目次
WEEK11 Input devices
WEEK11 Input devices
Group Assignment
probe an input device’s analog levels and digital signals
Individual Assignment
measure something: add a sensor to a microcontroller board that you have designed and read it
For this assignment, I would like to delve deeper into the use of the accelerometer sensor in the Final Project. In the Final Project, the goal is to attach an accelerometer sensor to a racket and display the swing speed and the number of practice swings on a connected smartphone via Bluetooth.
First, I will consider how to derive the speed from the accelerometer data (X, Y, Z acceleration) and gyro data (GX, GY, GZ). Next, I will think about how to detect the start and end points of a swing.
I will use the board I made in Session 8.
Creating sample data
First, I execute the sample code in the Arduino IDE to display the accelerometer and gyro data from the MPU6050 on the Serial Monitor.
Then, I will copy the data displayed on the Serial Monitor to create a sample data set.
The board was not recognized properly from the Arduino IDE, and the i2c_scanner program used in Assignment 8 helped solve the problem. The cause was a simple mistake of using the wrong board.
// MPU-6050 Short Example Sketch
// By Arduino User JohnChi
// August 17, 2014
// Public Domain
#include<Wire.h>
const int MPU_addr=0x68; // I2C address of the MPU-6050
int16_t AcX,AcY,AcZ,Tmp,GyX,GyY,GyZ;
void setup(){
Wire.begin();
Wire.beginTransmission(MPU_addr);
Wire.write(0x6B); // PWR_MGMT_1 register
Wire.write(0); // set to zero (wakes up the MPU-6050)
Wire.endTransmission(true);
Serial.begin(9600);
}
void loop(){
Wire.beginTransmission(MPU_addr);
Wire.write(0x3B); // starting with register 0x3B (ACCEL_XOUT_H)
Wire.endTransmission(false);
Wire.requestFrom(MPU_addr,14,true); // request a total of 14 registers
AcX=Wire.read()<<8|Wire.read(); // 0x3B (ACCEL_XOUT_H) & 0x3C (ACCEL_XOUT_L)
AcY=Wire.read()<<8|Wire.read(); // 0x3D (ACCEL_YOUT_H) & 0x3E (ACCEL_YOUT_L)
AcZ=Wire.read()<<8|Wire.read(); // 0x3F (ACCEL_ZOUT_H) & 0x40 (ACCEL_ZOUT_L)
Tmp=Wire.read()<<8|Wire.read(); // 0x41 (TEMP_OUT_H) & 0x42 (TEMP_OUT_L)
GyX=Wire.read()<<8|Wire.read(); // 0x43 (GYRO_XOUT_H) & 0x44 (GYRO_XOUT_L)
GyY=Wire.read()<<8|Wire.read(); // 0x45 (GYRO_YOUT_H) & 0x46 (GYRO_YOUT_L)
GyZ=Wire.read()<<8|Wire.read(); // 0x47 (GYRO_ZOUT_H) & 0x48 (GYRO_ZOUT_L)
Serial.print("AcX = "); Serial.print(AcX);
Serial.print(" , AcY = "); Serial.print(AcY);
Serial.print(" , AcZ = "); Serial.print(AcZ);
