Kutluhan Aktar
Published © CC BY

Mouse Fatigue Estimation by GSR and EMG Values w/ TensorFlow

Collate forearm muscle soreness data on the SD card, build and train a neural network model, and run the model directly on Wio Terminal.

ExpertFull instructions provided4,606

Things used in this project

Hardware components

Wio Terminal
Seeed Studio Wio Terminal
×1
Grove - GSR sensor
Seeed Studio Grove - GSR sensor
×1
Seeed Studio Grove - EMG Detector
×1
Creality CR-6 SE 3D Printer
×1
Seeed Studio Grove - 4 pin Male Jumper Conversion Cable
×1
Seeed Studio Grove - Universal 4 Pin Cable
×1
SD Card
×1
Xiaomi 20000 mAh 3 Pro Type-C Power Bank
×1

Software apps and online services

Arduino IDE
Arduino IDE
TensorFlow
TensorFlow
Thonny
Fusion 360
Autodesk Fusion 360
Ultimaker Cura
Visual Studio 2017
Microsoft Visual Studio 2017

Hand tools and fabrication machines

Hot glue gun (generic)
Hot glue gun (generic)

Story

Read more

Custom parts and enclosures

mouse fatigue detection cover v1.stl

mouse fatigue detection case v1.stl

rgb565.zip

Schematics

Schematic-1

Schematic-2

Code

main.py

Python
# Mouse Fatigue Estimation by GSR and EMG Values w/ TensorFlow
#
# Windows, Linux, or Ubuntu
#
# By Kutluhan Aktar
#
# Collate forearm muscle soreness data on the SD card, build and train a neural network model, and run the model directly on Wio Terminal.
# 
#
# For more information:
# https://www.theamplituhedron.com/projects/Mouse_Fatigue_Estimation_by_GSR_and_EMG_Values_w_TensorFlow/

import tensorflow as tf
from tensorflow import keras
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from tflite_to_c_array import hex_to_c_array
from test_data import test_inputs, test_labels

# Create a class to build a neural network model after visualizing and scaling (normalizing) the soreness data (GSR and EMG) collected by Wio Terminal.
class Mouse_Fatigue:
    def __init__(self, csv_path):
        self.inputs = []
        self.labels = []
        self.model_name = "mouse_fatigue_level"
        self.scale_val = 1000
        # Read the collated soreness data set (GSR and EMG):
        self.df = pd.read_csv(csv_path)
    # Create graphics for each requested column.
    def graphics(self, column_1, column_2, x_label, y_label):
        # Show the requested data column from the data set:
        plt.style.use("dark_background")
        plt.gcf().canvas.set_window_title('Mouse Fatigue Estimation by GSR and EMG Values')
        plt.hist2d(self.df[column_1], self.df[column_2], cmap="coolwarm")
        plt.colorbar()
        plt.xlabel(x_label)
        plt.ylabel(y_label)
        plt.title(x_label)
        plt.show()
    # Visualize data before creating and training the neural network model.
    def data_visualization(self):
        # Scrutinize data columns to build a model with appropriately formatted data:
        self.graphics('GSR', 'EMG', 'GSR', 'EMG')
    # Scale (normalize) data to define appropriately formatted inputs.
    def scale_data_and_define_inputs(self):
        self.df["scaled_GSR"] = self.df["GSR"] / self.scale_val
        self.df["scaled_EMG"] = self.df["EMG"] / self.scale_val
        # Create the inputs array by utilizing the scaled variables:
        for i in range(len(self.df)):
            self.inputs.append(np.array([self.df["scaled_GSR"][i], self.df["scaled_EMG"][i]]))
        self.inputs = np.asarray(self.inputs)
    # Assign labels for each input according to the predefined soreness classes for each data record.
    def define_and_assign_labels(self):
        self.labels = self.df["Soreness"]
    # Split inputs and labels into training and test sets.
    def split_data(self):
        # (training)
        self.train_inputs = self.inputs
        self.train_labels = self.labels
        # (test)
        self.test_inputs = test_inputs / self.scale_val
        self.test_labels = test_labels
    # Build and train an artificial neural network (ANN) model to make predictions on mouse fatigue levels (classes) based on GSR and EMG measurements.
    def build_and_train_model(self):
        # Build the neural network:
        self.model = keras.Sequential([
            keras.Input(shape=(2,)),
            keras.layers.Dense(64, activation='relu'),
            keras.layers.Dense(32, activation='relu'),
            keras.layers.Dense(8, activation='relu'),
            keras.layers.Dense(3, activation='softmax')
        ])
        # Compile:
        self.model.compile(optimizer='adam', loss="sparse_categorical_crossentropy", metrics=['accuracy'])
        # Train:
        self.model.fit(self.train_inputs, self.train_labels, epochs=150)
        # Test the model accuracy:
        print("\n\nModel Evaluation:")
        test_loss, test_acc = self.model.evaluate(self.test_inputs, self.test_labels) 
        print("Evaluated Accuracy: ", test_acc)
    # Save the model for further usage:
    def save_model(self):
        self.model.save("model/{}.h5".format(self.model_name))
    # Convert the TensorFlow Keras H5 model (.h5) to a TensorFlow Lite model (.tflite).
    def convert_TF_model(self, path):
        #model = tf.keras.models.load_model(path + ".h5")
        converter = tf.lite.TFLiteConverter.from_keras_model(self.model)
        #converter.optimizations = [tf.lite.Optimize.DEFAULT]
        #converter.target_spec.supported_types = [tf.float16]
        tflite_model = converter.convert()
        # Save the recently converted TensorFlow Lite model.
        with open(path + '.tflite', 'wb') as f:
            f.write(tflite_model)
        print("\r\nTensorFlow Keras H5 model converted to a TensorFlow Lite model!\r\n")
        # Convert the recently created TensorFlow Lite model to hex bytes (C array) to generate a .h file string.
        with open("model/{}.h".format(self.model_name), 'w') as file:
            file.write(hex_to_c_array(tflite_model, self.model_name))
        print("\r\nTensorFlow Lite model converted to a C header (.h) file!\r\n")
    # Run Artificial Neural Network (ANN):
    def Neural_Network(self, save):
        self.scale_data_and_define_inputs()
        self.define_and_assign_labels()
        self.split_data()
        self.build_and_train_model()
        if save:
            self.save_model()
            
