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ML Model and Arduino Programming for Machine Learning Typing Gloves

I am an Information Technology degree holder. I recently earned three certificates in Tiny Machine Learning.

This Tutorial Will Teach You Programming for Machine Learning Typing Gloves

  • The code that is required to get analog values from an Arduino and average them.
  • The code for the ML model to get your artificial neural network trained on the data from the first tutorial.
  • All so that your machine learning typing gloves will be ready to type on any hard surface.

Sliding Into Arduino Code

  1. The gloves themselves should be labeled for each hand to make them intuitive to use. I labeled mine as red and green.
  2. Please keep in mind that the below code will send the analog values from the flex-sensors attached to the fingers through the Arduino's Bluetooth Silver Mate to the computer.
  3. The receiving Python script built in the first tutorial will process the data coming into the computer.
  4. Then store it into a comma-separated-value list. I believe there are six bytes total for each analog value.
  5. The (A) signifying an analog character takes up 1 byte of memory sense chars are one byte. Then the sensor number indicator takes up four bytes, as it is an integer.
  6. Then, we have lowByte to get the least significant byte from the analog value. As long as the analog values are between 1-255, this should be ok.
  7. When working on the gloves, I also noticed that the analog values were spurious, so I used an averaging filter to smooth them into a more reliable value.

Math.h is needed to smooth the data in AverageFilter.

#include <math.h>

In the setup part of the Arduino program, we set the baud rate at 1,200

void setup() {

Average takes 16 analog values and averages them together sixteen times in total. After this, we divide by the number of analog values taken. Then, the function returns a fraction. By doing these things, it gives a more accurate representation of the final analog value.

// Smooth the input to eliminate random values from machine learning model
int AverageFilter(int aNum)
  float AverageAnalogValue = 0;
  int MeasurementsToAverage = 16;
  for(int i = 0; i < MeasurementsToAverage; ++i)
    AverageAnalogValue += analogRead(aNum);
  AverageAnalogValue /= MeasurementsToAverage;
  return TakeFraction(.10,AverageAnalogValue);
int TakeFraction(float FractionAmount,int AveragedAnalogValue)
  return round(AveragedAnalogValue * FractionAmount);

The first printed character (A) gives some separation between the values, and the second printed number 5-9 after the initial printed value. It allows us to identify which sensor it is.

The third printed value is the actual analog value of the flex-sensor.

void loop() {
   // 6 bytes per sensor
   Serial.print(lowByte(AverageFilter(0))); // Read the local analog signal
   Serial.print(lowByte(AverageFilter(1))); // Read the local analog signal
   Serial.print(lowByte(AverageFilter(2))); // Read the local analog signal
   Serial.print(lowByte(AverageFilter(3))); // Read the local analog signal
Scroll to Continue
A0{integer value}A1{integer value}A2{integer value}A3{integer value}A4{integer value}A5{integer value}A6{integer value}A7{integer value}A8{integer value}A9{integer value}

A Little Python

  1. First, we import some required libraries into the project. Some of these were explained in the first tutorial so that we won't go over them.
  2. Mainly I want to focus on the libraries TensorFlow, NumPy, and Keras. These will be used in the machine learning model.
  3. Since we want to predict what key is pressed, based on the data from the gloves, some parts of the script will be similar to the we made in the first tutorial. We have a dependent variable and an independent variable.
  4. The X and y of the program play the role of the changing data and the results of that change, respectively.
  5. The recorded keyboard key, in this case, will be the y or target value. The X will be all of the analog values that we record from our script when the key is pressed.
  6. We cannot simply take the values as they are. They must undergo some normalization.
  7. They may skew the final result for significant and minor values if not brought within a specific range. And as a computer cannot understand the alphabet letters, we must convert them using OneHotEncoder.
# Tensorflow v1.5 was used in this project
import pandas as pd
import tensorflow as tf
import numpy as np
import keras
import serial
import threading
import re
from keras.models import model_from_json
import numpy
from numpy import array
from numpy import reshape
import queue

