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Human Activity Recognition - Using Deep Learning Model

Last Updated : 27 Dec, 2021
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Human activity recognition using smartphone sensors like accelerometer is one of the hectic topics of research. HAR is one of the time series classification problem. In this project various machine learning and deep learning models have been worked out to get the best final result. In the same sequence, we can use LSTM (long short term memory) model of the Recurrent Neural Network (RNN) to recognize various activities of humans like standing, climbing upstairs and downstairs etc. 
LSTM model is a type of recurrent neural network capable of learning order dependence in sequence prediction problems. This model is used as this helps in remembering values over arbitrary intervals.
Human Activity Recognition dataset can be downloaded from the link given below: HAR dataset 
Activities: 
 

  • Walking
  • Upstairs
  • Downstairs
  • Sitting
  • Standing


Accelerometers detect magnitude and direction of the proper acceleration, as a vector quantity, and can be used to sense orientation (because direction of weight changes). GyroScope maintains orientation along a axis so that the orientation is unaffected by tilting or rotation of the mounting, according to the conservation of angular momentum. 
Understanding the dataset: 
 

  • Both the sensors generate data in 3D space over time. 
    ('XYZ' represents 3-axial signals in X, Y, and Z directions.)
  • The available data is pre-processed by applying noise filters and then sampled in fixed-width windows ie., each window has 128 readings.


Train and Test data were separated as 
The readings from 80% of the volunteers were taken as training data and remaining 20% volunteers records were taken for test data. All the data is present in the folder downloaded using the link provided above.
Phases 
 

  • Choosing a dataset 
     
  • Uploading the dataset in the drive to work on google colaboratory
  • Dataset cleaning and data Preprocessing
  • Choosing a model and building deep learned network model
  • Exporting in Android Studio.


The IDE used for this project is Google Colaboratory which is the best of the times to deal with deep learning projects. Phase 1 was explained above as from where the dataset is downloaded. In this sequence to start with the project open a new notebook in Google Colaboratory first import all the necessary libraries.
Code: Importing Libraries 
 

Python3 
import pandas as pd
import numpy as np
import pickle
import matplotlib.pyplot as plt
from scipy import stats
import tensorflow as tf
import seaborn as sns
from sklearn import metrics
from sklearn.model_selection import train_test_split

%matplotlib inline

Phase 2: 
It is uploading dataset in the notebook, before doing that we need to mount the notebook on drive so that this notebook is saved on our drive and retrieved whenever required. 
 

Python3 
sns.set(style="whitegrid", palette="muted", font_scale=1.5)
RANDOM_SEED = 42

from google.colab import drive
drive.mount('/content/drive')

Output: 
 

You will see a pop up similar to one shown in the screenshot below, open the link and copy the authorization code and paste it in the authorization code bar and enter the drive will be mounted.


 


Code: Uploading the dataset 
 

Python3 
from google.colab import files
uploaded = files.upload()

Now moving on to model building and training phase, we need to look for different models which can help in building better accuracy model. Here, LSTM model of Recurrent Neural Network is chosen. The image given below shows how the data looks. 
 


Phase 3: 
It begins with the data pre-processing. It is the phase where ~90% of time is consumed in actual data science projects. Here, raw data is taken and converted in some useful and efficient formats. 
Code: Data transformation is performed to normalize the data 
 

Python3 
#transforming shape
reshaped_segments = np.asarray(
    segments, dtype = np.float32).reshape(
    -1 , N_time_steps, N_features)

reshaped_segments.shape

Code: Splitting the dataset 
 

Python3 
X_train, X_test, Y_train, Y_test = train_test_split(
    reshaped_segments, labels, test_size = 0.2, 
    random_state = RANDOM_SEED)

The test size is taken as 20% i.e. out of the total records 20% of records are used for test accuracy while rest is used for training model.
Number of classes = 6 (Walking, Sitting, Standing, Running, Upstairs and Downstairs)
Phase 4: In this phase model chosen is the LSTM model of RNN.
Code: Model building 
 

Python3 
def create_LSTM_model(inputs):
    W = {
        'hidden': tf.Variable(tf.random_normal([N_features, N_hidden_units])),
        'output': tf.Variable(tf.random_normal([N_hidden_units, N_classes]))
    }
    biases = {
        'hidden': tf.Variable(tf.random_normal([N_hidden_units], mean = 0.1)),
        'output': tf.Variable(tf.random_normal([N_classes]))
    }
    X = tf.transpose(inputs, [1, 0, 2])
    X = tf.reshape(X, [-1, N_features])
    hidden = tf.nn.relu(tf.matmul(X, W['hidden']) + biases['hidden'])
    hidden = tf.split(hidden, N_time_steps, 0)

    lstm_layers = [tf.contrib.rnn.BasicLSTMCell(
        N_hidden_units, forget_bias = 1.0) for _ in range(2)]
    lstm_layers = tf.contrib.rnn.MultiRNNCell(lstm_layers)

    outputs, _ = tf.contrib.rnn.static_rnn(lstm_layers, 
                                           hidden, dtype = tf.float32)

    lstm_last_output = outputs[-1]
    return tf.matmul(lstm_last_output, W['output']) + biases['output']

Code: Performing optimization using AdamOptimizer to modifier loss values wrt the variables to improve accuracy and reduce loss. 
 

