Exercise 2: Arduino

Learning Open Source Hardware - Arduino

1. Learning Open Source Hardware - Arduino

Arduino UNO R4 WiFi

Source: Arduino UNO R4 WiFi Development Board Detailed Explanation http://xhslink.com/o/8dtNOFFrhe 这篇内容在【小红书】候着你~

Arduino is an open-source electronic prototyping platform that includes hardware (various models of Arduino development boards) and software (Arduino IDE). Its core advantages are:

Common Application Scenarios:

1. Arduino IDE

(1) Introduction

Arduino IDE is an integrated development environment for writing, uploading, and debugging programs based on the Arduino platform. It's an open-source tool that runs on Windows, Mac OS X, and Linux systems. Arduino IDE uses C/C++ language with a simple graphical interface, making it easy for users to write programs. Through this interface, users can easily access various Arduino board libraries and example programs, and quickly write and test their own programs.

Arduino IDE includes a code editor, compiler, uploader, and serial monitor for debugging and viewing program output. Additionally, it supports multiple platforms and programming languages, and can integrate with other open-source tools. Using Arduino IDE, users can develop various Arduino projects such as LED control, sensor reading, robot control, etc. Its ease of use and powerful features make it one of the preferred tools for many makers and engineers.

IDE Feature 1 IDE Feature 2 IDE Feature 3 IDE Feature 4

References:

(2) Coding Methods

(3) Hardware Connection

When using Arduino IDE, hardware connection is very important as it involves physical connections with the Arduino board and external components. Here are the general hardware connection methods when using Arduino IDE:

Running Light Program Execution

(1) Physical Connection Method

  1. Required Materials: Arduino development board, LED lights (quantity according to your needs), resistors (one per LED, determine resistance value based on selected LED characteristics), jumper wires or breadboard for connection
  2. Connect Circuit: Connect each LED's anode (long pin) to Arduino's digital output pins. Connect each LED's cathode (short pin) to ground (GND) through a resistor.

(2) Program Execution Method

Writing the Program

Running Light Setup

I. Hardware List

II. Circuit Wiring Diagram

1. HC-SR04 Ultrasonic Wiring

HC-SR04 Pin Arduino R4 Pin
VCC 5V
GND GND
Trig D9
Echo D10

2. Running Light LED Wiring (Common Cathode, Negative Terminal Connected to Resistor)

Using Ultrasonic Sensor to Complete Running Light Illumination

Implementation Code:

// Ultrasonic pin definitions
const int trigPin = 9;
const int echoPin = 10;
// LED running light pin array
int ledPins[] = {3,4,5,6};
int ledNum = sizeof(ledPins)/sizeof(ledPins[0]);
long distance;
long duration;

void setup() {
  Serial.begin(9600);
  // Ultrasonic pin modes
  pinMode(trigPin, OUTPUT);
  pinMode(echoPin, INPUT);
  // Set all LEDs as output
  for(int i=0; i<ledNum; i++){
    pinMode(ledPins[i], OUTPUT);
    digitalWrite(ledPins[i], LOW); // Initial light off
  }
}

// Get ultrasonic distance function
long getDistance(){
  digitalWrite(trigPin, LOW);
  delayMicroseconds(2);
  digitalWrite(trigPin, HIGH);
  delayMicroseconds(10);
  digitalWrite(trigPin, LOW);
  duration = pulseIn(echoPin, HIGH);
  distance = duration * 0.034 / 2;
  return distance;
}

void loop() {
  distance = getDistance();
  Serial.print("距离:");
  Serial.print(distance);
  Serial.println(" cm");

  // Trigger running light when less than 30cm
  if(distance < 30){
    // Forward running
    for(int i=0; i<ledNum; i++){
      digitalWrite(ledPins[i], HIGH);
      delay(150);
      digitalWrite(ledPins[i], LOW);
    }
    // Reverse return flow (optional, comment out to keep only unidirectional running)
    for(int i=ledNum-2; i>0; i--){
      digitalWrite(ledPins[i], HIGH);
      delay(150);
      digitalWrite(ledPins[i], LOW);
    }
  }else{
    // No object nearby, all off
    for(int i=0; i<ledNum; i++){
      digitalWrite(ledPins[i], LOW);
    }
  }
  delay(100);
}
Running Light Effect Running Light Animation

