jgaucin03 / caixieee-tinyml-workshop

All the relevant links and such

CAIxIEEE-TinyML-Workshop

All the relevant links and such

Software setup (Arduino)

• Step 1. Download and Install the latest version of Arduino IDE according to your operating system

  https://www.arduino.cc/en/software

• Step 2. Launch the Arduino application

  “Arduino IDE”

• Step 3. Add Seeed Studio XIAO nRF52840 (Sense) board package to your Arduino IDE

• Navigate to File > Preferences, and fill "Additional Boards Manager URLs" with the url below: https://files.seeedstudio.com/arduino/package_seeeduino_boards_index.json

• (On macOS): Navigate to Arduino IDE > Settings… > and fill “Additional Boards Manager URLs" with the URL below https://files.seeedstudio.com/arduino/package_seeeduino_boards_index.json

  

---

• Step 4: Navigate to Tools > Board > Boards Manager..., type the keyword "seeed nrf52" in the search box, select the latest version of the board you want, and install it. (You can install both) We NEED Seeed nRF52 mbed-enabled Boards installed to work the classifier

---

• Step 5. Select your board and port Board: After installing the board package, navigate to Tools > Board and choose the board you want, continue to select "Seeed XIAO nRF52840 Sense". Now we have finished setting up the Seeed Studio XIAO nRF52840 (Sense) for Arduino IDE

• Port: Navigate to Tools > Port and select the serial port name of the connected Seeed Studio XIAO nRF52840 (Sense).
• This is something like COM3 or higher for Windows and /dev/… for Mac
  • Note: For me (macOS) it’s: /dev/cu. usbmodem101 (Seeed XIAO BLE - nRF52840, Seeed XIAO BLE Sense - nRF52840)

- Board and Port are now setup correctly! ---

Software setup (Tensorflow and Machine Learning Model)

• Step 1. Download Seeed_Arduino_LSM6DS3 library as a zip file

• Step 2. Download tf_lite_arduino_examples library

**Note: Make sure you download both in a Folder you have direct/easy access to**

- Step 3. Open Arduino IDE, navigate to Sketch > Include Library > Add .ZIP Library... and open both the downloaded zip files one after the other

- Step 4. Navigate to File > Examples > Seeeed Arduino LSM6DS3 > IMU_Capture to open IMU_Capture.ino

- Step 5. Upload the codes

- Step 6. Open the Serial Monitor ( Tools > Serial Monitor )

- You should be able to see the data you collect from now on

• BUT for workshop, we give the data (Don’t want copying and pasting 1190 lines of code for punch and for flex each)

Inference

Now we will use the downloaded TensorFlow Lite model file (model.h) to recognize the punch and flex actions from Seeed Studio XIAO nRF52840 Sense.

• Step 1. Navigate to the library path of Seeed_Arduino_LSM6DS3 Library (normally under Documents > Arduino > libraries > Seeed_Arduino_LSM6DS3), and visit examples > IMU_Classifier

• Step 2. Replace the model.h file with the one that we downloaded before

• Step 3. Double click IMU_Classifier.ino and upload the codes to the Seeed Studio XIAO nRF52840 Sense.

Punch action

Open serial monitor and perform a punching action. You will see that it will give a result close to 1 next to punch

Flex action

Perform a flexing action. You will see that it will give a result close to 1 next to flex

References

Getting Started with TensorFlow Lite on Seeed Studio XIAO nRF52840 Sense

Seeed_Arduino_LSM6DS3 Github repo

tflite-micro-arduino-examples