Human_Activity_Recognition_using_chest_mounted_accelerometer.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/AnirbanMukherjeeXD/human-activity-recognition/blob/master/Human_Activity_Recognition_using_chest_mounted_accelerometer.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RJDKVbU_fT7A"
   },
   "source": [
    "_Reference: https://machinelearningmastery.com/how-to-load-and-explore-a-standard-human-activity-recognition-problem/_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QRiS8HBNbvIa"
   },
   "source": [
    "## Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JeKyvJ3bWL42"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-nuflfeybr5O"
   },
   "source": [
    "## Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "aPs9peTkb4TN"
   },
   "source": [
    "### Read Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SV7kBJtec-KN"
   },
   "outputs": [],
   "source": [
    "dataset=[]\n",
    "for i in range(1,16):\n",
    "  dataset.append(pd.read_csv('https://raw.githubusercontent.com/AnirbanMukherjeeXD/human-activity-recognition/master/datasets/Single-Chest-Mounted-Accelerometer/'+str(i)+'.csv',header=None,names=[4,0,1,2,3]).drop([4],axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 195
    },
    "colab_type": "code",
    "id": "Hs2xrEMYWlTW",
    "outputId": "94c37084-2776-4b48-9598-8ac869a9ace7"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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      "text/plain": [
       "      0     1     2  3\n",
       "0  1502  2215  2153  1\n",
       "1  1667  2072  2047  1\n",
       "2  1611  1957  1906  1\n",
       "3  1601  1939  1831  1\n",
       "4  1643  1965  1879  1"
      ]
     },
     "execution_count": 38,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[0].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KZydAYwbbWT9"
   },
   "source": [
    "### Visualizing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "UqB7cih4cTYb",
    "outputId": "90a38261-76f4-4758-9dfb-38120cfb245b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(162501,)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[0][1].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "m4-sGCdKfm3o"
   },
   "source": [
    "**Function to view subject data:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XLXKALUBb7re"
   },
   "outputs": [],
   "source": [
    "def view_subject(sub_id):\n",
    "  plt.figure(figsize=(10,8))\n",
    "  for i in range(4):\n",
    "    plt.subplot(4,1,i+1)\n",
    "    plt.plot(dataset[sub_id][i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 483
    },
    "colab_type": "code",
    "id": "3CvurdjHe-JI",
    "outputId": "089be477-32bb-4095-d2fd-dbf4f0aa8bc6"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "view_subject(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jWX0zatvfuzB"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "include_colab_link": true,
   "name": "Human-Activity-Recognition-using-chest-mounted-accelerometer.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}