AnalysisFrontLeg_Kinematics

To implement the Raspberry Pi 4 and Bosch BNO055 IMU sensor for orientation and motion analysis Analysis of Front Leg Kinematics of Cricket Bowler Using Wearable hardwaere and python code Analyzing the front leg kinematics of a cricket bowler using a Raspberry Pi 4 and Bosch BNO055 IMU sensor involves several steps, from setting up the hardware to developing the Python code for data collection and analysis. Here's a detailed guide:

Hardware Setup:

1. Raspberry Pi Setup:

   • Set up your Raspberry Pi 4 with a microSD card and a power supply.
   • Connect the Raspberry Pi to the internet.

2. BNO055 IMU Setup:

   • Connect the BNO055 IMU sensor to the Raspberry Pi using I2C communication (SDA and SCL pins).
   • Provide power (VCC) and ground (GND) connections to the sensor.

Python Code Implementation:

1. Install Libraries:

   Install the required libraries for communication with the BNO055 sensor and data analysis.

   pip install adafruit-circuitpython-bno055 numpy matplotlib

2. Data Collection Script (imu_data_collection.py):

   Write a Python script to collect sensor data from the BNO055 IMU sensor.

   import time
   import board
   import busio
   import adafruit_bno055
   import numpy as np
   import matplotlib.pyplot as plt
   
   i2c = busio.I2C(board.SCL, board.SDA)
   sensor = adafruit_bno055.BNO055(i2c)
   
   data = {'timestamp': [], 'euler_angles': []}
   
   try:
       while True:
           timestamp = time.time()
           euler_angles = sensor.euler
   
           data['timestamp'].append(timestamp)
           data['euler_angles'].append(euler_angles)
   
           time.sleep(0.01)  # Adjust the sleep time as needed
   except KeyboardInterrupt:
       print("Data collection stopped")
   
   np.save('imu_data.npy', data)

3. Data Analysis Script (imu_data_analysis.py):

   Write a Python script to analyze and visualize the collected data.

   import numpy as np
   import matplotlib.pyplot as plt
   
   data = np.load('imu_data.npy', allow_pickle=True).item()
   
   timestamps = data['timestamp']
   euler_angles = data['euler_angles']
   
   pitch_angles = np.array([euler[1] for euler in euler_angles])
   
   plt.plot(timestamps, pitch_angles)
   plt.xlabel('Time (s)')
   plt.ylabel('Pitch Angle (degrees)')
   plt.title('Front Leg Kinematics of Cricket Bowler')
   plt.show()

Data Collection and Analysis:

1. Run the imu_data_collection.py script on your Raspberry Pi to collect sensor data. Stop the script when you've collected sufficient data by pressing Ctrl + C.

2. Transfer the imu_data.npy file to your development machine for analysis.

3. Run the imu_data_analysis.py script on your development machine to visualize the front leg kinematics (pitch angles) of the cricket bowler over time.

Fine-Tuning and Interpretation:

1. Analyze the pitch angle data to understand the bowler's leg kinematics during different phases of the bowling action.

2. You might need to apply filters or signal processing techniques to enhance the accuracy of the kinematic analysis.

3. Compare the kinematic data with known patterns of efficient bowling techniques and identify areas for improvement.

Remember that this is a basic example and advanced analysis might involve calibrating the sensor, applying sensor fusion algorithms for accurate orientation, and using machine learning for phase classification. Collaborating with experts in biomechanics, sensor fusion, and data analysis can help refine your approach. Using MPU6050 Analyzing the kinematics of a cricket player's motion using an MPU6050 sensor and a Raspberry Pi involves capturing motion data and interpreting it to understand the player's movements. Here's a step-by-step guide:

Hardware Setup:

1. Raspberry Pi Setup:

   • Set up your Raspberry Pi with a compatible operating system (Raspberry Pi OS).
   • Connect to the internet and ensure you have the necessary software libraries installed.

