/HumanActivityRecognition

Classification system to precisely identify human fitness activities.

Primary LanguageJupyter Notebook

Human Activity Recognition

Built a classification system to precisely identify human fitness activities.

Built for the - kaggle challenge

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.

Features

  • Deployment of Keras model
  • Comparison of different classification algorithms-KNN, RandomForest, SVM, Logistic Regression
  • GridSearch optimisation

Dependencies:

Python 3.5+ with Anaconda
Tensorflow, keras, sklearn, pandas ,matplotlib