# Define a new class object named 'mouse_fatigue_level':
mouse_fatigue_level = Mouse_Fatigue("data/mouse_fatigue_data_set.csv")

# Visualize data columns:
mouse_fatigue_level.data_visualization()

# Artificial Neural Network (ANN):        
mouse_fatigue_level.Neural_Network(True)

# Convert the TensorFlow Keras H5 model to a TensorFlow Lite model:
mouse_fatigue_level.convert_TF_model("model/{}".format(mouse_fatigue_level.model_name))

test_data.py

Python
import numpy as np

test_inputs = np.array([
    [154,429],
    [156,425],
    [175,420],
    [188,435],
    [189,438],
    [192,440],
    [322,460],
    [346,465],
    [357,457],
    [168,416],
    [370,483],
    [201,445],
    [124,429],
    [350,461],
    [128,419],
    [301,450]
])

test_labels = np.array([0,0,0,1,1,1,2,2,2,0,2,1,0,2,0,1])

tflite_to_c_array.py

Python
# Code from:
# https://www.digikey.com/en/maker/projects/intro-to-tinyml-part-1-training-a-model-for-arduino-in-tensorflow/8f1fc8c0b83d417ab521c48864d2a8ec
# By Shawn Hymel

# Function: Convert some hex value into an array for C programming
def hex_to_c_array(hex_data, var_name):

  c_str = ''

  # Create header guard
  c_str += '#ifndef ' + var_name.upper() + '_H\n'
  c_str += '#define ' + var_name.upper() + '_H\n\n'

  # Add array length at top of file
  c_str += '\nunsigned int ' + var_name + '_len = ' + str(len(hex_data)) + ';\n'

  # Declare C variable
  c_str += 'unsigned char ' + var_name + '[] = {'
  hex_array = []
  for i, val in enumerate(hex_data) :

    # Construct string from hex
    hex_str = format(val, '#04x')

    # Add formatting so each line stays within 80 characters
    if (i + 1) < len(hex_data):
      hex_str += ','
    if (i + 1) % 12 == 0:
      hex_str += '\n '
    hex_array.append(hex_str)

  # Add closing brace
  c_str += '\n ' + format(' '.join(hex_array)) + '\n};\n\n'

  # Close out header guard
  c_str += '#endif //' + var_name.upper() + '_H'

  return c_str

mouse_fatigue_detection_data_collect.ino

Arduino
       /////////////////////////////////////////////
      // Mouse Fatigue Estimation by GSR and EMG //
     //          Values w/ TensorFlow           //
    //             ---------------             //
   //              (Wio Terminal)             //
  //             by Kutluhan Aktar           //
 //                                         //
/////////////////////////////////////////////

//
// Collate forearm muscle soreness data on the SD card, build and train a neural network model, and run the model directly on Wio Terminal.
//
// For more information:
// https://www.theamplituhedron.com/projects/Mouse_Fatigue_Estimation_by_GSR_and_EMG_Values_w_TensorFlow/
//
//
// Connections
// Wio Terminal :
//                                Grove - GSR sensor
// A0  --------------------------- Grove Connector
//                                Grove - EMG Detector
// A2  --------------------------- Grove Connector