2. MPU6050 Connection:

   • Connect the MPU6050 sensor to the Raspberry Pi using I2C communication (SDA and SCL pins).
   • Supply power (VCC) and ground (GND) to the sensor.

Python Code Implementation:

1. Install Libraries: Install the necessary libraries for I2C communication and data analysis.

   pip install smbus2 numpy matplotlib

2. Data Collection Script (mpu6050_data_collection.py): Write a Python script to collect motion data from the MPU6050 sensor.

   import time
   import smbus2
   import numpy as np
   import matplotlib.pyplot as plt
   
   bus = smbus2.SMBus(1)  # Use 1 for Raspberry Pi 2 or 3, use 0 for older models
   
   MPU6050_ADDR = 0x68
   
   def read_raw_data(addr):
       high = bus.read_byte_data(MPU6050_ADDR, addr)
       low = bus.read_byte_data(MPU6050_ADDR, addr + 1)
       value = ((high << 8) | low)
       if value > 32768:
           value = value - 65536
       return value
   
   data = {'timestamp': [], 'acceleration': [], 'gyroscope': []}
   
   try:
       while True:
           timestamp = time.time()
           accel_x = read_raw_data(0x3B)
           accel_y = read_raw_data(0x3D)
           accel_z = read_raw_data(0x3F)
   
           gyro_x = read_raw_data(0x43)
           gyro_y = read_raw_data(0x45)
           gyro_z = read_raw_data(0x47)
   
           data['timestamp'].append(timestamp)
           data['acceleration'].append((accel_x, accel_y, accel_z))
           data['gyroscope'].append((gyro_x, gyro_y, gyro_z))
   
           time.sleep(0.01)  # Adjust the sleep time as needed
   except KeyboardInterrupt:
       print("Data collection stopped")
   
   np.save('mpu6050_data.npy', data)

3. Data Analysis Script (mpu6050_data_analysis.py): Write a Python script to analyze and visualize the collected data.

   import numpy as np
   import matplotlib.pyplot as plt
   
   data = np.load('mpu6050_data.npy', allow_pickle=True).item()
   
   timestamps = data['timestamp']
   accelerations = np.array(data['acceleration'])
   gyroscopes = np.array(data['gyroscope'])
   
   plt.figure(figsize=(12, 6))
   plt.subplot(2, 1, 1)
   plt.plot(timestamps, accelerations)
   plt.xlabel('Time (s)')
   plt.ylabel('Acceleration (raw)')
   plt.title('Acceleration Data')
   
   plt.subplot(2, 1, 2)
   plt.plot(timestamps, gyroscopes)
   plt.xlabel('Time (s)')
   plt.ylabel('Gyroscope (raw)')
   plt.title('Gyroscope Data')
   
   plt.tight_layout()
   plt.show()

Data Collection and Analysis:

1. Run the mpu6050_data_collection.py script on your Raspberry Pi to collect motion data. Stop the script when you've collected sufficient data by pressing Ctrl + C.

2. Transfer the mpu6050_data.npy file to your development machine for analysis.

3. Run the mpu6050_data_analysis.py script on your development machine to visualize the acceleration and gyroscope data over time.

Interpretation and Further Analysis:

1. Analyze the raw acceleration and gyroscope data to understand the cricket player's motion patterns.

2. Apply signal processing techniques to filter and smooth the data for better analysis results.

3. Extract meaningful features from the raw data to calculate angles, angular velocities, and accelerations.

4. Compare the motion data to known patterns of efficient cricket movements to provide insights into the player's performance.

Remember that raw data from sensors like the MPU6050 might need additional calibration and processing to accurately represent real-world motions. This example provides a basic starting point, but more sophisticated analysis and interpretation techniques might be required for comprehensive biomechanical analysis.

