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Human Activity Recognition Using Smartphones Data Set

In this deep learning project, you will build a classification system where to precisely identify human fitness activities.

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What will you learn

  • Application of classification algorithms
  • Compare multiple algorithms
  • Deployment of Keras Deep learning algorithm
  • Deployment of SVM and Adaboost
  • Comparison of models

What will you get

  • Access to recording of the complete project
  • Access to all material related to project like data files, solution files etc.

Prerequisites

  • Language used: Python

Project Description

The Human Activity Recognition dataset was built from the recordings of 30 study participants performing activities of daily living (ADL) while carrying a waist-mounted smartphone with embedded inertial sensors. The objective is to classify activities into one of the six activities performed.Each person performed six activities (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING) wearing a smartphone (Samsung Galaxy S II) on the waist. 

Using its embedded accelerometer and gyroscope, we captured 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50Hz. The experiments have been video-recorded to label the data manually. The obtained dataset has been randomly partitioned into two sets, where 70% of the volunteers were selected for generating the training data and 30% the test data.

Instructors

 
Pradeepta

Curriculum For This Mini Project

 
  Problem Statement Overview
00:03:52
  Data Set Overview
00:02:07
  Shuffle the Data
00:01:34
  Separate Input/Ouput Labels
00:00:34
  Encoding Labels
00:01:20
  Classification Models
00:02:10
  Neural Network Model
00:09:14
  Graphical Representation of Data
00:06:50
  Logistic Regression
00:11:00
  Scalar VS PCA
00:02:07
  Transformation Function - TSNE
00:02:01
  Activation Functions
00:02:20
  Keras Neural Network
00:01:48
  Random Forest Classifier
00:01:13
  SGD Model
00:04:27
  K-Nearest Neighbour
00:00:55
  Support Vector Classification
00:10:34
  TensorFlow Model
00:08:27
  K-Neighbour Classifier
00:05:04
  Confusion Matrix
00:06:50
  Multiple Classifiers
00:01:35
  Grid Search
00:05:07
  Conclusion
00:01:55