Editing UBISS2024

This course is designed as a week-long tutorial to engage with ubiquitous devices in the domain of smart environments and how to use machine learning to build smart devices. Here, we use an [[Arduino_Nano_RP2040_Connect|Arduino Nano RP2040 Connect]].

= Schedule =
== Day 1 ==
* 16:00-17:00 Lecture: Introduction & Creating Interactive Smart Objects and Environments
* 17:00-17:45 Hands-On: [[UBISS2024#Task 1: Getting Started|Task 1: Getting Started]] Components, tools, and development environments
* 17:45-18:00 Lecture: Preview of the Tasks Ahead

== Day 2 ==
* 10:00-10:45 Lecture: Desiging and Implementing Sensor-Based Systems
* 10:45-11:00 ML Development Cycle: data collection, data cleaning, and labeling, selection of the AI/ML approach, hyper-parameter * selection and implementing the model, training the model/system, deploying the model, operations, re-training/continuous improvement 
* 11:00-12:00 Hands-On: [[UBISS2024#Task 2: Read Data|Task 2: Read Data]] (accelerometer and analog pin) 
* 12:00-13:00 lunch break
* 13:00-14:00 Hands-On: Task 3: Record the accelerometer data for four different actions (labeled dataset), store it, and transfer it to PC using the Arduino IDE
* 14:00-14:30 Lecture: Rule-based Systems: how to design them, pros: explainability, cons: it is hard
* 14:30-15:00 Hands-On: Task 4: analyze the data in Excel/Google sheets and find rules for the 4 actions
* 15:00-15:45 Hands-On: Task 5: Implement your rule-based algorithm, optional include explanations of why the state is recognized using the Arduino IDE
* 15:45-16:00 get Coffee / break
* 16:00-17:00 Ideation and testing ideas
* 17:00-17:15 Present your ideas
* 17:15-18:00 Hands-On: Discussion and presenting the results of Task 4 and 5

== Day 3 ==
* 10:00-10:30 Lecture: Introduction to Jupyter Notebooks, training an ML model based on a given data and the self-recorded data set on the PC
* 10:30-11:00 Lecture: Introduction to ML Libraries (everywhereML)
* 11:00-12:00 Hands-On: Project specification, Ideation on Project Ideas; and discussion of project ideas, group forming (groups of 2 or 3), Make your groups, specify your projects, see if you get the components (refine to work with the components available)
* 12:00-13:00 Lunch break
* 13:00-15:30 Hands-On: [[UBISS2024#Task 6: Getting Started with Jupyter Notebook|Task 6: Getting Started with Jupyter Notebook]] Installing Jupyter Notebook for Micropython, controlling LED, reading data, storing data 
* 15:30-16:00 Coffee break
* 16:00-16:30 Hands-On: Presentation: status update on your project 
* 16:15-18:00 Hands-On: [[UBISS2024#Task 7: Deploy Machine Learning Models|Task 7: Deploy Machine Learning Models]] Implementing a basic model using everywhereML

== Day 4 ==
* 10:00-10:45 Hands-On: Definition of project, project outline
* 10:45-11:15 Hands-On: project presentation: 60 sec per team
* 11:15-12:00 Hands-On: project work
* 12:00-13:00 Lunch break
* 13:00-15:00 Hands-On: project work
* 15:00-15:30 Hands-On: stand-up meeting on project progress
* 15:30-16:00 Coffee break
* 16:00-17:30 Hands-On: project work
* 17:30-18:00 Lecture: How to Evaluate ML Solutions (talk and discussion)