Calculating kinematics using the MPU6050 sensor and Arduino involves similar steps as mentioned earlier. Here's a guide on how to implement kinematics calculations using the MPU6050 sensor with Arduino:

1. Hardware Setup:

Connect the MPU6050 sensor to the Arduino board using the I2C communication protocol. The sensor provides accelerometer and gyroscope data that you'll use for kinematics calculations.

2. Arduino Code Implementation:

Write an Arduino sketch to read data from the MPU6050 sensor, convert the raw data into physical units, and perform kinematics calculations.

#include <Wire.h>
#include <MPU6050.h>

MPU6050 mpu;

void setup() {
  Wire.begin();
  Serial.begin(9600);

  mpu.initialize();
  mpu.setDMPEnabled(true);
}

void loop() {
  // Read accelerometer and gyroscope data
  int16_t accelX = mpu.getAccelerationX();
  int16_t accelY = mpu.getAccelerationY();
  int16_t accelZ = mpu.getAccelerationZ();
  
  int16_t gyroX = mpu.getRotationX();
  int16_t gyroY = mpu.getRotationY();
  int16_t gyroZ = mpu.getRotationZ();

  // Convert raw data to physical units
  float accelX_mss = accelX / 16384.0;
  float accelY_mss = accelY / 16384.0;
  float accelZ_mss = accelZ / 16384.0;

  float gyroX_dps = gyroX / 131.0;
  float gyroY_dps = gyroY / 131.0;
  float gyroZ_dps = gyroZ / 131.0;

  // Perform kinematics calculations (example: linear acceleration)
  float linearAccelX = accelX_mss - gravityX;
  float linearAccelY = accelY_mss - gravityY;
  float linearAccelZ = accelZ_mss - gravityZ;

  // Print the calculated kinematic parameters
  Serial.print("Linear Acceleration X: ");
  Serial.println(linearAccelX);
  
  delay(100);
}

In this example, the sketch reads raw accelerometer and gyroscope data from the MPU6050 sensor, converts the data to physical units, and calculates linear acceleration by subtracting the gravity component. You can extend this by integrating, differentiating, and applying kinematic equations based on your specific application.

3. Visualization and Interpretation:

Visualize and interpret the calculated kinematic parameters using the Arduino Serial Monitor or other visualization tools. You can also use serial communication to transmit data to a computer for more advanced analysis.

Identifying human joint angles using soft strain sensors and the MPU6050 sensor on an Arduino involves capturing data from the sensors, processing the strain data, and calculating joint angles. Here's a simplified example of how you might approach this:

1. Hardware Setup:

• Connect the MPU6050 sensor and soft strain sensors to the Arduino using appropriate pins and wiring.
• Ensure proper power supply for both sensors.

2. Arduino Code Implementation:

Install the "MPU6050" library using the Arduino Library Manager and write the code to read data from the sensors and calculate joint angles.

#include <Wire.h>
#include <MPU6050.h>

MPU6050 mpu;

const int strainSensorPin = A0;  // Analog pin for the strain sensor
const int referenceResistance = 10000;  // Resistance at no force applied
const float voltageSupply = 5.0;  // Supply voltage to the strain sensor

void setup() {
  Wire.begin();
  Serial.begin(9600);

  mpu.initialize();
  mpu.setDMPEnabled(true);
}

void loop() {
  // Read MPU6050 data
  int16_t accelX = mpu.getAccelerationX();
  int16_t accelY = mpu.getAccelerationY();
  int16_t accelZ = mpu.getAccelerationZ();

  // Read strain sensor data
  int strainSensorValue = analogRead(strainSensorPin);

  // Convert strain sensor data to force
  float resistance = (1023.0 / strainSensorValue - 1) * referenceResistance;
  float force = voltageSupply / resistance;

  // Calculate joint angles using sensor data
  float jointAngleX = atan2(accelY, accelZ) * (180.0 / M_PI);
  float jointAngleY = atan2(-accelX, accelZ) * (180.0 / M_PI);

