduinoBLE
- Arduino_LSM9DS1
- BLEAK
- pandas
- numpy
- matplotlib
To showcase the following benefits in developing Edge Computing applications without sacrificing accuracy on Microcontrollers such as Arduino Nano 33 BLE Sense board:
- Cost Effective
- Low Energy Consumption
- Data Privacy
- Tiny form factor
- Flexibility
- Dr.Somya Mohanty
The /src folder contains the Jupyter notebook, models, arduino code (capture , classify) and the /data folder contains the datasets that are used for this project. The detailed information and documentation about this project is included in the report under /docs folder in this repository.