HAR-01_data-preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Human Activity Recognition\n",
    "Credit to:\n",
    "* https://github.com/guillaume-chevalier/LSTM-Human-Activity-Recognition\n",
    "* https://github.com/cwi-dis/mobile-har-tutorial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, LeaveOneGroupOut\n",
    "# http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.LeaveOneGroupOut.html#sklearn.model_selection.LeaveOneGroupOut\n",
    "\n",
    "from scipy import stats\n",
    "import scipy.io\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as colors\n",
    "import matplotlib.cm\n",
    "import seaborn as sns\n",
    "import pickle\n",
    "import math\n",
    "import os\n",
    "# import wget\n",
    "import zipfile\n",
    "import warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2024-02-10 16:15:54--  http://abdoelali.com/data/mobilehci2018_tutorial_data.zip\n",
      "Resolving abdoelali.com (abdoelali.com)... 104.248.131.45\n",
      "Connecting to abdoelali.com (abdoelali.com)|104.248.131.45|:80... connected.\n",
      "HTTP request sent, awaiting response... 301 Moved Permanently\n",
      "Location: https://abdoelali.com/data/mobilehci2018_tutorial_data.zip [following]\n",
      "--2024-02-10 16:15:54--  https://abdoelali.com/data/mobilehci2018_tutorial_data.zip\n",
      "Connecting to abdoelali.com (abdoelali.com)|104.248.131.45|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 273858391 (261M) [application/zip]\n",
      "Saving to: ‘./data/mobilehci2018_tutorial_data.zip’\n",
      "\n",
      "mobilehci2018_tutor 100%[===================>] 261.17M   200MB/s    in 1.3s    \n",
      "\n",
      "2024-02-10 16:15:55 (200 MB/s) - ‘./data/mobilehci2018_tutorial_data.zip’ saved [273858391/273858391]\n",
      "\n",
      "--2024-02-10 16:15:55--  http://abdoelali.com/data/accel_only.zip\n",
      "Resolving abdoelali.com (abdoelali.com)... 104.248.131.45\n",
      "Connecting to abdoelali.com (abdoelali.com)|104.248.131.45|:80... connected.\n",
      "HTTP request sent, awaiting response... 301 Moved Permanently\n",
      "Location: https://abdoelali.com/data/accel_only.zip [following]\n",
      "--2024-02-10 16:15:55--  https://abdoelali.com/data/accel_only.zip\n",
      "Connecting to abdoelali.com (abdoelali.com)|104.248.131.45|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 86931679 (83M) [application/zip]\n",
      "Saving to: ‘./accel/accel_only.zip’\n",
      "\n",
      "accel_only.zip      100%[===================>]  82.90M   192MB/s    in 0.4s    \n",
      "\n",
      "2024-02-10 16:15:56 (192 MB/s) - ‘./accel/accel_only.zip’ saved [86931679/86931679]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "## wget UCD dataset + all .npy files and dump into ./data dir\n",
    "\n",
    "if not os.path.exists('./data'):\n",
    "    os.makedirs('./data')\n",
    "\n",
    "if not os.path.exists('./accel'):\n",
    "    os.makedirs('./accel')\n",
    "\n",
    "!wget -P ./data 'http://abdoelali.com/data/mobilehci2018_tutorial_data.zip'\n",
    "!wget -P ./accel 'http://abdoelali.com/data/accel_only.zip'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "## unzip and delete files\n",
    "with zipfile.ZipFile('./data/mobilehci2018_tutorial_data.zip','r') as zipref:\n",
    "    zipref.extractall('./data/')\n",
    "os.remove('./data/mobilehci2018_tutorial_data.zip')\n",
    "\n",
