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.

• 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

• 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.

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

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

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/

• 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

• Manish Vishwakarma - Complete work