//Serial.print(" , Tmp = "); Serial.print(Tmp/340.00+36.53); //equation for temperature in degrees C from datasheet
Serial.print(" , Gx = "); Serial.print(GyX);
Serial.print(" , Gy = "); Serial.print(GyY);
Serial.print(" , Gz = "); Serial.println(GyZ);
delay(333);
}
Sample data
AcX = -1340, AcY = 16344, AcZ = -2480, Gx = -4480, Gy = 5981, Gz = 337
AcX = -1360, AcY = 16320, AcZ = -2564, Gx = -4480, Gy = 5977, Gz = 384
AcX = -1468, AcY = 16364, AcZ = -2372, Gx = -4448, Gy = 5974, Gz = 372
AcX = -1392, AcY = 16400, AcZ = -2460, Gx = -4448, Gy = 5985, Gz = 358
AcX = -1528, AcY = 16444, AcZ = -2468, Gx = -4432, Gy = 5973, Gz = 366
AcX = -1624, AcY = 16184, AcZ = -2280, Gx = -4400, Gy = 5837, Gz = 680
AcX = -1456, AcY = 16504, AcZ = -2772, Gx = -4416, Gy = 6145, Gz = 314
AcX = -1428, AcY = 16300, AcZ = -2388, Gx = -4400, Gy = 5972, Gz = 327
AcX = 840, AcY = 14964, AcZ = -4496, Gx = -4400, Gy = -396, Gz = 4395
AcX = 3860, AcY = 13236, AcZ = -5996, Gx = -4384, Gy = 5496, Gz = -35
AcX = -11640, AcY = -20580, AcZ = -16856, Gx = -4384, Gy = -32768, Gz = 18097
AcX = 2508, AcY = -2776, AcZ = -14324, Gx = -4368, Gy = 8152, Gz = -21
AcX = 1528, AcY = -1016, AcZ = -15804, Gx = -4336, Gy = 19600, Gz = 5028
AcX = -2628, AcY = -20, AcZ = -15544, Gx = -4336, Gy = -2376, Gz = -14080
AcX = -13568, AcY = 5092, AcZ = -5032, Gx = -4336, Gy = 5002, Gz = 1136
AcX = -13760, AcY = 8172, AcZ = -3764, Gx = -4352, Gy = 6979, Gz = 217
AcX = -12484, AcY = 8592, AcZ = -4688, Gx = -4304, Gy = 6213, Gz = 507
AcX = -12988, AcY = 8584, AcZ = -2780, Gx = -4336, Gy = 6077, Gz = -387
AcX = -13196, AcY = 8776, AcZ = -2772, Gx = -4288, Gy = 5948, Gz = 299
AcX = -12796, AcY = 9148, AcZ = -2796, Gx = -4304, Gy = 6088, Gz = 429
AcX = -32768, AcY = 32767, AcZ = -32768, Gx = -4272, Gy = 32767, Gz = -32768
AcX = 7804, AcY = -5236, AcZ = -14148, Gx = -4304, Gy = 834, Gz = -359
AcX = 5348, AcY = -5744, AcZ = -24352, Gx = -4288, Gy = 6183, Gz = 14962
AcX = -11584, AcY = 6648, AcZ = 2876, Gx = -4272, Gy = 4733, Gz = -7217
AcX = -13332, AcY = 8624, AcZ = 1364, Gx = -4272, Gy = 6619, Gz = 1104
AcX = -13196, AcY = 9528, AcZ = -5024, Gx = -4256, Gy = 3142, Gz = -362
AcX = -11168, AcY = 11260, AcZ = 2920, Gx = -4272, Gy = 4108, Gz = 373
Execution Results:
A Python code that derives speed from sample data generated by ChatGPT
ChatGPT Prompt
Please write a Python code that calculates the speed in kilometers per hour from the data obtained from MPU-6050 (acceleration X, Y, Z & gyro Gx, Gy, Gz). The speed should be displayed as a single value by integrating all the data. Please use a Kalman filter.
