def collect_samples():
num_samples = 500 # 10 seconds of data @ 50Hz
times = CircularList([], num_samples)
ax = CircularList([], num_samples)
ay = CircularList([], num_samples)
az = CircularList([], num_samples)
comms = Communication("/dev/cu.ag-ESP32SPP", 115200)
try:
comms.clear() # just in case any junk is in the pipes
# wait for user to start walking before starting to collect data
input("Ready to collect data? Press [ENTER] to begin.\n")
sleep(3)
comms.send_message("wearable") # begin sending data
sample = 0
while (sample < num_samples):
message = comms.receive_message()
if (message != None):
try:
(m1, m2, m3, m4) = message.split(',')
except ValueError: # if corrupted data, skip the sample
continue
# add the new values to the circular lists
times.add(int(m1))
ax.add(int(m2))
ay.add(int(m3))
az.add(int(m4))
sample += 1
print("Collected {:d} samples".format(sample))
# a single ndarray for all samples for easy file I/O
data = np.column_stack([times, ax, ay, az])
except (Exception, KeyboardInterrupt) as e:
print(e) # exiting the program due to exception
finally:
comms.send_message("sleep") # stop sending data
comms.close()
return data
def collect_samples():
num_samples = 500 # 10 seconds of data @ 50Hz
times = CircularList([], num_samples)
ppg = CircularList([], num_samples)
comms = Communication("/dev/cu.usbserial-1420", 115200)
try:
comms.clear() # just in case any junk is in the pipes
# wait for user and then count down
input("Ready to collect data? Press [ENTER] to begin.\n")
print("Start measuring in...")
for k in range(3,0,-1):
print(k)
sleep(1)
print("Begin!")
comms.send_message("wearable") # begin sending data
sample = 0
while(sample < num_samples):
message = comms.receive_message()
if(message != None):
try:
(m1, _, _, _, m2) = message.split(',')
except ValueError: # if corrupted data, skip the sample
continue
# add the new values to the circular lists
times.add(int(m1))
ppg.add(int(m2))
sample += 1
print("Collected {:d} samples".format(sample))
# a single ndarray for all samples for easy file I/O
data = np.column_stack([times, ppg])
except(Exception, KeyboardInterrupt) as e:
print(e) # exiting the program due to exception
finally:
comms.send_message("sleep") # stop sending data
comms.close()
return data
current_time = time()
if (current_time - previous_time > process_time):
previous_time = current_time
try:
heart_rate, peaks, filtered = hr.process()
except:
continue
print("Heart Rate: " + str(heart_rate))
plt.cla()
plt.plot(t_plot, filtered)
plt.plot(t_plot[peaks], filtered[peaks], 'rx')
plt.plot(t_plot, [0.6]*len(filtered), 'b--')
# If the heart is too large, then it should be displayed as an inaccurate reading
if(heart_rate > MAX_HEART_RATE):
plt.title("Inaccurate Reading")
comms.send_message("Inaccurate Reading")
# Otherwise display the Heart Rate on the Plot and send to the LED display
else:
plt.title("Heart Rate: %d" % heart_rate)
comms.send_message("HR: " + str(int(heart_rate)) + " bpm")
plt.show(block=False)
plt.pause(0.001)
except(Exception, KeyboardInterrupt) as e:
print(e) # Exiting the program due to exception
finally:
print("Closing connection.")