Python3 
L2_LOSS = 0.0015
l2 = L2_LOSS * \
  sum(tf.nn.l2_loss(tf_var) for tf_var in tf.trainable_variables())
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(
    logits = pred_y, labels = Y)) + l2

Learning_rate = 0.0025
optimizer = tf.train.AdamOptimizer(learning_rate = Learning_rate).minimize(loss)
correct_pred = tf.equal(tf.argmax(pred_softmax , 1), tf.argmax(Y,1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, dtype = tf.float32))

Code: Performing 50 iterations of model training to get the highest accuracy and reduced loss 
 

Python3 
# epochs is number of iterations performed in model training.
N_epochs = 50  
batch_size = 1024

saver = tf.train.Saver()
history = dict(train_loss=[], train_acc=[], test_loss=[], test_acc=[])
sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())
train_count = len(X_train)

for i in range(1, N_epochs + 1):
    for start, end in zip(range(0, train_count, batch_size), 
                          range(batch_size, train_count + 1, batch_size)):
        sess.run(optimizer, feed_dict={X: X_train[start:end],
                                       Y: Y_train[start:end]})
    _, acc_train, loss_train = sess.run([pred_softmax, accuracy, loss], feed_dict={
        X: X_train, Y: Y_train})
    _, acc_test, loss_test = sess.run([pred_softmax, accuracy, loss], feed_dict={
        X: X_test, Y: Y_test})
    history['train_loss'].append(loss_train)
    history['train_acc'].append(acc_train)
    history['test_loss'].append(loss_test)
    history['test_acc'].append(acc_test)

    if (i != 1 and i % 10 != 0):
        print(f'epoch: {i} test_accuracy:{acc_test} loss:{loss_test}')
predictions, acc_final, loss_final = sess.run([pred_softmax, accuracy, loss], 
                                              feed_dict={X: X_test, Y: Y_test})
print()
print(f'final results : accuracy : {acc_final} loss : {loss_final}')

Output: 
 


So, with this approach the accuracy reaches nearly ~1 at 50th iteration. This indicates that most of the labels are identified clearly by this approach. To get the exact count of correctly identified activities confusion matrix is created. 
Code: Accuracy graph 
 

Python3 
plt.figure(figsize=(12,8))

plt.plot(np.array(history['train_loss']), "r--", label="Train loss")
plt.plot(np.array(history['train_acc']), "g--", label="Train accuracy")

plt.plot(np.array(history['test_loss']), "r--", label="Test loss")
plt.plot(np.array(history['test_acc']), "g--", label="Test accuracy")

plt.title("Training session's progress over iteration")
plt.legend(loc = 'upper right', shadow = True)
plt.ylabel('Training Progress(Loss or Accuracy values)')
plt.xlabel('Training Epoch')
plt.ylim(0)

plt.show()

Confusion Matrix: A confusion matrix is no less then a 2D matrix unlike it helps calculating exact count of activities correctly identified. In other words, it describes the performance of the classification model on the set of test dataset. 
Code: Confusion matrix 
 

Python3 
max_test = np.argmax(Y_test, axis=1)
max_predictions = np.argmax(predictions, axis = 1)
confusion_matrix = metrics.confusion_matrix(max_test, max_predictions)

plt.figure(figsize=(16,14))
sns.heatmap(confusion_matrix, xticklabels = LABELS, yticklabels = LABELS, annot =True, fmt = "d")
plt.title("Confusion Matrix")
plt.xlabel('Predicted_label')
plt.ylabel('True Label')
plt.show()

This is the complete description about the project so far. It can be built using other models like CNN or machine learning models like KNN.
 