Code
import numpy as np
def read_sensor_data(file_path):
sensor_data = []
with open(file_path, 'r') as file:
for line in file:
line = line.strip().replace(" ", "")
parts = line.split(",")
values = {
'ax': int(parts[0].split("=")[1]),
'ay': int(parts[1].split("=")[1]),
'az': int(parts[2].split("=")[1]),
'gx': int(parts[3].split("=")[1]),
'gy': int(parts[4].split("=")[1]),
'gz': int(parts[5].split("=")[1])
}
sensor_data.append(values)
return sensor_data
def kalman_filter(sensor_data, dt, accel_scale, gyro_scale, g):
# Calculate velocity and orientation from accelerometer and gyroscope data
velocity = np.array([0.0, 0.0, 0.0]) # Initial velocity
orientation = np.array([0.0, 0.0, 0.0]) # Orientation (roll, pitch, yaw)
covariance = np.eye(6) # Covariance matrix for estimating errors in velocity and orientation
process_noise = np.diag([0.1, 0.1, 0.1, 0.01, 0.01, 0.01]) # Process noise
measurement_noise = np.diag([20.0, 20.0, 20.0, 0.1, 0.1, 0.1]) # Measurement noise
I = np.eye(6) # Identity matrix
for data in sensor_data:
ax, ay, az = data['ax'] / accel_scale * g, data['ay'] / accel_scale * g, data['az'] / accel_scale * g
gx, gy, gz = data['gx'] / gyro_scale, data['gy'] / gyro_scale, data['gz'] / gyro_scale
# Update orientation based on gyroscope data
orientation += np.array([gx, gy, gz]) * dt
# Update state estimates
state = np.hstack((velocity, orientation))
state[0:3] += np.array([ax, ay, az]) * dt # Update velocity
# Prediction step of the Kalman filter
covariance = covariance + process_noise
# Update step
kalman_gain = covariance @ np.linalg.inv(covariance + measurement_noise)
state += kalman_gain @ (np.hstack([ax, ay, az, gx, gy, gz]) - state)
covariance = (I - kalman_gain) @ covariance
velocity = state[0:3] # Updated velocity
orientation = state[3:6] # Updated orientation
return velocity
def convert_velocity_to_kmph(velocity):
speed_ms = np.linalg.norm(velocity) # Velocity in m/s
speed_kmph = speed_ms * 3.6 # Convert m/s to km/h
return speed_kmph
def main():
file_path = "C:/Users/budob/Downloads/mpu6050.txt"
sensor_data = read_sensor_data(file_path)
accel_scale = 16384.0 # Accelerometer scale factor
gyro_scale = 131.0 # Gyroscope scale factor
g = 9.81 # Acceleration due to gravity in m/s^2
dt = 0.1 # Sampling interval in seconds
velocity = kalman_filter(sensor_data, dt, accel_scale, gyro_scale, g)
speed_kmph = convert_velocity_to_kmph(velocity)
print(f"Estimated speed: {speed_kmph:.2f} km/h")
if __name__ == "__main__":
main()
Execution Results
How to detect the start and end points of a swing
To measure accurate hourly speed and the number of practice swings, it is necessary to determine the starting and ending points of the swing from the data.
The following code is particularly organized to read the sensor data from a file, analyze the data to detect movements based on the acceleration changes, and then print out where these movements start and stop.
ChatGPT Prompt
Please write a Python code that attaches an MPU6050 accelerometer to a badminton racket to determine the starting and ending points of a swing. The data should be read from the uploaded file and the code should also use gyroscope data.
Code
import re
def read_sensor_data(file_path):
# Read data from the file line by line into a list
with open(file_path, 'r') as file:
lines = file.readlines()
return lines
def detect_swings(lines, accel_threshold=5000, gyro_threshold=2000):
previous_acx, previous_gz = None, None
swings = []
swing_active = False
start_line = None
for i, line in enumerate(lines):
# Use regular expression to extract the values of AcX and Gz
match = re.search(r"AcX = ([-\d]+),.*Gz = ([-\d]+)", line)
if match:
acx, gz = int(match.group(1)), int(match.group(2))
# Detect the start of a swing
if not swing_active and (abs(acx - (previous_acx if previous_acx is not None else 0)) > accel_threshold or
abs(gz - (previous_gz if previous_gz is not None else 0)) > gyro_threshold):
swing_active = True
start_line = i + 1 # Line numbers start from 1
# Detect the end of a swing
elif swing_active and abs(acx - previous_acx) <= accel_threshold and abs(gz - previous_gz) <= gyro_threshold:
swings.append((start_line, i))
swing_active = False
previous_acx, previous_gz = acx, gz
# Handle the last swing if it did not end within the data
if swing_active:
swings.append((start_line, len(lines)))
return swings
def main():
file_path = "C:\\Users\\budob\\Downloads\\mpu6050.txt"
lines = read_sensor_data(file_path)
swings = detect_swings(lines)
for swing in swings:
# Print the start and end line numbers of each detected swing
print(f"Swing started at line {swing[0]} and ended at line {swing[1]}")
if __name__ == "__main__":
main()
Execution Results
Going forward, it will be necessary to repeatedly test and adjust the threshold to improve the accuracy of the detection.