    "with zipfile.ZipFile('./accel/accel_only.zip','r') as zipref:\n",
    "    zipref.extractall('./accel/')\n",
    "os.remove('./accel/accel_only.zip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "## let's first define some function we will use\n",
    "\n",
    "## setting up a random seed for reproducibility\n",
    "random_seed = 611\n",
    "np.random.seed(random_seed)\n",
    "\n",
    "## matplotlib inline\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "## defining function for loading our dataset\n",
    "def readData(filePath):\n",
    "    # attributes of the dataset\n",
    "    columnNames = ['user_id','activity','timestamp','x-axis','y-axis','z-axis']\n",
    "    data = pd.read_csv(filePath,header = None, names=columnNames,na_values=';')\n",
    "    return data[0:2000]\n",
    "\n",
    "## feature normalization using standardization (or Z-score normalization)\n",
    "## (feature - mean)/stdiv\n",
    "def featureNormalizeZscore(data):\n",
    "    mu = np.mean(data,axis=0)\n",
    "    sigma = np.std(data,axis=0)\n",
    "    return (data-mu)/sigma\n",
    "\n",
    "# find the min and max values for each column\n",
    "def dataset_minmax(data):\n",
    "    minmax = list()\n",
    "    for i in range(len(data[0])):\n",
    "        col_values = [row[i] for row in data]\n",
    "        value_min = min(col_values)\n",
    "        value_max = max(col_values)\n",
    "        minmax.append([value_min, value_max])\n",
    "    return minmax\n",
    " \n",
    "# rescale dataset columns to the range 0-1\n",
    "def normalize_dataset(data, minmax):\n",
    "    for row in data:\n",
    "        for i in range(len(row)):\n",
    "            row[i] = (row[i] - minmax[i][0]) / (minmax[i][1] - minmax[i][0])\n",
    "            \n",
    "## compute Euclidean Norm\n",
    "def featureNormalizeEuclidean(data):\n",
    "    return np.sqrt(sum(data^2))\n",
    "\n",
    "## defining the function to plot a single axis data\n",
    "def plotAxis(axis,x,y,title):\n",
    "    axis.plot(x,y)\n",
    "    axis.set_title(title)\n",
    "    axis.xaxis.set_visible(False)\n",
    "    axis.set_ylim([min(y)-np.std(y),max(y)+np.std(y)])\n",
    "    axis.set_xlim([min(x),max(x)])\n",
    "    axis.grid(True)\n",
    "\n",
    "## defining a function to plot the data for a given activity\n",
    "def plotActivity(activity,data):\n",
    "    fig,(ax0,ax1,ax2) = plt.subplots(nrows=3, figsize=(15,10),sharex=True)\n",
    "    plotAxis(ax0,data['timestamp'],data['x-axis'],'x-axis')\n",
    "    plotAxis(ax1,data['timestamp'],data['y-axis'],'y-axis')\n",
    "    plotAxis(ax2,data['timestamp'],data['z-axis'],'z-axis')\n",
    "    plt.subplots_adjust(hspace=0.2)\n",
    "    fig.suptitle(activity)\n",
    "    plt.subplots_adjust(top=0.9)\n",
    "    plt.show()\n",
    "\n",
    "## defining a window function for segmentation purposes\n",
    "def windows(data,size):\n",
    "    start = 0\n",
    "    while start< data.count():\n",
    "        yield int(start), int(start + size)\n",
    "        start+= (size/2)    \n",
    "\n",
    "## our segmentation function to get streams of 90 samples in each timestep \n",
    "def segment_signal(data, window_size = 90):\n",
    "    segments = np.empty((0,window_size,6))\n",
    "    labels= np.empty((0))\n",
    "    subjects = np.empty((0))\n",
    "\n",
    "    for (start, end) in windows(data['activity'],window_size):\n",
    "        x = data['acc_x'][start:end]\n",
