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Classify Fitness Band Activities using Kera Deep Learning and Adaboost in Python/R

In this project, we are going to build a classification system where we can recognize 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.


  • R-Studio
  • Python 3
  • Language used: Python or R

Project Description

The Human Activity Recognition database 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.