  // Print the calculated joint angles and force
  Serial.print("Joint Angle X: ");
  Serial.print(jointAngleX);
  Serial.print(" degrees\t");

  Serial.print("Joint Angle Y: ");
  Serial.print(jointAngleY);
  Serial.print(" degrees\t");

  Serial.print("Force: ");
  Serial.print(force);
  Serial.println(" N");

  delay(100);
}

In this example, the code reads data from the MPU6050 sensor (acceleration values) and a soft strain sensor (analog input). It converts the strain sensor's analog reading into a force value. Then, it calculates joint angles (pitch and roll) based on the acceleration values. Note that this is a simplified example, and depending on the arrangement of the strain sensors and the intended joint angles, you might need to adjust the calculations accordingly.

3. Visualization and Interpretation:

Interpret the calculated joint angles and force values to analyze human joint movements. Visualize the data using the Arduino Serial Monitor or transfer it to a computer for further analysis and visualization.

Please note that this example assumes a simplified setup and calculation. Real-world implementations might require sensor calibration, noise reduction, more advanced sensor fusion techniques, and a more accurate model for joint angle calculations. Remember that kinematics calculations might require careful handling of noise, calibration, and numerical errors. Advanced techniques such as sensor fusion algorithms could be applied to improve accuracy.

Here's a merged idea that combines elements from both of the provided ideas:

Title: Enhancing Cricket Bowler Performance and Injury Prevention through Wearable Sensor Technology

Introduction:

In the game of cricket, fast bowlers are susceptible to injuries due to high-intensity actions during various phases of bowling. This article introduces an innovative approach that utilizes wearable sensor technology to improve both performance and injury prevention for cricket bowlers. Drawing inspiration from recent developments in joint angle identification using soft strain sensors and computational calibration techniques, this study introduces a novel wearable sensor unit powered by the Bosch BNO055 inertial measurement unit (IMU) sensor. This unit provides a comprehensive analysis of the bowler's front leg motion, contributing to refining techniques, enhancing performance, and reducing the risk of injuries.

Section 1: Soft Strain Sensors for Ankle Motion Analysis:

Building upon the data-driven approach used for joint angle identification, this study incorporates soft strain sensors to precisely measure multiple degrees of freedom (DOF) ankle motions. To address the inherent nonlinearity and hysteresis of soft sensors, a wearable sensing brace design is introduced. By strategically attaching the sensors to shin links rather than directly to the ankle joint, the impact of external disturbances during joint motions is minimized. This design also leads to improved measurement repeatability and reduced hysteresis. Additionally, a computationally efficient calibration method is implemented using sim-to-real transfer learning, leveraging results from musculoskeletal simulation (OpenSim) and sensor data. This method accelerates calibration speed while maintaining tracking accuracy, facilitating real-time analysis of ankle motions.

Section 2: Wearable Sensor Technology for Cricket Bowlers:

In the context of cricket bowling, the front leg motion is of paramount importance for efficient delivery and injury prevention. To address this, a miniature and economical wearable sensor unit is proposed, featuring the Bosch BNO055 IMU sensor. Bowlers can comfortably wear this unit during matches and training sessions. The unit captures quaternion data that enables the calculation of knee flexion during the crucial front foot contact (FFC) phase. This novel approach provides detailed insights into the biomechanics of a bowler's delivery, aiding in refining techniques and minimizing the risk of injuries.

Section 3: Data-Driven Approach for Performance Improvement:

The collected motion data from the wearable sensor unit enables the identification of various bowling phases, including the run-up, predelivery stride (PDS), mid-bound (MB), FFC, ball release, and follow-through. Through supervised machine-learning algorithms, statistical features extracted from sensor data are evaluated to classify these phases accurately. This data-driven approach offers coaches and players evidence-based insights for training and technique enhancement. By analyzing motion data and phase identification, bowlers can optimize their performance, correct improper landing techniques, and ensure balanced follow-through, ultimately leading to enhanced bowling efficiency and injury prevention.