    "        y = data['acc_y'][start:end]\n",
    "        z = data['acc_z'][start:end]\n",
    "        p = data['gyr_x'][start:end]\n",
    "        q = data['gyr_y'][start:end]\n",
    "        r = data['gyr_z'][start:end]\n",
    "\n",
    "        if(len(data['activity'][start:end])==window_size):\n",
    "            segments = np.vstack([segments,np.dstack([x,y,z,p,q,r])])\n",
    "            if labels is not None:\n",
    "                labels = np.append(labels,stats.mode(data['activity'][start:end])[0][0])\n",
    "            subjects = np.append(subjects,stats.mode(data['subject'][start:end])[0][0])\n",
    "    return segments, labels, subjects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "## read in the USC-HAD data. here we use activity number since activities have missing/incorrect labels\n",
    "DIR = './data/USC-HAD/data/'\n",
    "\n",
    "# activity = []\n",
    "subject = []\n",
    "# age = []\n",
    "act_num = []\n",
    "sensor_readings = []\n",
    "\n",
    "def read_dir(directory):\n",
    "    for path, subdirs, files in os.walk(DIR):\n",
    "        for name in files:\n",
    "            if name.endswith('.mat'):\n",
    "                mat = scipy.io.loadmat(os.path.join(path, name))\n",
    "#                 activity.append(mat['activity'])\n",
    "                subject.extend(mat['subject'])\n",
    "#                 age.extend(mat['age'])\n",
    "                sensor_readings.append(mat['sensor_readings'])\n",
    "\n",
    "                if mat.get('activity_number') is None:\n",
    "                    act_num.append('11')\n",
    "                else:\n",
    "                    act_num.append(mat['activity_number'])\n",
    "    return subject, act_num, sensor_readings\n",
    "\n",
    "## Corrupt datapoint:\n",
    "# act_num[258] = '11'            \n",
    "subject, act_num, sensor_readings = read_dir(DIR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df size 2811490\n"
     ]
    }
   ],
   "source": [
    "## get acc + gyr sensor readings and put in df (dataframe)\n",
    "acc_x = []\n",
    "acc_y = []\n",
    "acc_z = []\n",
    "gyr_x = []\n",
    "gyr_y = []\n",
    "gyr_z = []\n",
    "\n",
    "act_label = []\n",
    "subject_id = []\n",
    "df = None\n",
    "\n",
    "for i in range(840):\n",
    "    for j in sensor_readings[i]:   \n",
    "        acc_x.append(j[0]) # acc_x\n",
    "        acc_y.append(j[1]) # acc_y\n",
    "        acc_z.append(j[2]) # acc_z\n",
    "        gyr_x.append(j[3]) # gyr_x\n",
    "        gyr_y.append(j[4]) # gyr_y\n",
    "        gyr_z.append(j[5]) # gyr_z\n",
    "        act_label.append(act_num[i])\n",
    "        subject_id.append(subject[i])\n",
    "        \n",
    "df = pd.DataFrame({'subject':subject_id,'acc_x':acc_x,'acc_y':acc_y,'acc_z':acc_z,'gyr_x':gyr_x,'gyr_y':gyr_y,'gyr_z':gyr_z,'activity':act_label})                   \n",
    "\n",
    "df = df[['subject','acc_x', 'acc_y', 'acc_z', 'gyr_x', 'gyr_y', 'gyr_z','activity']]\n",
    "\n",
    "df.loc[df['activity'] == '1', 'activity'] = 'Walking Forward'\n",
    "df.loc[df['activity'] == '2', 'activity'] = 'Walking Left'\n",
    "df.loc[df['activity'] == '3', 'activity'] = 'Walking Right'\n",
    "df.loc[df['activity'] == '4', 'activity'] = 'Walking Upstairs'\n",
    "df.loc[df['activity'] == '5', 'activity'] = 'Walking Downstairs'\n",