Afterward
As I progressed with the Final Project, I realized that the above method was not very practical. Therefore, I decided to proceed with development using ML (TensorFlow Lite). By adjusting the threshold of the accelerometer during the acquisition of training data, it is possible to obtain more accurate data.
Preparation for Machine Learning Using TensorFlow
Obtain acceleration and gyroscopic data from a sensor device attached to the racket. Perform about 10 swings for each shot and save the obtained dataset to a CSV file.
Initially, I received the dataset via BLE, but since it took over 40 seconds to receive data for a single swing, I switched to data acquisition via USB. To obtain swing data, a sufficiently long cable was required, so I connected the sensor to the PC with a 5m USB cable, but the sensor could not be recognized. Maybe the cable is too long.
Ultimately, I resolved the issue by connecting a shorter USB cable (1.5m) to a smartphone, as shown in the photo.
I used the following code to obtain the data.
Arduino IDE -> File -> Examples -> Seeed_Arduino_LSM6DS3 ->IMUCapture.ino
#include <LSM6DS3.h>
#include <Wire.h>
//Create a instance of class LSM6DS3
LSM6DS3 myIMU(I2C_MODE, 0x6A); //I2C device address 0x6A
float aX, aY, aZ, gX, gY, gZ;
const float accelerationThreshold = 5.5; // threshold of significant in G's
const int numSamples = 119;
int samplesRead = numSamples;
void setup() {
// put your setup code here, to run once:
Serial.begin(9600);
while (!Serial);
//Call .begin() to configure the IMUs
if (myIMU.begin() != 0) {
Serial.println("Device error");
} else {
Serial.println("aX,aY,aZ,gX,gY,gZ");
}
}
void loop() {
// wait for significant motion
while (samplesRead == numSamples) {
// read the acceleration data
aX = myIMU.readFloatAccelX();
aY = myIMU.readFloatAccelY();
aZ = myIMU.readFloatAccelZ();
// sum up the absolutes
float aSum = fabs(aX) + fabs(aY) + fabs(aZ);
// check if it's above the threshold
if (aSum >= accelerationThreshold) {
// reset the sample read count
samplesRead = 0;
break;
}
}
// check if the all the required samples have been read since
// the last time the significant motion was detected
while (samplesRead < numSamples) {
// check if both new acceleration and gyroscope data is
// available
// read the acceleration and gyroscope data
samplesRead++;
// print the data in CSV format
Serial.print(myIMU.readFloatAccelX(), 3);
Serial.print(',');
Serial.print(myIMU.readFloatAccelY(), 3);
Serial.print(',');
Serial.print(myIMU.readFloatAccelZ(), 3);
Serial.print(',');
Serial.print(myIMU.readFloatGyroX(), 3);
Serial.print(',');
Serial.print(myIMU.readFloatGyroY(), 3);
Serial.print(',');
Serial.print(myIMU.readFloatGyroZ(), 3);
Serial.println();
if (samplesRead == numSamples) {
// add an empty line if it's the last sample
Serial.println();
}
}
}
