LSTM_of_walking_recognation_HAR.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/JonathanCristovao/Human-Activity-Recognition-HAR-LSTM/blob/main/LSTM_of_walking_recognation_HAR.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# This notebook was run on google colab"
      ],
      "metadata": {
        "id": "16WhqkwcseIu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#from google.colab import drive\n",
        "#drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_pcRQvKt9r4Q",
        "outputId": "c3df2612-86d9-4106-b781-3993f784ef25"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "csFMJZEe9I0I"
      },
      "source": [
        "# Import Library"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install keras_self_attention"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ai6fqg4x9rM9",
        "outputId": "184c99ec-5a11-4410-bd8c-ce523fce4a4b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting keras_self_attention\n",
            "  Downloading keras-self-attention-0.51.0.tar.gz (11 kB)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from keras_self_attention) (1.21.6)\n",
            "Building wheels for collected packages: keras-self-attention\n",
            "  Building wheel for keras-self-attention (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for keras-self-attention: filename=keras_self_attention-0.51.0-py3-none-any.whl size=18912 sha256=b5675f3369947a5721d15d0b28aec1fd1fba4fa7d6005dcb7d5eeaa86bd75c10\n",
            "  Stored in directory: /root/.cache/pip/wheels/95/b1/a8/5ee00cc137940b2f6fa198212e8f45d813d0e0d9c3a04035a3\n",
            "Successfully built keras-self-attention\n",
            "Installing collected packages: keras-self-attention\n",
            "Successfully installed keras-self-attention-0.51.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import tensorflow as tf\n",
        "from scipy import stats\n",
        "from sklearn import metrics\n",
        "from sklearn.metrics import classification_report\n",
        "import glob\n",
        "import csv\n",
        "import os\n",
        "%matplotlib inline\n",
        "from keras_self_attention import SeqSelfAttention\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_52yqQ6hc560",
        "outputId": "095ed272-f9ac-4361-9bb2-bc03b06bc308"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Default GPU Device: /device:GPU:0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## The dataset used in this notebook can be downloaded here:\n",
        "[Click here to download the Dataset](https://www.kaggle.com/datasets/jonathansilva2020/dataset-for-human-activity-recognition-har)\n",
        "\n"
      ],
      "metadata": {
        "id": "1-ak4Az0j11p"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_CZofzgJ9I0K"
      },
      "outputs": [],
      "source": [
        "interesting_dir = \"/content/drive/MyDrive/dataset_artigo_sensores_new\"\n",
        "interesting_labels = ['seated', 'stand','walk']\n",
        "save_filename = \"activity\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0tddFIoe9I0M"
      },
      "source": [
        "## Concate and label the raw data  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FXYz5psi9I0N"
      },
      "outputs": [],
      "source": [
        "def data_concate_label(path,label):\n",
        "    all_files1 = glob.glob(path + \"/*.csv\")\n",
        "    \n",
        "    li = []\n",
        "\n",
        "    for filename in all_files1:\n",
        "        df = pd.read_csv(filename, index_col=None, header=0)\n",
        "        \n",
        "        if 'label' in df.columns:\n",
        "            pass\n",
        "        else:df['label'] = str(label)\n",
        "        li.append(df)\n",
        "    frame = pd.concat(li, axis=0, ignore_index=True)\n",
        "    if len(df.columns) == 8 :\n",
        "        new_header =['date','time','ID_S','Q_I','Q_J','Q_K','Q_R','label']\n",
        "    \n",
        "    else :\n",
        "       \n",
        "        new_header =['date','time','ID_S','Q_I','Q_J','Q_K','Q_R','label']\n",
        "\n",
        "    frame = frame[:16000]\n",
        "    \n",
        "    print(frame.shape)\n",
        "    print(len(df.columns))\n",
        "    frame.to_csv(\"/content/drive/MyDrive/data/novo_\"+str(label)+\".csv\",index = False,header=new_header)\n",
        "    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n-B5PEIQ9I0P",
        "outputId": "c284c7bb-0e1a-41b2-b188-6ce4881ecea1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(7267, 8)\n",