    "df.loc[df['activity'] == '6', 'activity'] = 'Running Forward'\n",
    "df.loc[df['activity'] == '7', 'activity'] = 'Jumping Up'\n",
    "df.loc[df['activity'] == '8', 'activity'] = 'Sitting'\n",
    "df.loc[df['activity'] == '9', 'activity'] = 'Standing'\n",
    "df.loc[df['activity'] == '10', 'activity'] = 'Sleeping'\n",
    "df.loc[df['activity'] == '11', 'activity'] = 'Elevator Up'\n",
    "df.loc[df['activity'] == '12', 'activity'] = 'Elevator Down'\n",
    "\n",
    "## pickle the df \n",
    "df.to_pickle('data/df.pkl')\n",
    "\n",
    "## these are the 12 classes we want to recognize!\n",
    "df['activity'].unique() \n",
    "\n",
    "## print size of dataset\n",
    "print('df size ' + str(len(df)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "## use only for collapsing / reducing output classes\n",
    "\n",
    "# df2 = df\n",
    "\n",
    "# df2.loc[df2['activity'] == 'Walking Forward', 'activity'] = 'Walking'\n",
    "# df2.loc[df2['activity'] == 'Walking Left', 'activity'] = 'Walking'\n",
    "# df2.loc[df2['activity'] == 'Walking Right', 'activity'] = 'Walking'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  subject     acc_x     acc_y     acc_z     gyr_x      gyr_y     gyr_z  \\\n",
      "1       7  0.888076  0.425966 -0.211597 -8.287599 -11.061767  0.123535   \n",
      "2       7  0.891510  0.425966 -0.211597 -8.287599 -11.061767  0.123535   \n",
      "3       7  0.894944  0.429586 -0.211597 -0.789322  -1.004702 -0.025368   \n",
      "4       7  0.891510  0.429586 -0.207937 -0.621181  -0.523508  0.196681   \n",
      "5       7  0.894944  0.429586 -0.207937 -0.417196  -0.351597  0.132094   \n",
      "6       7  0.891510  0.425966 -0.207937 -0.311717  -0.262703  0.098697   \n",
      "7       7  0.894944  0.425966 -0.207937  0.072152  -0.208027  0.078155   \n",
      "8       7  0.898378  0.425966 -0.207937  0.059232  -0.170776  0.064160   \n",
      "9       7  0.894944  0.425966 -0.207937 -0.288966  -0.482402  0.053958   \n",
      "\n",
      "        activity  \n",
      "1  Elevator Down  \n",
      "2  Elevator Down  \n",
      "3  Elevator Down  \n",
      "4  Elevator Down  \n",
      "5  Elevator Down  \n",
      "6  Elevator Down  \n",
      "7  Elevator Down  \n",
      "8  Elevator Down  \n",
      "9  Elevator Down  \n"
     ]
    }
   ],
   "source": [
    "## inspect the dataframe\n",
    "print(df[1:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore your dataset (through visualization)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1300x500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>acc_x</th>\n",
       "      <th>acc_y</th>\n",
       "      <th>acc_z</th>\n",
       "      <th>gyr_x</th>\n",
       "      <th>gyr_y</th>\n",
       "      <th>gyr_z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.811490e+06</td>\n",
       "      <td>2.811490e+06</td>\n",
       "      <td>2.811490e+06</td>\n",
       "      <td>2.811490e+06</td>\n",
       "      <td>2.811490e+06</td>\n",
       "      <td>2.811490e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.827995e-01</td>\n",
       "      <td>2.013366e-01</td>\n",
       "      <td>-6.190999e-02</td>\n",
       "      <td>-4.138871e-01</td>\n",
       "      <td>-3.517485e-01</td>\n",
       "      <td>-1.747959e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.449592e-01</td>\n",
       "      <td>3.889872e-01</td>\n",
       "      <td>3.356888e-01</td>\n",
