etl_pipeline_fall_detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "import sys\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.interpolate import CubicSpline\n",
    "from datetime import datetime, timedelta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "path=\"C:\\\\Users\\\\ninja\\\\Desktop\\\\6_proje_makalesi\\\\1_fall_detection\\\\proje dosyaları ve kodları\\\\veri_etiketleme_etl_pipeline\\\\veriler\"\n",
    "detections_path=path + \"\\\\alp_sag_detection_dataDogru___20240821_175540_detections.csv\"\n",
    "imu_path=path +\"\\\\alp_sag_imu_data_20240821_175456_imu_data.csv\" # imu \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.interpolate import CubicSpline\n",
    "from datetime import timedelta\n",
    "import sys\n",
    "\n",
    "class ExtractFallData:\n",
    "    def __init__(self):\n",
    "        pass\n",
    "\n",
    "    @staticmethod\n",
    "    def ekf_filter(z, x_est, P, F, Q, H, R):\n",
    "        x_pred = np.dot(F, x_est)  # State prediction\n",
    "        P_pred = np.dot(F, np.dot(P, F.T)) + Q  # Covariance prediction\n",
    "\n",
    "        # Measurement update\n",
    "        y = z - np.dot(H, x_pred)  # Measurement residual\n",
    "        S = np.dot(H, np.dot(P_pred, H.T)) + R  # Residual covariance\n",
    "        K = np.dot(P_pred, np.dot(H.T, np.linalg.inv(S)))  # Kalman gain\n",
    "\n",
    "        x_est_new = x_pred + np.dot(K, y)  # State update\n",
    "        P_new = P_pred - np.dot(K, np.dot(H, P_pred))  # Covariance update\n",
    "\n",
    "        return x_est_new, P_new\n",
    "\n",
    "    @staticmethod\n",
    "    def process_and_interpolate(filtered_df, desired_freq=50/1000):\n",
    "        filtered_df['timestamp'] = pd.to_datetime(filtered_df['timestamp'], errors='coerce')\n",
    "        filtered_df.dropna(subset=['timestamp'], inplace=True)\n",
    "        filtered_df['timestamp_unix'] = (filtered_df['timestamp'].astype(np.int64) // 10**6)\n",
    "        filtered_df.sort_values(by='timestamp', inplace=True)\n",
    "    \n",
    "    \n",
    "        filtered_df = filtered_df.drop_duplicates(subset=['timestamp_unix'])\n",
    "\n",
    "        timestamps_unix = filtered_df['timestamp_unix'].values\n",
    "        values = filtered_df[['ax_filtered', 'ay_filtered', 'az_filtered', 'gx_filtered', 'gy_filtered', 'gz_filtered']].values\n",
    "        timestamps_unix, indices = np.unique(timestamps_unix, return_index=True)\n",
    "        values = values[indices]   \n",
    "        new_timestamps_unix = np.arange(timestamps_unix.min(), timestamps_unix.max(), 1 / desired_freq)\n",
    "    \n",
    "        try:\n",
    "            cs = CubicSpline(timestamps_unix, values, axis=0, bc_type='natural')\n",
    "            new_values = cs(new_timestamps_unix)\n",
    "        except ValueError as e:\n",
    "            print(f\"Error creating cubic spline: {e}\")\n",
    "            print(f\"Timestamps: {timestamps_unix}\")\n",
    "            print(f\"Values: {values}\")\n",
    "            return None   \n",
    "\n",
    "        interpolated_df = pd.DataFrame(new_values, columns=['ax_filtered', 'ay_filtered', 'az_filtered', 'gx_filtered', 'gy_filtered', 'gz_filtered'])\n",
    "        interpolated_df['timestamp_unix'] = new_timestamps_unix\n",
    "\n",
    "        return interpolated_df\n",
    "    @staticmethod\n",
    "    def apply_threshold(interpolated_df):\n",
    "        columns_to_zero = ['ax_filtered', 'ay_filtered', 'az_filtered', 'gx_filtered', 'gy_filtered', 'gz_filtered']\n",
    "        for column in columns_to_zero:\n",
    "            if column in interpolated_df.columns:\n",
    "                interpolated_df[column] = interpolated_df[column].where((interpolated_df[column] <= 20) & (interpolated_df[column] >= -20), 0)\n",