// Include the required libraries.
#include <SPI.h>
#include <Seeed_FS.h>
#include "TFT_eSPI.h"
#include "seeed_line_chart.h"
#include "SD/Seeed_SD.h"
#include "RawImage.h"

// Define the TFT screen:
TFT_eSPI tft;

// Define the sprite settings: 
#define max_size 50 // maximum size of data
doubles gsr_data, emg_data;
TFT_eSprite spr = TFT_eSprite(&tft);

// Initialize the File class and define the file name: 
File myFile;
const char* data_file = "mouse_fatigue_data_set.csv";

// Define the sensor voltage (signal) pins:
#define GSR A0
#define EMG A2

// Define the data holders.
int gsr_value, emg_value;
uint32_t background_color = tft.color565(31,32,32);
uint32_t text_color = tft.color565(174,255,205);

void setup(){
  Serial.begin(115200);

  // Configurable Buttons:
  pinMode(WIO_KEY_A, INPUT_PULLUP);
  pinMode(WIO_KEY_B, INPUT_PULLUP);
  pinMode(WIO_KEY_C, INPUT_PULLUP);
  
  // Check the connection status between Wio Terminal and the SD card.
  if(!SD.begin(SDCARD_SS_PIN, SDCARD_SPI)) while (1);

  // Initiate the TFT screen:
  tft.begin();
  tft.setRotation(3);
  tft.fillScreen(background_color);
  tft.setTextColor(text_color);
  tft.setTextSize(2);
  // Create the sprite.
  spr.createSprite(TFT_HEIGHT / 2, TFT_WIDTH);

  // Define and display the 16-bit images saved on the SD card:
  drawImage<uint16_t>("data_collect.bmp", TFT_HEIGHT, 0);
  drawImage<uint16_t>("carpal_tunnel.bmp", TFT_HEIGHT/2, 0);
  drawImage<uint16_t>("mouse.bmp", TFT_HEIGHT/2, TFT_WIDTH-90);
}

void loop(){
  // Obtain current measurements generated by the GSR sensor and the EMG sensor.
  get_GSR_data(3);
  get_EMG_data();
  // Initialize the sprite.
  spr.fillSprite(background_color);
  
  // Adjust the line chart data arrays:
  if(gsr_data.size() == max_size) gsr_data.pop();
  if(emg_data.size() == max_size) emg_data.pop();
  
  // Append new data variables to the line chart data arrays:
  gsr_data.push(gsr_value);
  emg_data.push(emg_value);
  
  // Display the line charts on the TFT screen: 
  display_line_chart(0, "GSR", TFT_HEIGHT/2, 90, gsr_data, text_color, tft.color565(165,40,44));
  display_line_chart(110, "EMG", TFT_HEIGHT/2, 90, emg_data, text_color, tft.color565(165,40,44));
  spr.pushSprite(0, 0);
  spr.setTextColor(text_color);
  delay(50);

  // Save the data record to the given CSV file with the selected soreness class.
  if(digitalRead(WIO_KEY_A) == LOW) save_data_to_SD_Card("0");
  if(digitalRead(WIO_KEY_B) == LOW) save_data_to_SD_Card("1");
  if(digitalRead(WIO_KEY_C) == LOW) save_data_to_SD_Card("2");
}


void save_data_to_SD_Card(String Soreness){
  // Open the given CSV file on the SD card in the APPEND file mode.
  // FILE MODES: WRITE, READ, APPEND
  myFile = SD.open(data_file, FILE_APPEND);
  // If the given file is opened successfully:
  if(myFile){
    Serial.print("Writing to "); Serial.print(data_file); Serial.println("...");
    // Create the data record to be inserted as a new row: 
    String data_record = String(gsr_value) + "," + String(emg_value) + "," + Soreness;
    // Append the data record:
    myFile.println(data_record);
    // Close the CSV file:
    myFile.close();
    Serial.println("Data saved successfully!\n");
    // Notify the user after appending the given data record successfully.
    tft.fillScreen(background_color);
    drawImage<uint16_t>("data_collect.bmp", TFT_HEIGHT/4, 0);
    tft.drawString("Selected Soreness Class: " + Soreness, 0, 140);
    tft.drawString("Data Stored!", 86, 180);
  }else{
    // If Wio Terminal cannot open the given CSV file successfully:
    Serial.println("Wio Terminal cannot open the given CSV file!\n");
    tft.fillScreen(background_color);
    tft.drawString("Wio Terminal", 35, 105);
    tft.drawString("cannot open the file!", 35, 125);
  }
  // Exit and clear:
  delay(3000);
  tft.fillScreen(background_color);
  drawImage<uint16_t>("carpal_tunnel.bmp", TFT_HEIGHT/2, 0);
  drawImage<uint16_t>("mouse.bmp", TFT_HEIGHT/2, TFT_WIDTH-90);
}