       "      <td>5.237366e+01</td>\n",
       "      <td>2.757491e+01</td>\n",
       "      <td>4.773775e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-3.352662e+00</td>\n",
       "      <td>-6.417827e+00</td>\n",
       "      <td>-5.416337e+00</td>\n",
       "      <td>-7.708486e+02</td>\n",
       "      <td>-7.754454e+02</td>\n",
       "      <td>-8.081837e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.168063e-01</td>\n",
       "      <td>-8.330392e-03</td>\n",
       "      <td>-2.445384e-01</td>\n",
       "      <td>-1.074605e+01</td>\n",
       "      <td>-6.752421e+00</td>\n",
       "      <td>-6.280653e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.812087e-01</td>\n",
       "      <td>2.305329e-01</td>\n",
       "      <td>-8.715175e-02</td>\n",
       "      <td>-5.208981e-02</td>\n",
       "      <td>-1.471133e-01</td>\n",
       "      <td>7.424915e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.807888e-01</td>\n",
       "      <td>4.332047e-01</td>\n",
       "      <td>1.104966e-01</td>\n",
       "      <td>9.589783e+00</td>\n",
       "      <td>4.131121e+00</td>\n",
       "      <td>7.904181e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.931558e+00</td>\n",
       "      <td>4.949892e+00</td>\n",
       "      <td>4.539283e+00</td>\n",
       "      <td>8.563609e+02</td>\n",
       "      <td>5.596140e+02</td>\n",
       "      <td>8.165777e+02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              acc_x         acc_y         acc_z         gyr_x         gyr_y  \\\n",
       "count  2.811490e+06  2.811490e+06  2.811490e+06  2.811490e+06  2.811490e+06   \n",
       "mean   7.827995e-01  2.013366e-01 -6.190999e-02 -4.138871e-01 -3.517485e-01   \n",
       "std    6.449592e-01  3.889872e-01  3.356888e-01  5.237366e+01  2.757491e+01   \n",
       "min   -3.352662e+00 -6.417827e+00 -5.416337e+00 -7.708486e+02 -7.754454e+02   \n",
       "25%    6.168063e-01 -8.330392e-03 -2.445384e-01 -1.074605e+01 -6.752421e+00   \n",
       "50%    8.812087e-01  2.305329e-01 -8.715175e-02 -5.208981e-02 -1.471133e-01   \n",
       "75%    9.807888e-01  4.332047e-01  1.104966e-01  9.589783e+00  4.131121e+00   \n",
       "max    6.931558e+00  4.949892e+00  4.539283e+00  8.563609e+02  5.596140e+02   \n",
       "\n",
       "              gyr_z  \n",
       "count  2.811490e+06  \n",
       "mean  -1.747959e-01  \n",
       "std    4.773775e+01  \n",
       "min   -8.081837e+02  \n",
       "25%   -6.280653e+00  \n",
       "50%    7.424915e-03  \n",
       "75%    7.904181e+00  \n",
       "max    8.165777e+02  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## explore your overall accel and gyro values: min, max, mean, and plot over time\n",
    "\n",
    "''' \n",
    "## USC-HAD Sensors\n",
    "\n",
    "From the UbiComp'12 paper:\n",
    "\"Based on the above considerations, we use an off-the-shelf sensing platform called MotionNode to capture human activity signals \n",
    "and build our dataset. MotionNode is a 6-DOF inertial measurement unit (IMU) specifically designed for human motion sensing \n",
    "applications (see Figure 2) [2]. Each MotionNode itself is a multi-modal sensor that integrates a 3-axis accelerometer, \n",
    "3-axis gyroscope, and a 3-axis mag- netometer. The measurement range is ±6g and ±500dps for each axis of accelerometer and gyroscope \n",