4. Case Studies

Case 1: "Emotion Aid" Project

Emotion Aid Device

Case Link: https://blog.arduino.cc/2023/05/19/the-emotion-aid-is-a-wearable-device-that-communicates-the-users-emotions/#respond

Advantages:

  1. Novel & Intuitive Output: Uses pure physical motion to express emotions rather than screens or lights, more friendly to sensory-sensitive individuals, and can attract attention and curiosity from surrounding people, opening non-verbal communication.
  2. Multi-modal Signal Fusion: Integrates three types of physiological signals: skin electrical activity (EDA), body temperature, and heart rate, using "multi-modal fusion" approach to improve emotion inference robustness, more scientific than single sensor.
  3. Open Source & Community Friendly: As a complete course design, code, circuit diagrams, and structural designs are fully open-sourced on Instructables, using entry-level hardware like Arduino Uno and servos, very convenient for subsequent reproduction and improvement.

Disadvantages:

  1. Oversimplified Emotion Inference: This is the project's biggest shortcoming. Inferring complex human emotions (such as frustration, anxiety) solely from EDA, heart rate, and temperature is scientifically extremely difficult or even unrealistic. This method simply links complex physiological signals with emotions, making conclusions very fragile.
  2. Rough Data Fusion & Processing: The project does not seem to effectively denoise or extract features from raw data. For example, motion artifacts severely interfere with pulse and EDA sensor signals, and the project ignores individual physiological baseline differences, reducing inference universality.
  3. Suboptimal Wearing Position: Design attached to underwear limits gender applicability, and this position is greatly affected by breathing and body movement, easily introducing interference.
  4. Battery & Durability Issues: 9V battery has limited capacity, difficult to support long-term use and drive servo current requirements.
  5. Risk of Emotional Labeling: Emotional states are crudely simplified to a single mode. When the device "judges" incorrectly, it may transmit wrong information, causing social misunderstandings or negative labels, which contradicts the original intention of helping communication.

Case 2: UESTC Professor Xu Peng's Team - "Wireless Brain Function Assessment System" and "Autism Precision Neuromodulation Technology"

Case Link: https://www.new1.uestc.edu.cn/?n=UestcNews.Front.DocumentV2.ArticlePage&Id=96164

Advantages:

  1. Pioneered "Quantitative Diagnosis" Paradigm: Combined EEG signals with AI algorithms to transform diagnosis from experience-based "qualitative judgment" to data-supported "quantitative diagnosis", achieving major clinical breakthrough.
  2. High User-Friendliness Experience: Wireless dry-electrode EEG cap for diagnosis and short-duration treatment mode (3 times daily, 40 seconds each) greatly improved comfort and cooperation of autistic children.
  3. "Deep Brain" Intervention Possible: Targeted transcranial magnetic therapy indirectly regulates deep brain regions through superficial "relay stations", providing new path for solving deep brain region intervention challenges, achieving over 80% effectiveness.

Disadvantages:

  1. High Technical Threshold & Cost: BCI and TMS equipment research and manufacturing costs are high, may mainly target tertiary hospitals or professional rehabilitation institutions in the short term, difficult to popularize.
  2. Physiological Signal Common Limitations: EEG signals themselves have low signal-to-noise ratio and are easily interfered with; post-processing and AI model training require extremely high algorithm standards, there is a certain risk of misjudgment.
  3. Institutional Application Scenario Constraints: The entire system requires professional venue operation, difficult to serve as home daily intervention equipment, unable to provide all-weather support like wearable products.
  4. Long-term Safety & Effectiveness Pending Verification: Although effectiveness exceeds 80%, this evaluation mostly originates from short-term clinical observations; long-term impacts of transcranial magnetic stimulation still require longer time-span tracking studies.
  5. "Black Box" Problem & Attribution Dilemma: AI algorithms are accurate but decision logic is opaque to humans. Doctors find it difficult to judge the specific basis for AI conclusions, increasing the difficulty of diagnostic review.

Summary

Through this exercise, we learned:

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