    "        return interpolated_df\n",
    "\n",
    "    @staticmethod\n",
    "    def extract_imu_data(imu_df, x_est, P, F, Q, H, R):\n",
    "        filtered_values = []\n",
    "        for i in range(len(imu_df)):\n",
    "            z = imu_df.iloc[i][['ax', 'ay', 'az', 'gx', 'gy', 'gz']].values\n",
    "            x_est, P = ExtractFallData.ekf_filter(z, x_est, P, F, Q, H, R)\n",
    "            filtered_values.append(x_est)\n",
    "        return pd.DataFrame(filtered_values, columns=['ax_filtered', 'ay_filtered', 'az_filtered', 'gx_filtered', 'gy_filtered', 'gz_filtered'])\n",
    "\n",
    "    def two_esp_extractor(self, detections_path, imu_path):\n",
    "        detections_df = pd.read_csv(detections_path)\n",
    "        interpolated_imu = pd.read_csv(imu_path)\n",
    "        \n",
    "        imu_df_1 = interpolated_imu[interpolated_imu['topic'] == 'esp32_1/data'].sort_values(by='timestamp')\n",
    "        imu_df_2 = interpolated_imu[interpolated_imu['topic'] == 'esp32_2/data'].sort_values(by='timestamp')\n",
    "\n",
    "        F = np.eye(6)\n",
    "        H = np.eye(6)\n",
    "        Q = np.eye(6) * 0.01\n",
    "        R = np.eye(6) * 0.1\n",
    "        P = np.eye(6)\n",
    "        x_est = np.zeros(6)\n",
    "\n",
    "        filtered_df_1 = self.extract_imu_data(imu_df_1, x_est, P, F, Q, H, R)\n",
    "        filtered_df_1['timestamp'] = imu_df_1['timestamp'].reset_index(drop=True)\n",
    "\n",
    "        filtered_df_2 = self.extract_imu_data(imu_df_2, x_est, P, F, Q, H, R)\n",
    "        filtered_df_2['timestamp'] = imu_df_2['timestamp'].reset_index(drop=True)\n",
    "\n",
    "        interpolated_df_1 = self.process_and_interpolate(filtered_df_1)\n",
    "        interpolated_df_2 = self.process_and_interpolate(filtered_df_2)\n",
    "\n",
    "        interpolated_imu_1 = self.apply_threshold(interpolated_df_1)\n",
    "        interpolated_imu_2 = self.apply_threshold(interpolated_df_2)\n",
    "\n",
    "        \n",
    "        interpolated_imu_1['timestamp'] = pd.to_datetime(interpolated_imu_1['timestamp_unix'], unit='ms')\n",
    "        interpolated_imu_2['timestamp'] = pd.to_datetime(interpolated_imu_2['timestamp_unix'], unit='ms')\n",
    "        first_timestamp = detections_df['Timestamp'].iloc[0]\n",
    "        last_timestamp = detections_df['Timestamp'].iloc[-1]\n",
    "\n",
    "        \n",
    "        interpolated_imu_1 = interpolated_imu_1[(interpolated_imu_1['timestamp'] > first_timestamp) & (interpolated_imu_1['timestamp'] < last_timestamp)]\n",
    "        interpolated_imu_2 = interpolated_imu_2[(interpolated_imu_2['timestamp'] > first_timestamp) & (interpolated_imu_2['timestamp'] < last_timestamp)]\n",
    "\n",
    "        detections_df['Timestamp'] = pd.to_datetime(detections_df['Timestamp'], format='%Y-%m-%d %H:%M:%S.%f', errors='coerce')\n",
    "\n",
    "        \n",
    "        interpolated_imu_1['Class Name'] = 'not_falling'\n",
    "        self.label_falling_periods(interpolated_imu_1, detections_df)\n",
    "        self.label_falling_periods(interpolated_imu_2, detections_df)\n",
    "\n",
    "        return interpolated_imu_1, interpolated_imu_2, detections_df\n",
    "\n",
    "    @staticmethod\n",
    "    def label_falling_periods(interpolated_imu, detections_df):\n",
    "        count = 0\n",
    "        falling_start_time = None\n",
    "\n",
    "        for index, row in detections_df.iterrows():\n",
    "            if row['Class Name'] == 'falling':\n",
    "                count += 1\n",
    "                if count == 5:\n",
    "                    falling_start_time = detections_df.iloc[index - 4]['Timestamp']  \n",
    "                    start_time = falling_start_time - timedelta(seconds=0.8)  \n",