            "8\n",
            "(6110, 8)\n",
            "8\n",
            "(4980, 8)\n",
            "8\n"
          ]
        }
      ],
      "source": [
        "path1 = \"/content/drive/MyDrive/dataset_artigo_sensores_new/seated\"\n",
        "label1 = \"seated\"\n",
        "path2 = \"/content/drive/MyDrive/dataset_artigo_sensores_new/stand\"\n",
        "label2 = \"stand\"\n",
        "path3 = \"/content/drive/MyDrive/dataset_artigo_sensores_new/walking\"\n",
        "label3 = \"walk\"\n",
        "data_concate_label(path1,label1)\n",
        "data_concate_label(path2,label2)\n",
        "data_concate_label(path3,label3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TGWpUbXX9I0P"
      },
      "source": [
        "## Concate all data into one .csv file"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": false,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "3jfl5sIf9I0Q",
        "outputId": "b0408d51-aaa5-4df7-bb21-4c34736f4085"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   ID_S   Q_I   Q_J   Q_K   Q_R   label\n",
              "0     2  0.09  0.02  0.79  0.61  seated\n",
              "1     4  0.02 -0.04  0.73  0.68  seated\n",
              "2     1  0.27  0.58 -0.09  0.77  seated\n",
              "3     3  0.29  0.53 -0.44  0.66  seated\n",
              "4     2  0.09  0.02  0.79  0.61  seated"
            ],
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              "\n",
              "  <div id=\"df-8c045caf-ad37-411b-94f1-3cc84743d2e0\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>ID_S</th>\n",
              "      <th>Q_I</th>\n",
              "      <th>Q_J</th>\n",
              "      <th>Q_K</th>\n",
              "      <th>Q_R</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.79</td>\n",
              "      <td>0.61</td>\n",
              "      <td>seated</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "      <td>0.02</td>\n",
              "      <td>-0.04</td>\n",
              "      <td>0.73</td>\n",
              "      <td>0.68</td>\n",
              "      <td>seated</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.58</td>\n",
              "      <td>-0.09</td>\n",
              "      <td>0.77</td>\n",
              "      <td>seated</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>0.29</td>\n",
              "      <td>0.53</td>\n",
              "      <td>-0.44</td>\n",
              "      <td>0.66</td>\n",
              "      <td>seated</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.79</td>\n",
              "      <td>0.61</td>\n",
              "      <td>seated</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "      </button>\n",
              "      \n",
              "  <style>\n",
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              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8c045caf-ad37-411b-94f1-3cc84743d2e0 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8c045caf-ad37-411b-94f1-3cc84743d2e0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "import copy\n",
        "\n",
        "df = pd.DataFrame()\n",
        "for i in range(len(interesting_labels)):\n",
        "    df_tmp = pd.read_csv(interesting_dir+\"/\"+save_filename+\"_\"+interesting_labels[i]+'.csv',header = 0)\n",
        "    df = pd.concat([df, df_tmp])\n",
        "print(len(df.columns))\n",
        "\n",
        "drop_list = ['date','time']\n",
        "\n",
        "df = df.drop(drop_list, axis=1)\n",
        "df = df.dropna()\n",
        "\n",
        "all_data_filename = \"total.csv\"\n",
        "df.to_csv(interesting_dir+all_data_filename)\n",
        "\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pIVWZ7qF9I0S"
      },
      "source": [
        "## Feature correlation "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T6lqHghB9I0S",
        "outputId": "11728271-b418-43fb-e76f-1ce81df4361c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['ID_S', 'Q_I', 'Q_J', 'Q_K', 'Q_R']\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ID_S    0\n",
              "Q_I     0\n",
              "Q_J     0\n",
              "Q_K     0\n",
              "Q_R     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "df_cor = copy.deepcopy(df) \n",
        "\n",
        "features = df_cor.columns[0:-1]\n",
        "df_cor[\"label\"] = pd.factorize(df_cor.label)[0]\n",
        "traindf = pd.DataFrame(df_cor,columns=features)\n",
        "\n",
        "feature_name = list(traindf.columns)\n",
        "print(feature_name)\n",