void display_line_chart(int header_y, const char* header_title, int chart_width, int chart_height, doubles data, uint32_t graph_color, uint32_t line_color){
  // Define the line graph title settings:
  auto header =  text(0, header_y)
                 .value(header_title)
                 .align(center)
                 .valign(vcenter)
                 .width(chart_width)
                 .color(tft.color565(243,208,296))
                 .thickness(2);
  // Define the header height and draw the line graph title. 
  header.height(header.font_height() * 2);
  header.draw();
  // Define the line chart settings:
  auto content = line_chart(0, header.height() + header_y);
  content
  .height(chart_height) // the actual height of the line chart
  .width(chart_width) // the actual width of the line chart
  .based_on(0.0) // the starting point of the y-axis must be float
  .show_circle(false) // drawing a circle at each point, default is on
  .value(data) // passing the given data array to the line graph
  .color(line_color) // setting the line color 
  .x_role_color(graph_color) // setting the line graph color
  .x_tick_color(graph_color)
  .y_role_color(graph_color)
  .y_tick_color(graph_color)
  .draw();
}

void get_GSR_data(int calibration){
  long sum = 0;
  // Calculate the average of the last ten GSR sensor measurements to remove the glitch.
  for(int i=0;i<10;i++){
    sum += analogRead(GSR);
    delay(5);
  }
  gsr_value = (sum / 10) - calibration;
  Serial.print("GSR Value => "); Serial.println(gsr_value);
}

void get_EMG_data(){
  long sum = 0;
  // Evaluate the summation of the last 32 EMG sensor measurements.
  for(int i=0;i<32;i++){
    sum += analogRead(EMG);
  }
  // Shift the summation by five with the right shift operator (>>) to obtain the EMG value.  
  emg_value = sum >> 5;
  Serial.print("EMG Value => "); Serial.println(emg_value); Serial.println();
  delay(10);
}

mouse_fatigue_detection_run_model.ino

Arduino
       /////////////////////////////////////////////
      // Mouse Fatigue Estimation by GSR and EMG //
     //          Values w/ TensorFlow           //
    //             ---------------             //
   //              (Wio Terminal)             //
  //             by Kutluhan Aktar           //
 //                                         //
/////////////////////////////////////////////

//
// Collate forearm muscle soreness data on the SD card, build and train a neural network model, and run the model directly on Wio Terminal.
//
// For more information:
// https://www.theamplituhedron.com/projects/Mouse_Fatigue_Estimation_by_GSR_and_EMG_Values_w_TensorFlow/
//
//
// Connections
// Wio Terminal :
//                                Grove - GSR sensor
// A0  --------------------------- Grove Connector
//                                Grove - EMG Detector
// A2  --------------------------- Grove Connector


// Include the required libraries.
#include <SPI.h>
#include <Seeed_FS.h>
#include "TFT_eSPI.h"
#include "seeed_line_chart.h"
#include "SD/Seeed_SD.h"
#include "RawImage.h"

// Import the required TensorFlow modules.
#include "TensorFlowLite.h"
#include "tensorflow/lite/micro/kernels/micro_ops.h"
#include "tensorflow/lite/micro/micro_error_reporter.h"
#include "tensorflow/lite/micro/micro_interpreter.h"
#include "tensorflow/lite/micro/all_ops_resolver.h"
#include "tensorflow/lite/version.h"

// Import the converted TensorFlow Lite model.
#include "mouse_fatigue_level.h"

// TFLite globals, used for compatibility with Arduino-style sketches:
namespace {
  tflite::ErrorReporter* error_reporter = nullptr;
  const tflite::Model* model = nullptr;
  tflite::MicroInterpreter* interpreter = nullptr;
  TfLiteTensor* model_input = nullptr;
  TfLiteTensor* model_output = nullptr;

  // Create an area of memory to use for input, output, and other TensorFlow arrays.
  constexpr int kTensorArenaSize = 15 * 1024;
  uint8_t tensor_arena[kTensorArenaSize];
} // namespace

// Define the threshold value for the model outputs (results).
float threshold = 0.75;

// Define the muscle soreness level (class) names and color codes:
String classes[] = {"Relaxed", "Tense", "Exhausted"};
uint32_t color_codes[] = {tft.color565(1,156,0), tft.color565(255,169,2), tft.color565(226,16,1)};