    "                    interpolated_imu.loc[(interpolated_imu['timestamp'] >= start_time) & (interpolated_imu['timestamp'] <= falling_start_time), 'Class Name'] = 'falling'\n",
    "                    interpolated_imu.loc[(interpolated_imu['timestamp'] > falling_start_time), 'Class Name'] = 'not_falling'\n",
    "                    count = 0 \n",
    "            else:\n",
    "                count = 0\n",
    "\n",
    "        falling_start_time = None\n",
    "        for index, row in interpolated_imu.iterrows():\n",
    "            if row['Class Name'] == 'falling':\n",
    "                if falling_start_time is None:\n",
    "                    falling_start_time = row['timestamp']\n",
    "                elif row['timestamp'] - falling_start_time > timedelta(seconds=1.6):\n",
    "                    interpolated_imu.at[index, 'Class Name'] = 'not_falling'\n",
    "            else:\n",
    "                falling_start_time = None\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "etl = ExtractFallData()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ninja\\AppData\\Local\\Temp\\ipykernel_39716\\4023519061.py:111: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  interpolated_imu_1['Class Name'] = 'not_falling'\n",
      "C:\\Users\\ninja\\AppData\\Local\\Temp\\ipykernel_39716\\4023519061.py:128: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  interpolated_imu.loc[(interpolated_imu['timestamp'] >= start_time) & (interpolated_imu['timestamp'] <= falling_start_time), 'Class Name'] = 'falling'\n"
     ]
    }
   ],
   "source": [
    "interpolated_imu_1, interpolated_imu_2, detections_df = etl.two_esp_extractor(detections_path, imu_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <td>7.584612</td>\n",
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       "      <td>2024-08-21 17:55:44.888</td>\n",
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       "      <td>10.685932</td>\n",
       "      <td>0.524652</td>\n",
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       "      <td>1.038288</td>\n",
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       "      <td>1.724263e+12</td>\n",
       "      <td>2024-08-21 17:55:44.948</td>\n",
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       "      <th>8844</th>\n",
       "      <td>-5.026528</td>\n",
       "      <td>-5.062301</td>\n",
       "      <td>-10.102765</td>\n",
       "      <td>-1.571690</td>\n",
       "      <td>-1.652236</td>\n",
       "      <td>2.247382</td>\n",
       "      <td>1.724263e+12</td>\n",
       "      <td>2024-08-21 17:58:37.648</td>\n",
       "      <td>not_falling</td>\n",
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       "      <td>-10.261009</td>\n",
       "      <td>-1.458750</td>\n",
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       "      <td>2.098301</td>\n",
       "      <td>1.724263e+12</td>\n",
       "      <td>2024-08-21 17:58:37.668</td>\n",
       "      <td>not_falling</td>\n",
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       "      <td>-5.882477</td>\n",
       "      <td>-2.932780</td>\n",
       "      <td>-11.091636</td>\n",
       "      <td>-1.340353</td>\n",
       "      <td>-1.018545</td>\n",
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       "      <td>1.724263e+12</td>\n",
       "      <td>2024-08-21 17:58:37.688</td>\n",
       "      <td>not_falling</td>\n",
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       "      <td>1.724263e+12</td>\n",
       "      <td>2024-08-21 17:58:37.708</td>\n",
       "      <td>not_falling</td>\n",
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       "      <td>not_falling</td>\n",
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       "<p>8643 rows × 9 columns</p>\n",