comms.send_message("sleep") # stop sending data
comms.close()
class Processing():
__num_samples = 250 # 2 seconds of data @ 50Hz
__refresh_time = 0.5 # update the plot every 0.1s (10 FPS)
# Thresholds for idle detection
__ax_idle_threshold = 1935
__ay_idle_threshold = 1918
__az_idle_threshold = 2430
"""
Initializes the processing object and sets the field variables
@:param transformation_method: the type of transformation that will be plotted and computed
@:param port_name: The Serial port name used for Serial communication
"""
def __init__(self, transformation_method, port_name):
self.comms = Communication(port_name, 115200)
__transform_dict = {
"average acceleration": self.__average_value,
"sample difference": self.__sample_difference,
"L2 norm": self.__l2_norm_calculation,
"L1 norm": self.__l1_norm_calculation,
"Maximum acceleration:": self.__max_acceleration
}
# This will keep track of the transformation method to be called
self.__transformation_method = __transform_dict[transformation_method]
self.transform_x = CircularList([], self.__num_samples)
self.transform_y = CircularList([], self.__num_samples)
self.transform_z = CircularList([], self.__num_samples)
# These will contain the acceleration values read from the accelerometer
self.times = CircularList([], self.__num_samples)
self.ax = CircularList([], self.__num_samples)
self.ay = CircularList([], self.__num_samples)
self.az = CircularList([], self.__num_samples)
# Set up the plotting
fig = plt.figure()
self.ax1 = fig.add_subplot(311)
self.ax2 = fig.add_subplot(312)
self.ax3 = fig.add_subplot(313)
self.graph_type = transformation_method
# times to keep track of when
self.__last_idlecheck_time = 0
self.__idle_state = False
self.__last_active_time = 0
"""
Updates the plot, displaying the acceeleration values as well as the
transformation values
"""
def __plot(self):
# Clears the plots and resets them
self.ax1.cla()
self.ax2.cla()
self.ax3.cla()
# Sets the title of the plot
self.ax1.set_title("X acceleration (Red) and " + self.graph_type +
" (Blue)")
self.ax2.set_title("Y acceleration (Red) and " + self.graph_type +
" (Blue)")
self.ax3.set_title("X acceleration (Red) and " + self.graph_type +
" (Blue)")
# Plots the acceleration values along with the transformation
self.ax1.plot(self.transform_x, 'b', self.ax, 'r')
self.ax2.plot(self.transform_y, 'b', self.ay, 'r')
self.ax3.plot(self.transform_z, 'b', self.az, 'r')
plt.show(block=False)
plt.pause(0.0001)
"""
Determines whether the device is inactive or not
@:param average_x: The average x-acceleration
@:param average_y: The average y-acceleration
@:param average_z: The average z-acceleration
@:return: a boolean representing whether the device is inactive or not
"""
def __is_inactive(self, average_x, average_y, average_z):
return average_x <= self.__ax_idle_threshold and average_y <= self.__ay_idle_threshold and average_z <= self.__az_idle_threshold
"""
Computes the average value for an axis
@:param acceleration_list: The acceleration over 5 seconds in either x,y,z direction
@:return: A scalar which is the average from the acceleration list
"""
def __average_value(self, acceleration_list):
return np.average(np.array(acceleration_list))
"""
Computes the difference between each adjacent indexes
@:param acceleration_list: The acceleration over 5 seconds in either x,y,z direction
@:return: a numpy array containing the differences between each point
"""
def __sample_difference(self, acceleration_list):
np.diff(np.array(acceleration_list))
return np.diff(np.array(acceleration_list))
"""
Computes the euclidean distance for the acceleration list
@:param x_acceleration: one sample of the x-acceleration
@:param y_acceleration: one sample of the y-acceleration
@:param z_acceleration: one sample of the z-acceleration
@:return: A scalar which is the square root of the sum of each number in the
"""
def __l2_norm_calculation(self, x_acceleration, y_acceleration,
z_acceleration):
return np.linalg.norm(
np.array([x_acceleration, y_acceleration, z_acceleration]))
"""
Computes the L1 norm for the acceleration lsit
@:param x_acceleration: one sample of the x-acceleration
@:param y_acceleration: one sample of the y-acceleration
@:param z_acceleration: one sample of the z-acceleration
@:return: The sum of the absolute value of each acceleration value
"""
def __l1_norm_calculation(self, x_acceleration, y_acceleration,
z_acceleration):
return np.linalg.norm(np.array(
[x_acceleration, y_acceleration, z_acceleration]),
ord=1)
"""
Finds the max acceleration in one of the accelerations