== Day 5 ==
* 10:00-10:30 Hands-On: stand-up meeting — project challenges and solutions
* 10:30-11:30 Hands-On: project work and preparing the presentation
** Requirements for the Presentation:
*** The presentation has to be 4 min long
*** Your team name and your names - and if you want a photo
*** A short video of the tech you envision (up to 60 sec, [https://www.kickstarter.com/ Kickstarter]-style promotion type)
*** A technology description, including the list of components used in the prototype
*** A description of your data set and how it was acquired
*** The ML model/approach you took to learning the data
*** An evaluation of how well your ML model works with the data set (and optional in real live)
* 11:30-12:00 Lecture: Pitfalls and Challenges in Developing ML/AI for IoT
* 12:00-13:00 Lunch break
* 13:00-15:30 Hands-On: project work
* 15:30-16:00 Coffee break
* 16:00-16:30 Lecture: Testing and Reporting ML Performance (How to Test the Prototype? & How to Report Performance?)
* 16:30-17:30 Hands-On: Testing of prototype performance
* 17:30-18:00 Hands-On: Open issues for the presentation on Saturday, Feedback sessions

== Day 6 ==
* 13:15-18:15: Workshop Result Presentations
* 18:30-18:50: Debriefing

== Final Submissions ==
You have to upload your final submission to the drive. This should include:
* a video where you explain your technology
** show the electronics components and name them
** show the physical setup that you created
** show the code you wrote and briefly explain it
* a zip file with all the code that is used in your project
* a schematic / drawing of your system as PDF or image (drawing it on paper and making a photo is fine)
* your final presentation (as PDF, Powerpoint)
* [optional] a drawing of your system architecture (hand drawing is fine)

= Tasks =

== Task 1: Getting Started ==
* Connect an Arduino Nano RP2040 Connect board, for this see [[Arduino_Nano_RP2040_Connect#Install the Arduino Nano RP2040 Connect Firmware|Install the Arduino Nano RP2040 Connect Firmware]]
* if you use Linux/MacOS and there are issues, look for serial line permissions, e.g. https://www.xanthium.in/linux-serial-port-programming-using-python-pyserial-and-arduino-avr-pic-microcontroller and https://github.com/arduino/lab-micropython-editor/issues/64
* Install the Arduino Lab for MicroPython development environment, https://labs.arduino.cc/en/labs/micropython
* Task 1.1: Make the orange LED (pin 6) blink using micro python https://docs.arduino.cc/micropython/basics/digital-analog-pins/   
* Task 1.2: Connect an external RGB LED (pin D2 = GPIO25, D3 = GPIO15, D4 = GPIO16) and control it (on, off, mix color, brightness), https://www.sketching-with-hardware.org/wiki/RGB_LED
* Task 1.3: Add the photo resistors to your board, read their values, and write them to the file; see the instructions for [[LDR]].
* Task 1.4: Combine your [[LDR]] and the [[RGB_LED]] example to change the blinking interval with the light value measures.

=== Solution Task 1.1: LED Blinking ===
<syntaxhighlight lang="python" line='line'>
# Blinky example

import time
from machine import Pin

# This is the only LED pin available on the Nano RP2040,
# other than the RGB LED connected to Nano WiFi module.
led = Pin(6, Pin.OUT)

while (True):
   led.on()
   time.sleep_ms(250)
   led.off()
   time.sleep_ms(200)
</syntaxhighlight>

=== Solution Task 1.2 Control external RGB ===
<syntaxhighlight lang="python" line='line'>
# RGB example

import time
from machine import Pin

# RGB LED connected to the RP2040
ledG = Pin(25, Pin.OUT)
ledR = Pin(15, Pin.OUT)
ledB = Pin(16, Pin.OUT)
print("start")

while (True):
    print("*")
    ledG.on()
    ledR.off()
    ledB.off()
    time.sleep_ms(250)
    ledG.off()
    ledR.on()
    ledB.off()
    time.sleep_ms(250)
    ledG.off()
    ledR.off()
    ledB.on()
    time.sleep_ms(250)
</syntaxhighlight>

=== Solution Task 1.3 Read Light-Dependent Resistor (LDR) ===
See [[LDR]]. A0 is the analog input with 16 bit resolution. It reads the analog value every second and print it to the console-

<syntaxhighlight lang="python" line='line'>
#Example usage for Arduino Nano
from machine import Pin, ADC
from time import sleep

analogPin = ADC(Pin(26))

while True:
  analogVal16 = analogPin.read_u16()
  print(analogVal16)
  sleep(1)
</syntaxhighlight>