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      ],
      "text/plain": [
       "      ax_filtered  ay_filtered  az_filtered  gx_filtered  gy_filtered  \\\n",
       "206      7.584612    10.132272     0.463267     1.621311     0.701167   \n",
       "207      7.446307    10.359714     0.512082     1.468659     0.778727   \n",
       "208      7.172568    10.530567     0.537091     1.313954     0.889188   \n",
       "209      6.815641    10.685932     0.524652     1.149797     1.038288   \n",
       "210      6.402705    10.773990     0.397876     1.020254     1.180785   \n",
       "...           ...          ...          ...          ...          ...   \n",
       "8844    -5.026528    -5.062301   -10.102765    -1.571690    -1.652236   \n",
       "8845    -5.152721    -4.135279   -10.261009    -1.458750    -1.378814   \n",
       "8846    -5.882477    -2.932780   -11.091636    -1.340353    -1.018545   \n",
       "8847    -6.300267    -2.165874   -11.474019    -1.237328    -0.747149   \n",
       "8848    -6.094230    -1.978118   -11.087933    -1.156209    -0.605860   \n",
       "\n",
       "      gz_filtered  timestamp_unix               timestamp   Class Name  \n",
       "206     -0.930295    1.724263e+12 2024-08-21 17:55:44.888      falling  \n",
       "207     -0.958439    1.724263e+12 2024-08-21 17:55:44.908      falling  \n",
       "208     -0.972525    1.724263e+12 2024-08-21 17:55:44.928      falling  \n",
       "209     -0.971165    1.724263e+12 2024-08-21 17:55:44.948      falling  \n",
       "210     -0.953356    1.724263e+12 2024-08-21 17:55:44.968      falling  \n",
       "...           ...             ...                     ...          ...  \n",
       "8844     2.247382    1.724263e+12 2024-08-21 17:58:37.648  not_falling  \n",
       "8845     2.098301    1.724263e+12 2024-08-21 17:58:37.668  not_falling  \n",
       "8846     1.914958    1.724263e+12 2024-08-21 17:58:37.688  not_falling  \n",
       "8847     1.728567    1.724263e+12 2024-08-21 17:58:37.708  not_falling  \n",
       "8848     1.553912    1.724263e+12 2024-08-21 17:58:37.728  not_falling  \n",
       "\n",
       "[8643 rows x 9 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interpolated_imu_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>ax_filtered</th>\n",
       "      <th>ay_filtered</th>\n",
       "      <th>az_filtered</th>\n",
       "      <th>gx_filtered</th>\n",
       "      <th>gy_filtered</th>\n",
       "      <th>gz_filtered</th>\n",
       "      <th>timestamp_unix</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>Class Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>-5.419303</td>\n",
       "      <td>9.167630</td>\n",
       "      <td>-3.488300</td>\n",
       "      <td>0.378283</td>\n",
       "      <td>1.364569</td>\n",
       "      <td>-0.568936</td>\n",
       "      <td>1.724263e+12</td>\n",
       "      <td>2024-08-21 17:55:44.887000064</td>\n",
       "      <td>falling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>-4.788715</td>\n",
       "      <td>9.865896</td>\n",
       "      <td>-1.894273</td>\n",
       "      <td>0.629358</td>\n",
       "      <td>0.962854</td>\n",
       "      <td>-0.512865</td>\n",
       "      <td>1.724263e+12</td>\n",
       "      <td>2024-08-21 17:55:44.907000064</td>\n",
       "      <td>falling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>-3.786127</td>\n",
       "      <td>10.067715</td>\n",
       "      <td>-1.538152</td>\n",
       "      <td>0.734812</td>\n",
       "      <td>0.910025</td>\n",
       "      <td>-0.511990</td>\n",
       "      <td>1.724263e+12</td>\n",
       "      <td>2024-08-21 17:55:44.927000064</td>\n",
       "      <td>falling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>-3.539241</td>\n",