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      Convolutional Neural Networks (CNNs) are a type of deep learning model specifically designed for processing images. Unlike traditional neural networks CNNs uses convolutional layers to automatically and efficiently extract features such as edges, textures and patterns from images. This makes them hi
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    • Traffic Signs Recognition using CNN and Keras in Python
      We always come across incidents of accidents where drivers' Overspeed or lack of vision leads to major accidents. In winter, the risk of road accidents has a 40-50% increase because of the traffic signs' lack of visibility. So here in this article, we will be implementing Traffic Sign recognition us
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    • Lung Cancer Detection using Convolutional Neural Network (CNN)
      Computer Vision is one of the applications of deep neural networks and one such use case is in predicting the presence of cancerous cells. In this article, we will learn how to build a classifier using Convolution Neural Network which can classify normal lung tissues from cancerous tissues.The follo
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    • Lung Cancer Detection Using Transfer Learning
      Computer Vision is one of the applications of deep neural networks that enables us to automate tasks that earlier required years of expertise and one such use in predicting the presence of cancerous cells.In this article, we will learn how to build a classifier using the Transfer Learning technique
      8 min read
    • Pneumonia Detection using Deep Learning
      In this article, we will discuss solving a medical problem i.e. Pneumonia which is a dangerous disease that may occur in one or both lungs usually caused by viruses, fungi or bacteria. We will detect this lung disease based on the x-rays we have. Chest X-rays dataset is taken from Kaggle which conta
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    • Detecting Covid-19 with Chest X-ray
      COVID-19 pandemic is one of the biggest challenges for the healthcare system right now. It is a respiratory disease that affects our lungs and can cause lasting damage to the lungs that led to symptoms such as difficulty in breathing and in some cases pneumonia and respiratory failure. In this artic
      9 min read
    • Skin Cancer Detection using TensorFlow
      In this article, we will learn how to implement a Skin Cancer Detection model using Tensorflow. We will use a dataset that contains images for the two categories that are malignant or benign. We will use the transfer learning technique to achieve better results in less amount of training. We will us
      5 min read
    • Age Detection using Deep Learning in OpenCV
      The task of age prediction might sound simple at first but it's quite challenging in real-world applications. While predicting age is typically seen as a regression problem this approach faces many uncertainties like camera quality, brightness, climate condition, background, etc. In this article we'
      5 min read
    • Face and Hand Landmarks Detection using Python - Mediapipe, OpenCV
      In this article, we will use mediapipe python library to detect face and hand landmarks. We will be using a Holistic model from mediapipe solutions to detect all the face and hand landmarks. We will be also seeing how we can access different landmarks of the face and hands which can be used for diff
      4 min read
    • Detecting COVID-19 From Chest X-Ray Images using CNN
      A Django Based Web Application built for the purpose of detecting the presence of COVID-19 from Chest X-Ray images with multiple machine learning models trained on pre-built architectures. Three different machine learning models were used to build this project namely Xception, ResNet50, and VGG16. T
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    • Image Segmentation Using TensorFlow
      Image segmentation refers to the task of annotating a single class to different groups of pixels. While the input is an image, the output is a mask that draws the region of the shape in that image. Image segmentation has wide applications in domains such as medical image analysis, self-driving cars,
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    • License Plate Recognition with OpenCV and Tesseract OCR
      License Plate Recognition is widely used for automated identification of vehicle registration plates for security purpose and law enforcement. By combining computer vision techniques with Optical Character Recognition (OCR) we can extract license plate numbers from images enabling applications in ar
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    • Detect and Recognize Car License Plate from a video in real time
      Recognizing a Car License Plate is a very important task for a camera surveillance-based security system. We can extract the license plate from an image using some computer vision techniques and then we can use Optical Character Recognition to recognize the license number. Here I will guide you thro
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    • Residual Networks (ResNet) - Deep Learning
      After the first CNN-based architecture (AlexNet) that win the ImageNet 2012 competition, Every subsequent winning architecture uses more layers in a deep neural network to reduce the error rate. This works for less number of layers, but when we increase the number of layers, there is a common proble
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    Natural Language Processing Projects