// Define the class image list:
const char* images[] = {"relaxed.bmp", "tense.bmp", "exhausted.bmp"};

// Define the TFT screen:
TFT_eSPI tft;

// Define the sprite settings: 
#define max_size 50 // maximum size of data
doubles gsr_data, emg_data;
TFT_eSprite spr = TFT_eSprite(&tft);

// Define the sensor voltage (signal) pins:
#define GSR A0
#define EMG A2

// Define the data holders.
int gsr_value, emg_value;
uint32_t background_color = tft.color565(31,32,32);
uint32_t text_color = tft.color565(174,255,205);

void setup(){
  Serial.begin(115200);

  // 5-Way Switch
  pinMode(WIO_5S_PRESS, INPUT_PULLUP);

  // TensorFlow Lite Model settings:
  
  // Set up logging (will report to Serial, even within TFLite functions).
  static tflite::MicroErrorReporter micro_error_reporter;
  error_reporter = &micro_error_reporter;

  // Map the model into a usable data structure.
  model = tflite::GetModel(mouse_fatigue_level);
  if (model->version() != TFLITE_SCHEMA_VERSION) {
    error_reporter->Report("Model version does not match Schema");
    while(1);
  }

  // This pulls in all the operation implementations we need.
  // NOLINTNEXTLINE(runtime-global-variables)
  static tflite::AllOpsResolver resolver;

  // Build an interpreter to run the model.
  static tflite::MicroInterpreter static_interpreter(
    model, resolver, tensor_arena, kTensorArenaSize,
    error_reporter);
  interpreter = &static_interpreter;

  // Allocate memory from the tensor_arena for the model's tensors.
  TfLiteStatus allocate_status = interpreter->AllocateTensors();
  if (allocate_status != kTfLiteOk) {
    error_reporter->Report("AllocateTensors() failed");
    while(1);
  }

  // Assign model input and output buffers (tensors) to pointers.
  model_input = interpreter->input(0);
  model_output = interpreter->output(0);

  delay(1000);

  // Check the connection status between Wio Terminal and the SD card.
  if(!SD.begin(SDCARD_SS_PIN, SDCARD_SPI)) while (1);

  // Initiate the TFT screen:
  tft.begin();
  tft.setRotation(3);
  tft.fillScreen(background_color);
  tft.setTextColor(text_color);
  tft.setTextSize(2);
  // Create the sprite.
  spr.createSprite(TFT_HEIGHT / 2, TFT_WIDTH);

  // Define and display the 16-bit images saved on the SD card:
  for(int i=0; i<3; i++){ drawImage<uint16_t>(images[i], TFT_HEIGHT, 0); }
  drawImage<uint16_t>("carpal_tunnel.bmp", TFT_HEIGHT/2, 0);
  drawImage<uint16_t>("mouse.bmp", TFT_HEIGHT/2, TFT_WIDTH-90);

  delay(1000);
}

void loop(){
  // Obtain current measurements generated by the GSR sensor and the EMG sensor.
  get_GSR_data(3);
  get_EMG_data();
  // Initialize the sprite.
  spr.fillSprite(background_color);
  
  // Adjust the line chart data arrays:
  if(gsr_data.size() == max_size) gsr_data.pop();
  if(emg_data.size() == max_size) emg_data.pop();
  
  // Append new data variables to the line chart data arrays:
  gsr_data.push(gsr_value);
  emg_data.push(emg_value);
  
  // Display the line charts on the TFT screen: 
  display_line_chart(0, "GSR", TFT_HEIGHT/2, 90, gsr_data, text_color, tft.color565(165,40,44));
  display_line_chart(110, "EMG", TFT_HEIGHT/2, 90, emg_data, text_color, tft.color565(165,40,44));
  spr.pushSprite(0, 0);
  spr.setTextColor(text_color);
  delay(50);

  // Execute the TensorFlow Lite model to make predictions on the muscle soreness levels (classes). 
  if(digitalRead(WIO_5S_PRESS) == LOW) run_inference_to_make_predictions();
}

void run_inference_to_make_predictions(){    
    // Scale (normalize) muscle soreness data depending on the given model and copy them to the input buffer (tensor):
    model_input->data.f[0] = gsr_value / 1000;
    model_input->data.f[1] = emg_value / 1000;
    
    // Run inference:
    TfLiteStatus invoke_status = interpreter->Invoke();
    if (invoke_status != kTfLiteOk) {
      error_reporter->Report("Invoke failed on the given input.");
    }