       "      <td>9.976071</td>\n",
       "      <td>-2.155057</td>\n",
       "      <td>0.800648</td>\n",
       "      <td>0.892626</td>\n",
       "      <td>-0.501407</td>\n",
       "      <td>1.724263e+12</td>\n",
       "      <td>2024-08-21 17:55:44.947000064</td>\n",
       "      <td>falling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>-4.015271</td>\n",
       "      <td>9.757013</td>\n",
       "      <td>-2.805410</td>\n",
       "      <td>0.885365</td>\n",
       "      <td>0.730984</td>\n",
       "      <td>-0.443543</td>\n",
       "      <td>1.724263e+12</td>\n",
       "      <td>2024-08-21 17:55:44.967000064</td>\n",
       "      <td>falling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10277</th>\n",
       "      <td>19.019658</td>\n",
       "      <td>3.071444</td>\n",
       "      <td>-0.915498</td>\n",
       "      <td>-1.604479</td>\n",
       "      <td>-10.690152</td>\n",
       "      <td>2.674269</td>\n",
       "      <td>1.724263e+12</td>\n",
       "      <td>2024-08-21 17:59:06.287000064</td>\n",
       "      <td>not_falling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10278</th>\n",
       "      <td>8.294459</td>\n",
       "      <td>5.444997</td>\n",
       "      <td>3.059237</td>\n",
       "      <td>-0.825387</td>\n",
       "      <td>-6.321310</td>\n",
       "      <td>-0.537672</td>\n",
       "      <td>1.724263e+12</td>\n",
       "      <td>2024-08-21 17:59:06.307000064</td>\n",
       "      <td>not_falling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10279</th>\n",
       "      <td>-0.003505</td>\n",
       "      <td>7.521016</td>\n",
       "      <td>2.347053</td>\n",
       "      <td>-0.449788</td>\n",
       "      <td>-2.443959</td>\n",
       "      <td>-2.099323</td>\n",
       "      <td>1.724263e+12</td>\n",
       "      <td>2024-08-21 17:59:06.327000064</td>\n",
       "      <td>not_falling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10280</th>\n",
       "      <td>-2.745932</td>\n",
       "      <td>7.642096</td>\n",
       "      <td>-2.204985</td>\n",
       "      <td>-0.385891</td>\n",
       "      <td>-0.468596</td>\n",
       "      <td>-0.473108</td>\n",
       "      <td>1.724263e+12</td>\n",
       "      <td>2024-08-21 17:59:06.347000064</td>\n",
       "      <td>not_falling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10281</th>\n",
       "      <td>-5.411624</td>\n",
       "      <td>0.689026</td>\n",
       "      <td>-3.359028</td>\n",
       "      <td>-1.144656</td>\n",
       "      <td>1.163404</td>\n",
       "      <td>-0.412006</td>\n",
       "      <td>1.724263e+12</td>\n",
       "      <td>2024-08-21 17:59:06.367000064</td>\n",
       "      <td>not_falling</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10075 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ax_filtered  ay_filtered  az_filtered  gx_filtered  gy_filtered  \\\n",
       "207      -5.419303     9.167630    -3.488300     0.378283     1.364569   \n",
       "208      -4.788715     9.865896    -1.894273     0.629358     0.962854   \n",
       "209      -3.786127    10.067715    -1.538152     0.734812     0.910025   \n",
       "210      -3.539241     9.976071    -2.155057     0.800648     0.892626   \n",
       "211      -4.015271     9.757013    -2.805410     0.885365     0.730984   \n",
       "...            ...          ...          ...          ...          ...   \n",
       "10277    19.019658     3.071444    -0.915498    -1.604479   -10.690152   \n",
       "10278     8.294459     5.444997     3.059237    -0.825387    -6.321310   \n",
       "10279    -0.003505     7.521016     2.347053    -0.449788    -2.443959   \n",
       "10280    -2.745932     7.642096    -2.204985    -0.385891    -0.468596   \n",