Conclusion:

The integration of wearable sensor technology, soft strain sensors, and advanced computational techniques presents a comprehensive solution for enhancing cricket bowler performance and reducing the risk of injuries. The proposed wearable sensor unit combined with accurate joint angle identification and phase classification provides actionable insights for coaches and players, enabling data-driven training and evidence-based technique refinement. As technology continues to advance, this approach has the potential to revolutionize how cricket bowlers train, perform, and safeguard their well-being on the field.

Enhancing cricket bowler performance and injury prevention using a Raspberry Pi and MPU6050 IMU (Inertial Measurement Unit) involves designing and implementing a system that captures, processes, and analyzes the motion data of the bowler during different phases of bowling. Here's a step-by-step guide on how to implement this idea:

Hardware Setup:

1. Raspberry Pi Setup:

   • Set up your Raspberry Pi with the required operating system (e.g., Raspberry Pi OS).
   • Ensure you have Python installed on the Raspberry Pi.

2. MPU6050 Connection:

   • Connect the MPU6050 sensor to the Raspberry Pi using I2C communication (SDA and SCL pins).
   • Supply power (VCC) and ground (GND) to the sensor.

3. Wearable Unit Design:

   • Create a wearable unit using a suitable platform (e.g., a strap or brace) to attach the Raspberry Pi and MPU6050 sensor to the bowler's front leg securely.

Software Implementation:

1. Install Required Libraries:

   • Install the necessary Python libraries on the Raspberry Pi.

     pip install smbus2 numpy

2. Collecting Data:

   • Write a Python script to continuously read data from the MPU6050 sensor (accelerometer and gyroscope data) and save it to a file.

     import smbus2
     import time
     import numpy as np
     
     bus = smbus2.SMBus(1)  # I2C bus number
     
     MPU6050_ADDR = 0x68
     
     def read_data(addr):
         high = bus.read_byte_data(MPU6050_ADDR, addr)
         low = bus.read_byte_data(MPU6050_ADDR, addr + 1)
         value = ((high << 8) | low)
         if value > 32768:
             value = value - 65536
         return value
     
     data = []
     
     try:
         while True:
             timestamp = time.time()
             accel_x = read_data(0x3B)
             accel_y = read_data(0x3D)
             accel_z = read_data(0x3F)
             gyro_x = read_data(0x43)
             gyro_y = read_data(0x45)
             gyro_z = read_data(0x47)
             data.append([timestamp, accel_x, accel_y, accel_z, gyro_x, gyro_y, gyro_z])
             time.sleep(0.01)  # Adjust the sleep time as needed
     except KeyboardInterrupt:
         np.savetxt("bowler_motion_data.csv", data, delimiter=",")

3. Data Processing and Analysis:

   • Transfer the collected data from the Raspberry Pi to your computer for further analysis.
   • Use software like Python with libraries such as NumPy and pandas to load, preprocess, and analyze the motion data.
   • Calculate joint angles, acceleration, and other relevant parameters based on the gyroscope and accelerometer data.

4. Phase Classification:

   • Develop algorithms to identify different phases of the bowling motion based on the collected data.
   • Use machine learning techniques (e.g., clustering, classification) to classify the phases such as run-up, PDS, MB, FFC, ball release, and follow-through.

Visualization and Feedback:

• Visualize the collected motion data and the identified phases using graphs, charts, and animations.
• Provide feedback to the bowler and coaches based on the analysis results to optimize performance and prevent injuries.
• Use the insights gained to improve bowling techniques, balance, and landing postures.

Testing and Refinement:

• Test the system during practice sessions to ensure accuracy and reliability.
• Refine the algorithms, data collection rate, and processing methods based on the practical results and user feedback.

By following these steps, you can implement a wearable sensor system using the Raspberry Pi and MPU6050 IMU to enhance cricket bowler performance and injury prevention through data-driven analysis and feedback.