PSoC6 Data Collection to CSV log file

1. Collect dataset using Eclipse ModusToolbox

1.1 Create Project through ModusToolbox

1.2 Steps to Configure the Code:

Navigate to the gesture.h File:
- Locate the gesture.h file within the source folder of the project directory.
Modify the Macro for Data Collection Mode:
- Open the gesture.h file.
- Find the line defining GESTURE_DATA_COLLECTION_MODE.
- Change its value from 0u to 1u.
  #define GESTURE_DATA_COLLECTION_MODE 1u
- This change enables printing sensor data to the terminal instead of running the inference engine.

Navigate to the Makefile:
- Locate the Makefile in the root directory of the project.
Update the Shield:
- Open the Makefile.
- Look for the shield configuration line:
```
SHIELD_DATA_COLLECTION = CY_028_SENSE_SHIELD_v1
```
- Change this to:
```
SHIELD_DATA_COLLECTION = CY_028_SENSE_SHIELD_v2
```
- This updates the configuration to use the CY_028_SENSE_SHIELD_v2 for gathering IMU data.

1.3 Build and Launch the application

1.4 Let's collect dataset by Eclipse

Open modus-shell

Navigate to the folder and install the library.

pip install -r requirements.txt

To collect data from the sensor, use the command line as below:

./collect.sh <COM PORT> <GESTURE NAME> <PERSON

ตัวอย่าง:

./collect.sh COM6 circle Chhun

2. Convert .txt dataset to .csv dataset

After data collection, the dataset is stored in the Machine_Learning_Gesture_Classification\train\gesture_data\<your gesture name> directory as a .txt file

2.1 Plot graph

Create project in google colab: https://colab.research.google.com/drive/1zkqpE8NGNUJv6uIUV-4ItKIbkxr1n4qj?usp=sharing
Install library
```
!pip install tsaug
```

Using below code for plot the graph for each sensor dataset:

import matplotlib.pyplot as plt

# Define the file name
filename = '<your .txt file location'

# Initialize lists to store data
gyro_x, gyro_y, gyro_z, accel_x, accel_y, accel_z = [], [], [], [], [], []

# Read data from the file
with open(filename, 'r') as file:
    for line in file:
        # Split the line by comma
        data = line.strip().split(',')
        # Check if the line has 6 values
        if len(data) == 6:
            # Check if all values in the line are not '-'
            if all(value != '-' for value in data):
                # Append data to respective lists
                gyro_x.append(float(data[0]))
                gyro_y.append(float(data[1]))
                gyro_z.append(float(data[2]))
                accel_x.append(float(data[3]))
                accel_y.append(float(data[4]))
                accel_z.append(float(data[5]))

# Create time values (assuming each line represents a time step)
time = list(range(len(accel_x)))

# Plot accelerometer and gyroscope data with a wider figure
plt.figure(figsize=(16, 8))  # Increase width for a longer x-axis

# Set a main title for the entire figure
plt.suptitle('Square Dataset', fontsize=20)

# Plot accelerometer data
plt.subplot(2, 1, 1)
plt.plot(time, accel_x, label='Accel X')
plt.plot(time, accel_y, label='Accel Y')
plt.plot(time, accel_z, label='Accel Z')
plt.title('Accelerometer Data', fontsize=16)  # Title for this subplot
plt.xlabel('Time', fontsize=12)
plt.ylabel('Acceleration', fontsize=12)
plt.legend()

# Plot gyroscope data
plt.subplot(2, 1, 2)
plt.plot(time, gyro_x, label='Gyro X')
plt.plot(time, gyro_y, label='Gyro Y')
plt.plot(time, gyro_z, label='Gyro Z')
plt.title('Gyroscope Data', fontsize=16)  # Title for this subplot
plt.xlabel('Time', fontsize=12)
plt.ylabel('Angular Velocity', fontsize=12)
plt.legend()

# Adjust layout to prevent overlap
plt.tight_layout(rect=[0, 0, 1, 0.95])  # Leave space for main title
plt.show()