Human Activity Recognition

1. Business Problem

1.1 Description

This project is to build a model that predicts the human activities such as Walking, Walking_Upstairs, Walking_Downstairs, Sitting, Standing or Laying. This dataset is collected from 30 persons(referred as subjects in this dataset), performing different activities with a smartphone to their waists. The data is recorded with the help of sensors (accelerometer and Gyroscope) in that smartphone. This experiment was video recorded to label the data manually.

1.2 Sources/Useful Links

• https://arxiv.org/pdf/1805.07020.pdf
• https://www.researchgate.net/publication/305649713_Deep_learning_for_human_activity_recognition_A_resource_efficient_implementation_on_low-power_devices

2. Machine Learning Probelm

2.1 Data

2.1.1 Data Overview

• Accelerometer and Gyroscope readings are taken from 30 volunteers(referred as subjects) while performing the following 6 Activities.

  1. Walking
  2. WalkingUpstairs
  3. WalkingDownstairs
  4. Standing
  5. Sitting
  6. Lying.

• Readings are divided into a window of 2.56 seconds with 50% overlapping.

• Accelerometer readings are divided into gravity acceleration and body acceleration readings, which has x,y and z components each.

• Gyroscope readings are the measure of angular velocities which has x,y and z components.

• Jerk signals are calculated for BodyAcceleration readings.

• Fourier Transforms are made on the above time readings to obtain frequency readings.

• Now, on all the base signal readings., mean, max, mad, sma, arcoefficient, engerybands,entropy etc., are calculated for each window.

• We get a feature vector of 561 features and these features are given in the dataset.

• Each window of readings is a datapoint of 561 features.

2.2 Mapping the real world problem to an ML problem

2.2.1 Type of Machine Leaning Problem

• It is a multi class classification problem, given a new datapoint we have to predict the Activity.

3. Getting Started

Start by downloading the project and run "HAR_DNN_Fusion_Model.ipynb" file in ipython-notebook.

4. Prerequisites

You need to have installed following softwares and libraries before running this project.

1. Python 3: https://www.python.org/downloads/
2. Anaconda: It will install ipython notebook and most of the libraries which are needed like sklearn, pandas, seaborn, matplotlib, numpy and scipy: https://www.anaconda.com/download/

5. Libraries

• Gensim: Gensim is a Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community.

  • pip install gensim
  • conda install -c conda-forge gensim

• Keras: Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano.

  • pip install Keras
  • conda install -c conda-forge kera

• xgboost: It is used to make xgboost regression model.

  • pip install xgboost
  • conda install -c conda-forge xgboost

• scikit-learn: scikit-learn is a Python module for machine learning built on top of SciPy.

  • pip install scikit-learn
  • conda install -c conda-forge scikit-learn

• nltk: The Natural Language Toolkit (NLTK) is a Python package for natural language processing.

  • pip install nltk
  • conda install -c conda-forge nltk

6. Authors

• Manish Vishwakarma - Complete work