que = queue.Queue()
# How to connect gloves to fingers
# Start at pinky 1-5 green
# Start at thumb 1-5 red
ser0 = serial.Serial("COM9", 1200,bytesize=serial.EIGHTBITS,timeout=None, parity=serial.PARITY_NONE, rtscts=1)
#Changed stopbits to 1
ser1 = serial.Serial("COM10", 1200,bytesize=serial.EIGHTBITS,timeout=None, parity=serial.PARITY_NONE,rtscts=1)
dataset = pd.read_csv('directory')
X = dataset.iloc[:, 1:11].values
y = dataset.iloc[:, 0].values

# Taking care of missing data
from sklearn.preprocessing import Imputer
imputer = Imputer(missing_values = 'NaN', strategy = 'mean', axis = 0)
imputer =[:, 1:11])
X[:, 1:11] = imputer.transform(X[:, 1:11])

# dataset starts at 1 due to columns from openoffice formatting, I suppose
# Encoding categorical data
# Encoding the Dependent Variable
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_y = LabelEncoder()
y = labelencoder_y.fit_transform(y)
# May need to only go this far in the encoding
onehotencoder = OneHotEncoder(sparse=False)
y = y.reshape(len(y), 1)
y = onehotencoder.fit_transform(y)

# Splitting the dataset into the Training set and Test set

from sklearn.model_selection import train_test_split
# Consider using another random_state seed when training the next time
# Changed random_state to 42
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)

# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.transform(X_test)
# Importin keras dependencies
import keras
from keras.models import Sequential
from keras.layers import Dense
from keras.models import model_from_json
# Initializing the ANN
classifier = Sequential()

These are the layers of the neural network. We have only one input layer and one hidden layer. The number of neurons for this ANN was arbitrary and can be improved.

However, the output layer does need 28 neurons for the encoded letters of the alphabet.

# Add 1st input hidden layer
classifier.add(Dense(17,kernel_initializer='uniform',activation='relu',input_dim = 10))

# Add second hidden layer

# Add the output layer

Categorical_crossentropy was used because this is a classification problem.

classifier.compile(loss="categorical_crossentropy", optimizer="adam", metrics=['accuracy'])

# load json and create model
#json_file = open('model.json', 'r')
#loaded_model_json =
#loaded_model = model_from_json(loaded_model_json)
# load weights into new model


print("Loaded model from disk")
# load json and create model
#json_file = open('directory', 'r')
#loaded_model_json =
#classifier = model_from_json(loaded_model_json)

#print("Loaded model from disk"),y_train,batch_size=10,nb_epoch=1000)

graph = tf.get_default_graph()

# serialize model to JSON
#model_json = classifier.to_json()
#with open("directory", "w") as json_file:
   # json_file.write(model_json)
# serialize weights to HDF5
#print("Saved model to disk")

Almost all of the following code is the same as the Python script made in the previous tutorial. But note the use of a graph at the bottom. This was used to save the state of the neural network.

# Class to do the important procedures on the serial data
class serprocedure():
	# The following variables are class-level and are static, which means they will persist across class instances
	except NameError:
		sensorlist = []
	# A list to store the missing data
	except NameError:
		missingdata = []
	# A counter for missing data
	except NameError:
		mcounter = 0
	except NameError:
		times_counter = 0