       "10281    -5.411624     0.689026    -3.359028    -1.144656     1.163404   \n",
       "\n",
       "       gz_filtered  timestamp_unix                     timestamp   Class Name  \n",
       "207      -0.568936    1.724263e+12 2024-08-21 17:55:44.887000064      falling  \n",
       "208      -0.512865    1.724263e+12 2024-08-21 17:55:44.907000064      falling  \n",
       "209      -0.511990    1.724263e+12 2024-08-21 17:55:44.927000064      falling  \n",
       "210      -0.501407    1.724263e+12 2024-08-21 17:55:44.947000064      falling  \n",
       "211      -0.443543    1.724263e+12 2024-08-21 17:55:44.967000064      falling  \n",
       "...            ...             ...                           ...          ...  \n",
       "10277     2.674269    1.724263e+12 2024-08-21 17:59:06.287000064  not_falling  \n",
       "10278    -0.537672    1.724263e+12 2024-08-21 17:59:06.307000064  not_falling  \n",
       "10279    -2.099323    1.724263e+12 2024-08-21 17:59:06.327000064  not_falling  \n",
       "10280    -0.473108    1.724263e+12 2024-08-21 17:59:06.347000064  not_falling  \n",
       "10281    -0.412006    1.724263e+12 2024-08-21 17:59:06.367000064  not_falling  \n",
       "\n",
       "[10075 rows x 9 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interpolated_imu_1"
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  {
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   "execution_count": 8,
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   "outputs": [
    {
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       "      <th>3782</th>\n",
       "      <td>2024-08-21 17:59:06.176</td>\n",
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       "      <td>3.0</td>\n",
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       "      <th>3783</th>\n",
       "      <td>2024-08-21 17:59:06.226</td>\n",
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       "      <th>3784</th>\n",
       "      <td>2024-08-21 17:59:06.276</td>\n",
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       "      <th>3785</th>\n",
       "      <td>2024-08-21 17:59:06.326</td>\n",
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       "      <th>3786</th>\n",
       "      <td>2024-08-21 17:59:06.376</td>\n",
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       "      <td>3.0</td>\n",
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       "<p>3787 rows × 4 columns</p>\n",
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       "                   Timestamp   Class Name  Confidence  Track ID\n",
       "0    2024-08-21 17:55:44.876      falling    0.345433       NaN\n",
       "1    2024-08-21 17:55:44.926      falling    0.479454       NaN\n",
       "2    2024-08-21 17:55:44.976      falling    0.290418       NaN\n",
       "3    2024-08-21 17:55:45.026      falling    0.515806       NaN\n",
       "4    2024-08-21 17:55:45.126      falling    0.116733       NaN\n",
       "...                      ...          ...         ...       ...\n",
       "3782 2024-08-21 17:59:06.176  not_falling    0.590795       3.0\n",
       "3783 2024-08-21 17:59:06.226  not_falling    0.334215       3.0\n",
       "3784 2024-08-21 17:59:06.276  not_falling    0.358066       3.0\n",
       "3785 2024-08-21 17:59:06.326  not_falling    0.160431       3.0\n",
       "3786 2024-08-21 17:59:06.376  not_falling    0.128574       3.0\n",
       "\n",
       "[3787 rows x 4 columns]"
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     "execution_count": 8,
     "metadata": {},
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   "source": [
    "detections_df"
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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