@:param: acceleration_list: The acceleration over 5 seconds in either x,y,z direction
@:return: The maximum value in the acceleration list
"""
def __max_acceleration(self, acceleration_list):
return int(np.max(np.array(acceleration_list)))
"""
Records the acceleration and times from the accelerometer
@:param time: the time from the Serial monitor
@:param x_acceleration: the x-acceleration
@:param y_acceleration: the y-acceleration
@:param z_acceleration: the z-acceleration
"""
def __record_acceleration(self, time, x_acceleration, y_acceleration,
z_acceleration):
# add the new values to the circular lists
self.times.add(int(time))
self.ax.add(int(x_acceleration))
self.ay.add(int(y_acceleration))
self.az.add(int(z_acceleration))
"""
Records the transformation value based on what transformation type the
instance uses
@:param x_acceleration: one sample of the x-acceleration
@:param y_acceleration: one sample of the y-acceleration
@:param z_acceleration: one sample of the z-acceleration
"""
def __record_transformation(self, x_acceleration, y_acceleration,
z_acceleration):
# These if statements are used because some of these methods have different parameters and return types
if self.__transformation_method == self.__l1_norm_calculation or self.__transformation_method == self.__l2_norm_calculation:
norm_number = self.__transformation_method()
self.transform_x.add(norm_number)
self.transform_y.add(norm_number)
self.transform_z.add(norm_number)
# sets each transformation method to the sample difference array
elif self.__transformation_method == self.__sample_difference:
self.transform_x = self.__transformation_method(self.ax)
self.transform_y = self.__transformation_method(self.ay)
self.transform_z = self.__transformation_method(self.az)
# This will either call average_value or maximum_acceleration
else:
self.transform_x.add(self.__transformation_method(self.ax))
self.transform_y.add(self.__transformation_method(self.ay))
self.transform_z.add(self.__transformation_method(self.az))
"""
Checks if the device has been idle for 5 seconds or if it's been active for 1
second. This will either cause the motor to buzz or another message displaying that the person has
been active.
@:param current_time: The current time the program is at
"""
def __check_idle(self, current_time):
# If it's been 5 seconds since the last time the person has been inactive
if current_time - self.__last_idlecheck_time >= 5:
# get the average acceleration over 5 seconds
average_x = self.__average_value(self.ax)
average_y = self.__average_value(self.ay)
average_z = self.__average_value(self.az)
print(average_x, ",", average_y, ",", average_z)
self.__last_idlecheck_time = current_time
# if the device has been idle for 5 seconds, buzz the motor
if self.__is_inactive(average_x, average_y, average_z):
self.__idle_state = True
self.comms.send_message("Buzz motor")
else:
self.__idle_state = False
# If the person has been inactive but has become active for 1 second
if self.__idle_state and current_time - self.__last_active_time >= 1:
self.__last_active_time = current_time
# get the average values for the last 1 second
average_x = self.__average_value(self.ax[200:])
average_y = self.__average_value(self.ay[200:])
average_z = self.__average_value(self.az[200:])
if not self.__is_inactive(average_x, average_y, average_z):
print("Active accelerations: ", average_x, average_y,
average_z)
self.__last_idlecheck_time = current_time # this ensures that the person must be inactive for 5 seconds after their activity
self.comms.send_message("Keep it up!")
"""
Runs all the processing for the Serial communication, including plotting the
acceleration values and checking whether the device has been inactive or not.
"""
def run(self):
self.comms.clear() # just in case any junk is in the pipes
self.comms.send_message("wearable") # begin sending data
try:
previous_time = 0
while (True):
message = self.comms.receive_message()
if (message != None):
try:
(m1, m2, m3, m4) = message.split(',')
except ValueError: # if corrupted data, skip the sample
continue
# Record the acceleration and transformation
self.__record_acceleration(m1, m2, m3, m4)
self.__record_transformation(m2, m3, m4)
current_time = time()
if current_time - previous_time > self.__refresh_time:
previous_time = current_time
self.__plot()
self.__check_idle(current_time)
except (Exception, KeyboardInterrupt) as e:
print(e) # Exiting the program due to exception
finally:
print("Closing Connection")
self.comms.send_message("sleep") # stop sending data
self.comms.close()
sleep(1)