    // Read predicted y values (muscle soreness classes) from the output buffer (tensor): 
    for(int i = 0; i<3; i++){
      if(model_output->data.f[i] >= threshold){
        int w = 150, h = 40, str_x = 12, str_y = 65;
        int y_offset = h + ((h - str_y) / 2);
        int x_offset = classes[i].length() * str_x;
        // Display the detection result (class).
        tft.fillScreen(background_color);
        drawImage<uint16_t>(images[i], (TFT_HEIGHT-75)/2, 0);
        // Print:
        tft.fillRect((TFT_HEIGHT-w)/2, TFT_WIDTH-h, w, h, color_codes[i]);
        tft.drawString(classes[i], (TFT_HEIGHT-x_offset)/2, TFT_WIDTH-y_offset);
      }
    }
    // Exit and clear:
    delay(3000);
    tft.fillScreen(background_color);
    drawImage<uint16_t>("carpal_tunnel.bmp", TFT_HEIGHT/2, 0);
    drawImage<uint16_t>("mouse.bmp", TFT_HEIGHT/2, TFT_WIDTH-90);
}

void display_line_chart(int header_y, const char* header_title, int chart_width, int chart_height, doubles data, uint32_t graph_color, uint32_t line_color){
  // Define the line graph title settings:
  auto header =  text(0, header_y)
                 .value(header_title)
                 .align(center)
                 .valign(vcenter)
                 .width(chart_width)
                 .color(tft.color565(243,208,296))
                 .thickness(2);
  // Define the header height and draw the line graph title. 
  header.height(header.font_height() * 2);
  header.draw();
  // Define the line chart settings:
  auto content = line_chart(0, header.height() + header_y);
  content
  .height(chart_height) // the actual height of the line chart
  .width(chart_width) // the actual width of the line chart
  .based_on(0.0) // the starting point of the y-axis must be float
  .show_circle(false) // drawing a circle at each point, default is on
  .value(data) // passing the given data array to the line graph
  .color(line_color) // setting the line color 
  .x_role_color(graph_color) // setting the line graph color
  .x_tick_color(graph_color)
  .y_role_color(graph_color)
  .y_tick_color(graph_color)
  .draw();
}

void get_GSR_data(int calibration){
  long sum = 0;
  // Calculate the average of the last ten GSR sensor measurements to remove the glitch.
  for(int i=0;i<10;i++){
    sum += analogRead(GSR);
    delay(5);
  }
  gsr_value = (sum / 10) - calibration;
  Serial.print("GSR Value => "); Serial.println(gsr_value);
}

void get_EMG_data(){
  long sum = 0;
  // Evaluate the summation of the last 32 EMG sensor measurements.
  for(int i=0;i<32;i++){
    sum += analogRead(EMG);
  }
  // Shift the summation by five with the right shift operator (>>) to obtain the EMG value.  
  emg_value = sum >> 5;
  Serial.print("EMG Value => "); Serial.println(emg_value); Serial.println();
  delay(10);
}