=== Solution Task 1.4 Combine Light-Dependent Resistor (LDR) with Blinking LED ===
<syntaxhighlight lang="python" line='line'>
from machine import Pin, ADC
import time
from time import sleep

led = Pin(6, Pin.OUT)

while (True):
  analogPin = ADC(Pin(26))
  analogVal16 = analogPin.read_u16()
  print(analogVal16)
  rate = analogVal16 / 300 # create a simple mapping
  led.on()
  time.sleep_ms(int(rate)) # convert the rate to an integer type
  led.off()
  time.sleep_ms(int(rate))
</syntaxhighlight>

== Task 2: Read Data ==
* read data from the accelerometer and the gyro and print them (Arduino IDE) https://docs.arduino.cc/micropython/basics/board-examples/
* extend your program to write the data from the accelerometers to a file; see the instructions for [[FileIO]].
* transfer the file to your computer
* optional: add the photo resistors to your board, read their values, and write them to the file; see the instructions for [[LDR]].

=== Solution Task 2.1: Read Accelerometer and Gyro ===
<syntaxhighlight lang="python" line='line'>
import time
from lsm6dsox import LSM6DSOX

from machine import Pin, I2C
lsm = LSM6DSOX(I2C(0, scl=Pin(13), sda=Pin(12)))

while (True):
    accel_data = lsm.accel()
    print('Accelerometer: x:{:>8.3f} y:{:>8.3f} z:{:>8.3f}'.format(*accel_data))
    gyro_data = lsm.gyro()
    print('Gyroscope:     x:{:>8.3f} y:{:>8.3f} z:{:>8.3f}'.format(*gyro_data))
    print("")
    time.sleep_ms(100)
</syntaxhighlight>

=== Solution Task 2.2: Read analog values ===
A0 is the analog input with 16-bit resolution. It reads the analog value every second and print it to the console-

<syntaxhighlight lang="python" line='line'>
from machine import Pin, ADC
from time import sleep

analogPin = ADC(Pin(26))

while True:
  analogVal16 = analogPin.read_u16()
  print(analogVal16)
  sleep(1)
</syntaxhighlight>

== Task 6: Getting Started with Jupyter Notebook ==
* Connect the board
* Install the [[Jupyter Notebook]], 
* Read the accelerometer and the gyro and show it in the notebook

=== Task 6.1: is it moved? ===
* read acceleration and gyro
* calculate the differences between values
* show an ouput when it is move
* create a file on the device that logs, when it is moved

=== Task 6.2: it was turned upside down? ===
* read acceleration and gyro
* make a rule based "AI" that records
** it was put upside down
** it was turned 360 
** it was moved "quickly"

== Task 7: Deploy Machine Learning Models ==
We will use https://github.com/eloquentarduino/everywhereml to detect the same gestures as in Task 2.2. For this, install everywhereml:

See [[EverywhereML]] for downloading the full example and a dataset to experiment with.

<syntaxhighlight lang="Bash">
pip3 install -U everywhere
</syntaxhighlight>

Using everywhereml we can train a model on a more powerful machine for deployment on a microcontroller. See https://eloquentarduino.com/posts/micropython-machine-learning for example for such a training process. Assuming that our ML model is trained and stored in variable clf then we can save the model to a file using
<syntaxhighlight lang="python">
clf.to_micropython_file("MyModel.py")
</syntaxhighlight>

The MyModel.py file can then be saved and called directly on the microcontroller. To run the model on the microcontroller, assume your data is stored in x and you trained a RandomForestClassifier. Then you can predict via the following code snippet
<syntaxhighlight lang="python">
import MyModel
clf = MyModel.RandomForestClassifier()
clf.predict(x)
</syntaxhighlight>

== Task 8: Connect to WiFi ==
This is an optional task.
* Connect both boards to WIFI using [[Tutorial Network]]
* Use the Arduino Nano RP2040 Connect as output (showing a color)
* Use the Arduino Nano RP2040 Connect as input (recognize with rules 3 gestures)

= Links =
See the full list of links: [[UBISS2024-Links]]