    "respectively. Although body limbs and extremities can exhibit up to ±12g in acceleration, points near the torso \n",
    "and hip experience no more than ±6g range in acceleration [6].\"\n",
    "\n",
    "• MotionNode is extremely small in size (35mm×35mm× 15mm) and lightweight enough (14g) to wear comfort- ably for long period of time. \n",
    "This feature makes MotionN- ode unobtrusive and thus perfect as a wearable device.\n",
    "• Compared to the accelerometer and gyroscope embedded in the smartphones (e.g. iPhone 4G), the integrated sen- sors have higher \n",
    "resolution (0.001g ± 10% for accelerom- eter, 0.5◦/second for gyroscope) and wider sensing ranges. In addition, MotionNode \n",
    "is gyro-stablized and well cali- brated such that the readings are accurate and reliable.\n",
    "• The highest sampling rate can reach 100Hz. This sampling frequency is much higher than the one used in some of the existing \n",
    "datasets [23] [21].\n",
    "\n",
    "**********************************************\n",
    "Section 1: Device Configuration\n",
    "**********************************************\n",
    "\n",
    "1. Device Type: MotionNode\n",
    "2. Sampling rate: 100Hz (or 10,000 microseconds)\n",
    "3. Accelerometer range: +-6g\n",
    "4. Gyroscope range: +-500dps\n",
    "\n",
    "For sensor_readings field, it consists of 6 readings:\n",
    "From left to right:\n",
    "1. acc_x, w/ unit g (gravity)\n",
    "2. acc_y, w/ unit g\n",
    "3. acc_z, w/ unit g\n",
    "4. gyro_x, w/ unit dps (degrees per second)\n",
    "5. gyro_y, w/ unit dps\n",
    "6. gyro_z, w/ unit dps\n",
    "\n",
    "BMI MotionNode sensor spec sheet: https://www.motionnode.com/technology.html \n",
    "\n",
    "============\n",
    "## Android Nexus 5 sensors\n",
    "\n",
    "For Android Nexus 5 sensor specs, check: https://www.bosch-sensortec.com/bst/products/all_products/bmi160\n",
    "\n",
    "'''\n",
    "\n",
    "## explore your overall accel and gyro values: min, max, mean, and plot over time\n",
    "\n",
    "## accelerometer \n",
    "\n",
    "cols = [\"acc_x\", \"acc_y\", \"acc_z\", \"gyr_x\", \"gyr_y\", \"gyr_z\"]\n",
    "\n",
    "fig, ax = plt.subplots(2, 3, figsize=(13,5), sharex=True)\n",
    "ax = ax.flatten()\n",
    "\n",
    "for i, e in enumerate(cols):\n",
    "    \n",
    "    ax[i].plot(df[e])\n",
    "    \n",
    "plt.show()\n",
    "\n",
    "df[cols].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "## set up a plots dir\n",
    "\n",
    "plot_dir = './plots/activity_vis/'\n",
    "\n",
    "os.makedirs(plot_dir, exist_ok=True)\n",
    "    \n",
    "## two functions below to plot your data, and save them to disk\n",
    "def plot_activity(activity, df,i=0, j=100):\n",
    "    data = df[df['activity'] == activity][['acc_x', 'acc_y', 'acc_z', 'gyr_x', 'gyr_y', 'gyr_z']][i:j]\n",
    "    axis = data.plot(subplots=True, figsize=(16, 12), \n",
    "                     title=activity)\n",
    "    for ax in axis:\n",
    "        ax.legend(loc='lower left', bbox_to_anchor=(1.0, 0.5))\n",
    "        \n",
    "    if not os.path.isdir(plot_dir):\n",
    "        os.makedirs(plot_dir)\n",
    "\n",
    "    plt.savefig(plot_dir + str(activity) + '.pdf',bbox_inches='tight')\n",
    "        \n",
    "def plot_datasets(df,i=0,j=1000):\n",
    "    plot_activity('Walking Forward', df,i,j)\n",
    "    plot_activity('Walking Left', df,i,j)\n",
    "    plot_activity('Walking Right', df,i,j)\n",