mouse_fatigue_level.h

C/C++
#ifndef MOUSE_FATIGUE_LEVEL_H
#define MOUSE_FATIGUE_LEVEL_H


unsigned int mouse_fatigue_level_len = 12420;
unsigned char mouse_fatigue_level[] = {
 0x1c, 0x00, 0x00, 0x00, 0x54, 0x46, 0x4c, 0x33, 0x14, 0x00, 0x20, 0x00,
  0x04, 0x00, 0x08, 0x00, 0x0c, 0x00, 0x10, 0x00, 0x14, 0x00, 0x00, 0x00,
  0x18, 0x00, 0x1c, 0x00, 0x14, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,
  0x18, 0x00, 0x00, 0x00, 0x20, 0x00, 0x00, 0x00, 0xbc, 0x00, 0x00, 0x00,
  0x20, 0x00, 0x00, 0x00, 0x64, 0x00, 0x00, 0x00, 0x5c, 0x00, 0x00, 0x00,
  0x02, 0x00, 0x00, 0x00, 0x64, 0x04, 0x00, 0x00, 0x8c, 0x01, 0x00, 0x00,
  0x01, 0x00, 0x00, 0x00, 0xc0, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,
  0x2c, 0x30, 0x00, 0x00, 0x28, 0x30, 0x00, 0x00, 0x58, 0x2e, 0x00, 0x00,
  0x64, 0x2d, 0x00, 0x00, 0xdc, 0x2c, 0x00, 0x00, 0x68, 0x2c, 0x00, 0x00,
  0x18, 0x2a, 0x00, 0x00, 0xc4, 0x09, 0x00, 0x00, 0x70, 0x05, 0x00, 0x00,
  0xbc, 0x04, 0x00, 0x00, 0x04, 0x30, 0x00, 0x00, 0x00, 0x30, 0x00, 0x00,
  0xfc, 0x2f, 0x00, 0x00, 0xf8, 0x2f, 0x00, 0x00, 0xf4, 0x2f, 0x00, 0x00,
  0x3c, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
  0x0c, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0c, 0x00, 0x04, 0x00, 0x08, 0x00,
  0x08, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x0f, 0x00, 0x00, 0x00,
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  0x31, 0x2e, 0x35, 0x2e, 0x30, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
  0x00, 0x00, 0x00, 0x00, 0x0f, 0x00, 0x00, 0x00, 0x4d, 0x4c, 0x49, 0x52,
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  0x10, 0x00, 0x14, 0x00, 0x0e, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00,
  0x4c, 0x00, 0x00, 0x00, 0x50, 0x00, 0x00, 0x00, 0x54, 0x00, 0x00, 0x00,
  0x68, 0x00, 0x00, 0x00, 0x0e, 0x00, 0x00, 0x00, 0x14, 0x2f, 0x00, 0x00,
  0xa4, 0x2e, 0x00, 0x00, 0x20, 0x2d, 0x00, 0x00, 0x38, 0x2c, 0x00, 0x00,
  0xb0, 0x2b, 0x00, 0x00, 0x54, 0x2b, 0x00, 0x00, 0x00, 0x29, 0x00, 0x00,
  0xac, 0x08, 0x00, 0x00, 0x58, 0x04, 0x00, 0x00, 0x68, 0x03, 0x00, 0x00,
  0x74, 0x02, 0x00, 0x00, 0xa4, 0x01, 0x00, 0x00, 0xfc, 0x00, 0x00, 0x00,
  0x84, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
  0x01, 0x00, 0x00, 0x00, 0x0d, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00,
  0xe8, 0x02, 0x00, 0x00, 0x18, 0x02, 0x00, 0x00, 0x48, 0x01, 0x00, 0x00,
  0xa4, 0x00, 0x00, 0x00, 0x1c, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,
  0x6d, 0x61, 0x69, 0x6e, 0x00, 0x00, 0x0e, 0x00, 0x18, 0x00, 0x08, 0x00,
  0x0c, 0x00, 0x10, 0x00, 0x07, 0x00, 0x14, 0x00, 0x0e, 0x00, 0x00, 0x00,
  0x00, 0x00, 0x00, 0x09, 0x01, 0x00, 0x00, 0x00, 0x1c, 0x00, 0x00, 0x00,
  0x10, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x0a, 0xd3, 0xff, 0xff,
  0x00, 0x00, 0x80, 0x3f, 0x01, 0x00, 0x00, 0x00, 0x0d, 0x00, 0x00, 0x00,
  0x01, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x38, 0xfd, 0xff, 0xff,
  0x19, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x19, 0x01, 0x00, 0x00, 0x00,
  0xb8, 0xd1, 0xff, 0xff, 0x14, 0x00, 0x00, 0x00, 0x0e, 0x00, 0x00, 0x00,
  0x24, 0x00, 0x00, 0x00, 0x30, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,
  0x02, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,
  0x02, 0x00, 0x00, 0x00, 0xff, 0xff, 0xff, 0xff, 0x03, 0x00, 0x00, 0x00,
  0x08, 0x00, 0x00, 0x00, 0x49, 0x64, 0x65, 0x6e, 0x74, 0x69, 0x74, 0x79,
  0x00, 0x00, 0x00, 0x00, 0xa4, 0xd1, 0xff, 0xff, 0xa2, 0xfe, 0xff, 0xff,
  0x00, 0x00, 0x00, 0x08, 0x18, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x00, 0x00,