10 reps dataset obtained from the sensor
13:22:45 Connected to CDC device
13:22:45 aX,aY,aZ,gX,gY,gZ
13:22:55 -4.046,0.098,-1.801,337.120,458.780,170.940
13:22:55 -4.887,0.125,-2.002,387.870,497.560,142.030
13:22:55 -5.703,0.166,-2.113,443.380,547.610,108.850
13:22:55 -6.665,0.232,-2.162,473.130,600.110,79.170
13:22:55 -7.700,0.327,-2.164,469.700,639.310,36.960
13:22:55 -8.966,0.593,-2.209,451.990,692.650,8.820
13:22:55 -10.118,1.213,-2.086,387.870,730.730,-10.850
13:22:55 -11.878,1.642,-1.399,306.810,772.730,-8.680
13:22:55 -13.372,1.641,-0.381,173.180,792.050,14.280
13:22:55 -14.671,0.580,1.996,152.110,793.730,35.630
13:22:55 -14.989,-3.426,6.087,286.650,734.510,0.350
13:22:55 -13.537,-12.760,13.999,386.750,501.830,-343.700
13:22:55 -13.221,-15.989,15.989,592.620,-166.250,-1067.290
13:22:55 -15.989,-15.989,15.989,561.050,-927.710,-1499.820
13:22:55 -15.989,-10.808,15.989,685.160,-1817.690,-1620.500
13:22:55 -15.989,-5.484,15.989,1012.200,-2293.480,-1295.770
13:22:55 -15.989,8.638,15.989,1859.760,-2293.480,329.910
13:22:55 -15.989,6.026,15.028,1332.100,-2293.480,1521.730
13:22:55 -15.989,-15.989,-6.754,1129.450,-1791.510,1292.970
13:22:55 -15.989,-15.989,-15.978,172.340,-984.130,663.250
13:22:55 -15.989,-15.989,-15.978,-351.890,-297.850,206.780
13:22:55 -5.212,-14.577,-15.978,40.460,200.340,-137.550
13:22:55 -2.355,-3.890,-9.173,10.360,519.820,-238.770
13:22:55 -6.211,3.840,-0.295,-83.790,610.470,-18.060
13:22:55 -5.547,4.524,6.456,-120.120,438.970,140.350
13:22:55 -3.000,0.917,9.175,-132.510,200.130,188.790
13:22:55 -1.381,-0.838,8.011,-73.640,-69.440,173.950
13:22:55 -1.378,-2.054,3.609,67.480,-221.830,135.870
13:22:55 -1.594,-2.390,0.377,142.450,-230.090,101.780
13:22:55 -1.392,-2.441,-1.893,149.030,-185.780,66.710
13:22:55 -1.059,-2.132,-3.161,69.090,-89.390,20.370
13:22:55 -0.873,-1.450,-3.131,7.210,-8.960,2.310
13:22:55 -0.962,-0.659,-2.285,-56.700,55.160,9.940
13:22:55 -1.074,-0.383,-1.607,-77.700,85.680,22.470
13:22:55 -1.112,-0.739,-0.998,-52.500,94.150,23.100
13:22:55 -0.953,-1.220,-0.644,-18.270,92.960,8.260
13:22:55 -0.807,-1.261,-0.360,-1.400,87.710,-4.970
13:22:55 -0.734,-1.147,-0.155,7.350,82.110,-17.500
13:22:55 -0.682,-0.946,0.038,14.490,71.750,-25.690
13:22:55 -0.695,-0.589,0.074,11.830,57.050,-27.020
13:22:55 -0.749,-0.363,-0.080,1.260,44.100,-20.510
13:22:55 -0.750,-0.366,-0.264,-4.130,36.540,-15.820
13:22:55 -0.698,-0.462,-0.412,0.350,33.040,-13.650
13:22:55 -0.549,-0.529,-0.430,4.200,30.240,-14.350
13:22:55 -0.452,-0.502,-0.464,0.490,29.050,-14.630
13:22:55 -0.479,-0.363,-0.502,-9.240,28.210,-8.610
13:22:55 -0.561,-0.234,-0.596,-19.950,26.880,0.210
13:22:55 -0.616,-0.306,-0.698,-22.750,27.090,9.520
13:22:55 -0.574,-0.531,-0.692,-17.920,27.160,6.650
13:22:55 -0.459,-0.749,-0.659,-20.720,26.810,2.380
13:22:56 -0.473,-0.732,-0.668,-22.680,26.460,-1.400