    "    plot_activity('Walking Upstairs', df,i,j)\n",
    "    plot_activity('Walking Downstairs', df,i,j)\n",
    "    plot_activity('Running Forward', df,i,j)\n",
    "    plot_activity('Jumping Up', df,i,j)\n",
    "    plot_activity('Sitting', df,i,j)\n",
    "    plot_activity('Standing', df,i,j)\n",
    "    plot_activity('Sleeping', df,i,j)\n",
    "    plot_activity('Elevator Up', df,i,j)\n",
    "    plot_activity('Elevator Down', df,i,j)\n",
    "\n",
    "plot_datasets(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## check class districtions, and save fig to disk\n",
    "df['activity'].value_counts().plot(kind='bar', color=['r','g','b','k','y','m','c'], title='Plotting records by activity type', figsize=(10, 4),align='center');\n",
    "plt.savefig(plot_dir + 'sample_dist.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "activity\n",
      "Walking Forward       381400\n",
      "Sleeping              375000\n",
      "Walking Right         269700\n",
      "Walking Left          264600\n",
      "Sitting               261500\n",
      "Standing              236000\n",
      "Walking Upstairs      211800\n",
      "Walking Downstairs    197400\n",
      "Running Forward       176500\n",
      "Elevator Up           165491\n",
      "Elevator Down         164999\n",
      "Jumping Up            107100\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "## print the value counts for each activity\n",
    "print(df['activity'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "## if you want to just read your saved df\n",
    "# df = pd.read_pickle('data/df.pkl')\n",
    "\n",
    "## UNCOMMENT below line for segmenting the signal in overlapping windows of 90 samples with 50% overlap\n",
    "# segments, labels, subjects = segment_signal(df)\n",
    "\n",
    "## COMMENT below segments + labels files if you want to segment afresh . open a file, where you stored the pickled data\n",
    "segments = pickle.load(open('./data/segments_90_logo.p', 'rb'), encoding='latin1')\n",
    "labels = pickle.load(open('./data/labels_90_logo.p','rb'), encoding='latin1')\n",
    "subjects = pickle.load(open('./data/subjects_90_logo.p','rb'),encoding='latin1')\n",
    "\n",
    "## dump information to that file (UNCOMMENT to save fresh segmentation!)\n",
    "# pickle.dump(segments, open( './data/segments_90_logo.p','wb'))\n",
    "# pickle.dump(labels, open( './data/labels_90_logo.p','wb'))\n",
    "# pickle.dump(subjects, open( './data/subjects_90_logo.p','wb'))\n",
    "\n",
    "# segments, labels, subjects = segment_signal(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check mismatch between sensor readings of dataset and Android sensors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "## USC-HAD dataset inspect\n",
    "\n",
    "len_sample = len(df)\n",
    "\n",
    "# print(df[df['activity'] == 'Sitting']['acc_x'])\n",
    "usc_sit_df_acc_x = df[df['activity'] == 'Sitting']['acc_x'][0:len_sample]\n",
    "usc_sit_df_acc_y = df[df['activity'] == 'Sitting']['acc_y'][0:len_sample]\n",
    "usc_sit_df_acc_z = df[df['activity'] == 'Sitting']['acc_z'][0:len_sample]\n",
    "usc_sit_df_gyr_x = df[df['activity'] == 'Sitting']['gyr_x'][0:len_sample]\n",
    "usc_sit_df_gyr_y = df[df['activity'] == 'Sitting']['gyr_y'][0:len_sample]\n",
    "usc_sit_df_gyr_z = df[df['activity'] == 'Sitting']['gyr_z'][0:len_sample]\n",
    "\n",
    "\n",