  0x04, 0x00, 0x00, 0x00, 0xbc, 0xd1, 0xff, 0xff, 0x01, 0x00, 0x00, 0x00,
  0x0c, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x0b, 0x00, 0x00, 0x00,
  0x08, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x2c, 0xd2, 0xff, 0xff,
  0x14, 0x00, 0x00, 0x00, 0x0d, 0x00, 0x00, 0x00, 0x24, 0x00, 0x00, 0x00,
  0x5c, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00,
  0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00,
  0xff, 0xff, 0xff, 0xff, 0x03, 0x00, 0x00, 0x00, 0x34, 0x00, 0x00, 0x00,
  0x73, 0x65, 0x71, 0x75, 0x65, 0x6e, 0x74, 0x69, 0x61, 0x6c, 0x2f, 0x64,
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  0x42, 0xff, 0xff, 0xff, 0x00, 0x00, 0x00, 0x08, 0x1c, 0x00, 0x00, 0x00,
  0x10, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x66, 0xfe, 0xff, 0xff,
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  0x03, 0x00, 0x00, 0x00, 0x0a, 0x00, 0x00, 0x00, 0x07, 0x00, 0x00, 0x00,
  0x03, 0x00, 0x00, 0x00, 0xd0, 0xd2, 0xff, 0xff, 0x14, 0x00, 0x00, 0x00,
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  0x10, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x32, 0xff, 0xff, 0xff,
  0x00, 0x00, 0x00, 0x01, 0x01, 0x00, 0x00, 0x00, 0x0a, 0x00, 0x00, 0x00,
  0x03, 0x00, 0x00, 0x00, 0x09, 0x00, 0x00, 0x00, 0x06, 0x00, 0x00, 0x00,
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  0x20, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0xff, 0xff, 0xff, 0xff,
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  0x0c, 0x00, 0x10, 0x00, 0x0b, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x04, 0x00,
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  0xb6, 0xc2, 0x89, 0x3e, 0xc7, 0x27, 0x36, 0x3e, 0xaa, 0x59, 0xee, 0xbe,
  0x84, 0x6c, 0x45, 0x3f, 0xa0, 0xdb, 0x85, 0xbf, 0x3f, 0x2e, 0x23, 0x3e,
  0xde, 0xd5, 0xff, 0xff, 0x10, 0x00, 0x00, 0x00, 0x09, 0x00, 0x00, 0x00,
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  0xb8, 0x4c, 0xef, 0xbd, 0x38, 0x3f, 0x4f, 0xbd, 0x90, 0xeb, 0xd3, 0xbc,
  0xde, 0x43, 0x2b, 0xbe, 0xae, 0x59, 0x5f, 0xbe, 0x10, 0x3c, 0xd8, 0xbd,
  0x8c, 0x51, 0xaf, 0x3e, 0xa2, 0xf5, 0xad, 0x3e, 0x08, 0x5b, 0x51, 0xbd,
  0x68, 0xcf, 0xbd, 0xbe, 0xd2, 0xe4, 0x4f, 0xbe, 0xa8, 0x49, 0x38, 0x3e,
  0x58, 0x9b, 0x14, 0x3e, 0xb0, 0x2d, 0x63, 0xbd, 0xa4, 0x80, 0x5d, 0xbe,
  0xe7, 0x4d, 0x89, 0xbe, 0xc8, 0x80, 0x21, 0x3d, 0xf8, 0x9f, 0x68, 0x3e,
  0xe0, 0x6c, 0x98, 0xbc, 0xb4, 0x79, 0x80, 0x3e, 0xda, 0xa0, 0xbf, 0x3e,
  0x02, 0x4a, 0xc0, 0xbe, 0xef, 0xf6, 0x0c, 0xbe, 0xd0, 0x05, 0x34, 0x3e,
  0x4c, 0x36, 0x2d, 0xbe, 0x90, 0x43, 0x33, 0x3e, 0xe4, 0xa2, 0xa9, 0x3e,
  0x73, 0xa9, 0xb1, 0xbe, 0x00, 0x98, 0x87, 0xbb, 0xf0, 0x53, 0x9e, 0x3e,
  0xd0, 0xac, 0x4d, 0xbd, 0xdd, 0x27, 0xac, 0xbe, 0xb0, 0xd6, 0x80, 0xbe,
  0xd0, 0xda, 0x30, 0x3e, 0x60, 0x18, 0xd6, 0x3d, 0xcc, 0x6d, 0x55, 0x3e,
  0x2e, 0xd9, 0x96, 0xbe, 0xff, 0xfc, 0x20, 0xbe, 0xac, 0xb6, 0xc8, 0xbd,
  0x40, 0x49, 0xa7, 0x3e, 0xf0, 0x16, 0x07, 0x3d, 0xff, 0x1e, 0xaa, 0xbe,
  0xd8, 0xea, 0x5a, 0x3e, 0x00, 0x49, 0x81, 0xba, 0x18, 0x87, 0x84, 0x3d,
  0x64, 0xaa, 0x5d, 0x3e, 0x98, 0x92, 0xa1, 0xbd, 0xf0, 0x04, 0xe6, 0xbd,
  0x44, 0x8e, 0x21, 0x3e, 0x03, 0x51, 0x9d, 0xbe, 0x98, 0xd6, 0x42, 0xbd,
  0xd0, 0x0b, 0xbb, 0x3c, 0x54, 0x8a, 0x71, 0x3e, 0xc0, 0xbe, 0x06, 0x3c,
  0xe4, 0xd3, 0xcb, 0x3d, 0x64, 0xc8, 0xb2, 0xbe, 0xb4, 0x64, 0xff, 0xbd,
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Credits

Kutluhan Aktar

Kutluhan Aktar

80 projects β€’ 295 followers
AI & Full-Stack Developer | @EdgeImpulse | @Particle | Maker | Independent Researcher

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