    • Twitter Sentiment Analysis using Python
      This article covers the sentiment analysis of any topic by parsing the tweets fetched from Twitter using Python. What is sentiment analysis? Sentiment Analysis is the process of 'computationally' determining whether a piece of writing is positive, negative or neutral. It’s also known as opinion mini
      10 min read
    • Facebook Sentiment Analysis using python
      This article is a Facebook sentiment analysis using Vader, nowadays many government institutions and companies need to know their customers' feedback and comment on social media such as Facebook. What is sentiment analysis? Sentiment analysis is one of the best modern branches of machine learning, w
      6 min read
    • Next Sentence Prediction using BERT
      Next Sentence Prediction is a pre-training task used in BERT to help the model understand the relationship between different sentences. It is widely used for tasks like question answering, summarization and dialogue systems. The goal is to determine whether a given second sentence logically follows
      4 min read
    • Hate Speech Detection using Deep Learning
      There must be times when you have come across some social media post whose main aim is to spread hate and controversies or use abusive language on social media platforms. As the post consists of textual information to filter out such Hate Speeches NLP comes in handy. This is one of the main applicat
      5 min read
    • Image Caption Generator using Deep Learning on Flickr8K dataset
      Generating a caption for a given image is a challenging problem in the deep learning domain. In this article we will use different computer vision and NLP techniques to recognize the context of an image and describe them in a natural language like English. We will build a working model of the image
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    • Movie recommendation based on emotion in Python
      Movies that effectively portray and explore emotions resonate deeply with audiences because they tap into our own emotional experiences and vulnerabilities. A well-crafted emotional movie can evoke empathy, understanding, and self-reflection, allowing viewers to connect with the characters and their
      4 min read
    • Speech Recognition in Python using Google Speech API
      Speech recognition means converting spoken words into text. It used in various artificial intelligence applications such as home automation, speech to text, etc. In this article, you’ll learn how to do basic speech recognition in Python using the Google Speech Recognition API.Step 1: Install Require
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    • Voice Assistant using python
      Speech recognition is the process of turning spoken words into text. It is a key part of any voice assistant. In Python the SpeechRecognition module helps us do this by capturing audio and converting it to text. In this guide we’ll create a basic voice assistant using Python.Step 1: Install Required
      3 min read
    • Human Activity Recognition - Using Deep Learning Model
      Human activity recognition using smartphone sensors like accelerometer is one of the hectic topics of research. HAR is one of the time series classification problem. In this project various machine learning and deep learning models have been worked out to get the best final result. In the same seque
      6 min read
    • Fine-tuning BERT model for Sentiment Analysis
      Google created a transformer-based machine learning approach for natural language processing pre-training called Bidirectional Encoder Representations from Transformers. It has a huge number of parameters, hence training it on a small dataset would lead to overfitting. This is why we use a pre-train
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    • Sentiment Classification Using BERT
      BERT stands for Bidirectional Representation for Transformers and was proposed by researchers at Google AI language in 2018. Although the main aim of that was to improve the understanding of the meaning of queries related to Google Search, BERT becomes one of the most important and complete architec
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    • Sentiment Analysis with an Recurrent Neural Networks (RNN)
      Recurrent Neural Networks (RNNs) are used in sequence tasks such as sentiment analysis due to their ability to capture context from sequential data. In this article we will be apply RNNs to analyze the sentiment of customer reviews from Swiggy food delivery platform. The goal is to classify reviews
      5 min read
    • Building an Autocorrector Using NLP in Python
      Autocorrect feature predicts and correct misspelled words, it helps to save time invested in the editing of articles, emails and reports. This feature is added many websites and social media platforms to ensure easy typing. In this tutorial we will build a Python-based autocorrection feature using N
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    • Python | NLP analysis of Restaurant reviews
      Natural language processing (NLP) is an area of computer science and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data. It is the branch of mach
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    • Restaurant Review Analysis Using NLP and SQLite
      Normally, a lot of businesses are remained as failures due to lack of profit, lack of proper improvement measures. Mostly, restaurant owners face a lot of difficulties to improve their productivity. This project really helps those who want to increase their productivity, which in turn increases thei
      9 min read
    • Twitter Sentiment Analysis using Python
      This article covers the sentiment analysis of any topic by parsing the tweets fetched from Twitter using Python. What is sentiment analysis? Sentiment Analysis is the process of 'computationally' determining whether a piece of writing is positive, negative or neutral. It’s also known as opinion mini
      10 min read

    Clustering Projects

    • Customer Segmentation using Unsupervised Machine Learning in Python
      Customer Segmentation involves grouping customers based on shared characteristics, behaviors and preferences. By segmenting customers, businesses can tailor their strategies and target specific groups more effectively and enhance overall market value. Today we will use Unsupervised Machine Learning
      5 min read
    • Music Recommendation System Using Machine Learning
      When did we see a video on youtube let's say it was funny then the next time you open your youtube app you get recommendations of some funny videos in your feed ever thought about how? This is nothing but an application of Machine Learning using which recommender systems are built to provide persona
      4 min read
    • K means Clustering – Introduction
      K-Means Clustering is an Unsupervised Machine Learning algorithm which groups unlabeled dataset into different clusters. It is used to organize data into groups based on their similarity. Understanding K-means ClusteringFor example online store uses K-Means to group customers based on purchase frequ
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    • Image Segmentation using K Means Clustering
      Image segmentation is a technique in computer vision that divides an image into different segments. This can help identify specific objects, boundaries or patterns in the image.  Image is basically a set of given pixels and in image segmentation pixels with similar intensity are grouped together. Im
      2 min read

    Recommender System Project

    • AI Driven Snake Game using Deep Q Learning
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