    "print('sitting mean usc-had acc_x: '+ str(np.mean(usc_sit_df_acc_x)))\n",
    "print('sitting std usc-had acc_x: '+ str(np.std(usc_sit_df_acc_x)))\n",
    "\n",
    "print('sitting mean usc-had acc_y: '+ str(np.mean(usc_sit_df_acc_y)))\n",
    "print('sitting std usc-had acc_y: '+ str(np.std(usc_sit_df_acc_y)))\n",
    "\n",
    "print('sitting mean usc-had acc_z: '+ str(np.mean(usc_sit_df_acc_z)))\n",
    "print('sitting std usc-had acc_z: '+ str(np.std(usc_sit_df_acc_z)))\n",
    "\n",
    "print('sitting mean usc-had gyr_x: '+ str(np.mean(usc_sit_df_acc_z)))\n",
    "print('sitting std usc-had gyr_x: '+ str(np.std(usc_sit_df_acc_z)))\n",
    "\n",
    "print('sitting mean usc-had gyr_y: '+ str(np.mean(usc_sit_df_acc_z)))\n",
    "print('sitting std usc-had gyr_y: '+ str(np.std(usc_sit_df_gyr_y)))\n",
    "\n",
    "print('sitting mean usc-had gyr_z: '+ str(np.mean(usc_sit_df_gyr_z)))\n",
    "print('sitting std usc-had gyr_z: '+ str(np.std(usc_sit_df_gyr_z)))\n",
    "\n",
    "\n",
    "# print 'usc-had dataset acc_x sample for sitting activity: '+ str(np.mean(sit_df_acc_x))\n",
    "plt.plot(usc_sit_df_acc_x)\n",
    "plt.show()\n",
    "plt.plot(usc_sit_df_acc_y)\n",
    "plt.show()\n",
    "plt.plot(usc_sit_df_acc_z)\n",
    "plt.show()\n",
    "plt.plot(usc_sit_df_gyr_x)\n",
    "plt.show()\n",
    "plt.plot(usc_sit_df_gyr_y)\n",
    "plt.show()\n",
    "plt.plot(usc_sit_df_gyr_z)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "## Android sensor reading samples inspect\n",
    "\n",
    "acc_sit_android = pd.read_csv('./data/android_sitting_acc.csv')[100:400]\n",
    "gyr_sit_android = pd.read_csv('./data/android_sitting_gyr.csv')[100:400]\n",
    "\n",
    "sit_acc_x = acc_sit_android['X']\n",
    "sit_acc_y = acc_sit_android['Y']\n",
    "sit_acc_z = acc_sit_android['Z']\n",
    "sit_gyr_x = gyr_sit_android['X']\n",
    "sit_gyr_y = gyr_sit_android['Y']\n",
    "sit_gyr_z = gyr_sit_android['Z']\n",
    "\n",
    "print('sitting android acc_x size: '+ str(len(sit_acc_x)))\n",
    "\n",
    "print('sitting mean android acc_x: '+ str(np.mean(sit_acc_x)))\n",
    "print('sitting std android acc_x: '+ str(np.std(sit_acc_x)))\n",
    "\n",
    "print('sitting mean android acc_y: '+ str(np.mean(sit_acc_y)))\n",
    "print('sitting std android acc_y: '+ str(np.std(sit_acc_y)))\n",
    "\n",
    "print('sitting mean android acc_z: '+ str(np.mean(sit_acc_z)))\n",
    "print('sitting std android acc_z: '+ str(np.std(sit_acc_z)))\n",
    "\n",
    "print('sitting mean android gyr_x: '+ str(np.mean(sit_gyr_x)))\n",
    "print('sitting std android gyr_x: '+ str(np.std(sit_gyr_x)))\n",
    "\n",
    "print('sitting mean android gyr_y: '+ str(np.mean(sit_gyr_y)))\n",
    "print('sitting std android gyr_y: '+ str(np.std(sit_gyr_y)))\n",
    "\n",
    "print('sitting mean android gyr_z: '+ str(np.mean(sit_gyr_z)))\n",
    "print('sitting std android gyr_z: '+ str(np.std(sit_gyr_z)))\n",
    "\n",
    "\n",
    "plt.plot(sit_acc_x)\n",
    "plt.show()\n",
    "plt.plot(sit_acc_y)\n",
    "plt.show()\n",
    "plt.plot(sit_acc_z)\n",
    "plt.show()\n",
    "plt.plot(sit_gyr_x)\n",
    "plt.show()\n",
    "plt.plot(sit_gyr_y)\n",
    "plt.show()\n",
    "plt.plot(sit_gyr_z)\n",
    "plt.show()"
   ]
  }
 ],
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   "display_name": "Python 3 (ipykernel)",
   "language": "python",
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