== Local Links ==
https://ubicomp.net/sw/db1/var2db.php?
http://localhost:8888/notebooks/ArduinoNanoRP2040_v01.ipynb
http://localhost:8888/doc/tree/create-ML-model01.ipynb

= Reading =
== Required Reading before the course ==
* Albrecht Schmidt (2020) Interactive Human Centered Artificial Intelligence: A Definition and Research Challenges. In Proceedings of the International Conference on Advanced Visual Interfaces (AVI '20). Association for Computing Machinery, New York, NY, USA, Article 3, 1–4. https://doi.org/10.1145/3399715.3400873 https://uni.ubicomp.net/as/iHCAI2020.pdf (4p)
* Albrecht Schmidt and Kristof van Laerhoven (2021) How to build smart appliances? In IEEE Personal Communications, vol. 8, no. 4, pp. 66-71, Aug. 2001, https://doi.org/10.1109/98.944006 https://www.eti.uni-siegen.de/ubicomp/papers/sl_ieeepc2001.pdf (6p)
* Albrecht Schmidt (2017) Understanding and researching through making: a plea for functional prototypes. interactions 24.3, 78-81. https://doi.org/10.1145/3058498 https://www.sketching-with-hardware.org/files/functional3058498.pdf (4p)
* Huy Viet Le, Sven Mayer, and Niels Henze (2020) Deep learning for human-computer interaction. interactions 28, 1 (January - February 2021), 78–82. https://doi.org/10.1145/3436958 https://sven-mayer.com/wp-content/uploads/2021/01/huy2021deep.pdf (5p)
* Huy Viet Le, Sven Mayer, Max Weiß, Jonas Vogelsang, Henrike Weingärtner, and Niels Henze (2020) Shortcut Gestures for Mobile Text Editing on Fully Touch Sensitive Smartphones. In: ACM Trans. Comput.-Hum. Interact., vol. 27, no. 5, pp. 38. https://sven-mayer.com/wp-content/uploads/2020/09/le2020shortcuts.pdf (38p) 
* Judith Hurwitz, and Daniel Kirsch (2018) Machine learning for dummies. IBM Limited Edition 75, 9780429196645-6. https://www.ibm.com/downloads/cas/GB8ZMQZ3 (Pages 3-18 and 29-47, this is Chapters 1 and 3) (35p)
* Chris Garrett. MicroPython: An Intro to Programming Hardware in Python https://realpython.com/micropython/ (14 pages)
* MicroPython Basics https://docs.arduino.cc/micropython/basics/micropython-basics/ (5 pages)

== Recommended Reading before the course ==
* John D. Kelleher (2019) Deep Learning. https://mitpress.mit.edu/9780262537551/deep-learning/
* Yuli Vasiliev, Python for Data Science: A Hands-On Introduction, https://nostarch.com/python-data-science 
* Tutorial on Jupyter Notebooks: https://www.datacamp.com/tutorial/tutorial-jupyter-notebook 
* Alex Smola, and S. V. N. Vishwanathan (2008) Introduction to machine learning. Cambridge University, UK 32.34. https://alex.smola.org/drafts/thebook.pdf

= Random Commands =
pip install micropython-lsm6dsox

picotool.exe load -x C:\Users\ru42qak\AppData\Roaming\OpenMV\openmvide\firmware\ARDUINO_NANO_RP2040_CONNECT\firmware.bin

pip install jupyterlab  

pip install everywhereml       

python -m pip install jupyter

git clone https://github.com/goatchurchprime/jupyter_micropython_kernel.git

pip install -e jupyter_micropython_kernel

python -m notebook

python -m jupyter kernelspec list


C:\Users\ru42qak\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\LocalCache\local-packages\Python311\site-packages\jupyterlab>pip install -e jupyter_micropython_kernel

C:\Users\ru42qak\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\LocalCache\local-packages\Python311\site-packages\jupyterlab>python -m notebook

[[Category:Courses]]
[[Category:UBISS2024]]
[[Category:Arduino Nano RP2040 Connect]]
[[Category:MicroPython]]