		# Use the __init__ constructor to take argument self and serdata
		# Each method should only do 1 thing
		def __init__(self,serdata,flag):
			self.serdata = serdata
			self.flag = flag
			# If it is the second thread with the second serial com, and missing counter less than 1, and sensorlist not greater than 10
			if self.flag == 1 and serprocedure.mcounter < 1 and len(serprocedure.sensorlist) == 10:
				# Tell the user that the program is performing further extraction
				print("Performing further extraction of data and exportation")
				# Perform further extraction
				serprocedure.times_counter = serprocedure.times_counter + 1
				# It will only do this, if it's not the first serial COM with flag 0
			elif self.flag != 0:
				# Reset counter
				serprocedure.mcounter = 0
				# Clear the list so it doesn't build up
				print("Throwing away partial or excess result.")
		# Method to extract the individual parts of the serial data
		def extractanalog(self,analognumber,flag):
			# Changed the decimal regexp to {1,2} quantifier
			found ='(A' + str(analognumber) + '\d{1,2})',str(self.serdata))
			if found is not None:
					# Create a list of data
					# sensorlist must be moved to the top to be a class-level variable
					#sensorlist = []
				serprocedure.mcounter += 1
				# It's getting stuck here
		def furtherextraction(self,newlist):
			# A list to hold analog labels
			findanaloglabel = []
			# A list to hold analog values
			findanalogvalue = []
			z = 0
			#print("This is the list in furtherextraction:")
			# Len counts 10 elements in the list but the index starts at 0
			while z < len(newlist):
				# These will have to be made into lists
				# ?<= looks behind and only matches whats ahead
				# Changed the decimal regexp to {1,2} quantifier
				# Increment z
				z = z + 1
			# print the list findanalogvalue
				# Call the export method
			# Return the analog values to main read_analog

		def serdatalooper(self,flag):
			if flag == 0:
				i = 0
				end = i + 4
				i = 5
				end = i + 4
			# Loop through the entire length of the list and extract the data
			while i <= end:
				# Increment the counter
				i = i + 1
				# Sort the list
			#if len(serprocedure.missingdata) < 1:
				#q.put("There were no missing data")
				#return True
				#q.put("There are " + str(len(serprocedure.missingdata)) + " of data missing from list")
				#return False

# read from serial port
def read_from_serial(serial,board,flag):
    #print("reading from {}: port {}".format(board, port))
    payload = b''
    bytes_count = 0
	# Changed the 1 in to bytes_at_a_time
    #bytes_at_a_time = serial.in_waiting
	# Changed to  <= to make it count the last byte
    #while bytes_count <= BYTES_TO_READ:
        #read_bytes =

	# sum number of bytes returned (not 2), you have set the timeout on serial port
	# see
    # bytes_count = bytes_count + len(read_bytes)
	# This seems to be an improvement. Try to catch missing serial data.
    read_bytes = serial.read_until('\n',40)
    payload = payload + read_bytes

            # here you have the bytes, do your logic
            # Instantiate object from serprocedure class
    serprocobj = serprocedure(payload,flag)

def main():
	while True:
				# Pass in the function, serial, board, and key as agrguments
				# We'll pass in a flag to identify which board is being used
				t = threading.Thread(target=read_from_serial, args=(ser0,"HC-06(Red)",0))
				t1 = threading.Thread(target=read_from_serial, args=(ser1,"RN(Green)",1))
				# Start the threads
				# Be careful this is blocking. Gets the missing data amount
				# wait for all threads termination
				# The joins may be holding up the buffer flushes, if they are move them to the bottom
				# Flush the serial input and output buffers
				global graph
				with graph.as_default():
					analog_to_predict = que.get()
					if analog_to_predict != False:
						# The input had to be scaled in order for it to work somewhat better
						analog_to_predict = sc_X.transform(numpy.array([analog_to_predict]))
						new_pred = classifier.predict_classes(analog_to_predict)
						new_pred = labelencoder_y.inverse_transform(new_pred)
						print("que.get() was False")


Would You Like to Know More About Making Machine Learning Typing Gloves?

If you're interested in this process, it's a great idea to check out my first in-depth tutorial on this subject, which contains further information:

How to Make Better Machine Learning Typing Gloves

This content is accurate and true to the best of the author’s knowledge and is not meant to substitute for formal and individualized advice from a qualified professional.

© 2021 Brock Lynch

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