13:22:56 -0.524,-0.703,-0.670,-17.080,26.110,-4.130
13:22:56 -0.504,-0.686,-0.632,-11.130,25.130,-5.600
13:22:56 -0.488,-0.624,-0.567,-6.020,22.890,-6.160
13:22:56 -0.479,-0.583,-0.525,0.700,20.090,-5.250
13:22:56 -0.462,-0.585,-0.482,9.520,16.800,-5.600
13:22:56 -0.453,-0.561,-0.455,10.640,13.090,-5.460
13:22:56 -0.446,-0.562,-0.477,2.800,10.290,-5.320
13:22:56 -0.467,-0.549,-0.491,-7.350,7.210,-4.620
13:22:56 -0.497,-0.547,-0.501,-13.860,4.410,-2.450
13:22:56 -0.492,-0.603,-0.503,-9.940,-0.910,-1.610
13:22:56 -0.472,-0.655,-0.554,-1.540,-5.040,-1.540
13:22:56 -0.463,-0.741,-0.653,6.300,-7.700,-3.850
13:22:56 -0.376,-0.778,-0.693,15.330,-8.190,-8.820
13:22:56 -0.340,-0.738,-0.658,18.410,-9.100,-13.650
13:22:56 -0.332,-0.562,-0.643,8.680,-10.360,-13.860
13:22:56 -0.402,-0.409,-0.697,-2.310,-10.640,-9.730
13:22:56 -0.462,-0.393,-0.734,-7.420,-10.150,-5.250
13:22:56 -0.438,-0.444,-0.769,-7.560,-9.520,-2.030
13:22:56 -0.419,-0.595,-0.741,3.570,-8.120,-2.520
13:22:56 -0.335,-0.646,-0.679,5.880,-8.750,-4.900
13:22:56 -0.332,-0.609,-0.700,4.270,-9.170,-5.460
13:22:56 -0.398,-0.538,-0.827,5.950,-7.000,-3.430
13:22:56 -0.402,-0.564,-0.862,8.330,-2.240,-2.800
13:22:56 -0.386,-0.594,-0.837,6.860,1.190,-3.430
13:22:56 -0.363,-0.615,-0.794,2.590,4.620,-4.900
13:22:56 -0.364,-0.579,-0.719,1.260,5.810,-4.970
13:22:56 -0.374,-0.536,-0.663,1.680,5.460,-4.060
13:22:56 -0.388,-0.510,-0.630,-2.170,4.550,-2.660
13:22:56 -0.399,-0.529,-0.632,-3.010,3.780,-1.470
13:22:56 -0.414,-0.554,-0.644,-4.480,2.800,0.280
13:22:56 -0.420,-0.642,-0.662,0.700,2.240,-0.280
13:22:56 -0.386,-0.723,-0.641,0.770,1.680,-5.180
13:22:56 -0.360,-0.693,-0.612,-2.100,0.910,-9.030
13:22:56 -0.370,-0.578,-0.633,-8.960,-0.700,-8.680
13:22:56 -0.417,-0.512,-0.689,-5.810,-0.770,-6.160
13:22:56 -0.428,-0.524,-0.738,-10.850,-0.630,-4.970
13:22:56 -0.430,-0.577,-0.794,-9.940,1.400,-4.620
13:22:56 -0.407,-0.632,-0.818,-7.770,4.200,-5.530
13:22:56 -0.389,-0.656,-0.769,-4.200,6.090,-7.910
13:22:56 -0.376,-0.586,-0.740,-8.750,6.790,-7.980
13:22:56 -0.427,-0.529,-0.781,-11.620,7.980,-6.370
13:22:56 -0.438,-0.565,-0.780,-8.260,9.100,-6.090
13:22:56 -0.403,-0.592,-0.787,-9.030,9.940,-6.930
13:22:56 -0.373,-0.625,-0.788,-1.680,11.760,-8.120
13:22:56 -0.327,-0.578,-0.779,0.840,13.510,-8.540
13:22:56 -0.347,-0.516,-0.791,-4.690,17.500,-7.280
13:22:56 -0.376,-0.459,-0.759,-15.190,21.070,-3.710
13:22:56 -0.394,-0.515,-0.715,-14.770,21.980,-2.450
13:22:56 -0.374,-0.574,-0.705,-16.940,21.980,-2.240
13:22:56 -0.368,-0.648,-0.708,-15.190,22.190,-4.480
13:22:56 -0.354,-0.650,-0.676,-8.890,20.650,-6.440
13:22:56 -0.355,-0.634,-0.655,-1.470,17.920,-7.910
13:22:56 -0.340,-0.597,-0.669,0.350,15.960,-8.400
13:22:56 -0.349,-0.550,-0.726,-2.450,16.170,-7.350
13:22:56 -0.377,-0.532,-0.772,-7.490,17.290,-5.670
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