        "y = df_cor['label']\n",
        "X = traindf.copy()\n",
        "X.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "hK9Au9z29I0T",
        "outputId": "c960a630-3552-47c4-fcc1-268764cabb28"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.08077179 0.16421936 0.20482018 0.23527106 0.31491761]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "from sklearn.ensemble import ExtraTreesClassifier\n",
        "import matplotlib.pyplot as plt\n",
        "model = ExtraTreesClassifier()\n",
        "model.fit(X,y)\n",
        "print(model.feature_importances_) \n",
        "#plot graph of feature importances for better visualization\n",
        "feat_importances = pd.Series(model.feature_importances_, index=X.columns)\n",
        "feat_importances.nlargest(10).plot(kind='barh')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "scrolled": true,
        "id": "5-HsSboK9I0U"
      },
      "outputs": [],
      "source": [
        "#get correlations of each features in dataset\n",
        "#corrmat = df_cor.corr()\n",
        "#top_corr_features = corrmat.index\n",
        "#plt.figure(figsize=(20,20))\n",
        "#plot heat map\n",
        "#g=sns.heatmap(df_cor[top_corr_features].corr(),annot=True,cmap=\"RdYlGn\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TMpkc4pQ9I0U",
        "outputId": "34bc7de7-3838-4f8d-fc9a-626b5c7acdee"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['ID_S', 'Q_I', 'Q_J', 'Q_K', 'Q_R']"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "num_feats= len(feature_name)\n",
        "X.columns.tolist()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Etbf339z9I0V",
        "outputId": "b601bbb3-dd55-4555-a479-d1058bf33ac4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'ID_S': 1.2412168298060989e-19,\n",
              " 'Q_I': 0.22076947143773396,\n",
              " 'Q_J': 0.2078337620343928,\n",
              " 'Q_K': 0.528879629654535,\n",
              " 'Q_R': 0.4553419160597277}"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "def cor_selector(X, y,num_feats):\n",
        "    cor_list = []\n",
        "    feature_name = X.columns.tolist()\n",
        "    \n",
        "    # calculate the correlation with y for each feature\n",
        "    for i in X.columns.tolist():\n",
        "        cor = np.corrcoef(X[i], y)[0, 1]\n",
        "        cor_list.append(cor)\n",
        "        \n",
        "    # replace NaN with 0\n",
        "    cor_list = [0 if np.isnan(i) else i for i in cor_list]\n",
        "    \n",
        "    # feature name\n",
        "    cor_feature = X.iloc[:,np.argsort(np.abs(cor_list))[-num_feats:]].columns.tolist()\n",
        "   \n",
        "    # feature selection? 0 for not select, 1 for select\n",
        "    cor_support = [True if i in cor_feature else False for i in feature_name]\n",
        "\n",
        "    return cor_support, cor_feature, cor_list\n",
        "\n",
        "cor_support, cor_feature, cor_list = cor_selector(X, y,num_feats)\n",
        "cor_val=[abs(x) for x in cor_list]\n",
        "cor_result = dict(zip(cor_feature, cor_val))\n",
        "myDict = {key:val for key, val in cor_result.items() if val > 0}\n",
        "{k: v for k, v in sorted(myDict.items(),reverse=True, key=lambda item: item[1])}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qS2rAhS99I0W",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "13e929d3-71df-4dcd-eab7-fc7609af0c50"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['seated', 'stand', 'walk'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "activities = df['label'].value_counts().index\n",
        "activities"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "huRwjGga9I0Z"
      },
      "source": [
        "## Split training and testing data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vEqVzdL-9I0Z",
        "outputId": "3d7b03bf-ac8d-47f9-d006-8c0929a94860"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of train samples:  1556\n",
            "Number of test samples:  390\n"
          ]
        }
      ],
      "source": [
        "def train_test_split(label, ratio):\n",
        "    split_point = int(len(df[df.label == label]) * ratio)\n",
        "    return (df[df.label == label].iloc[:split_point, :], df[df.label == label].iloc[split_point:, :])\n",
        "\n",
        "split_ratio = 0.8\n",
        "train_data = pd.DataFrame([])\n",
        "test_data = pd.DataFrame([])\n",
        "\n",
        "for i in range(len(interesting_labels)):\n",
        "    (train, test) = train_test_split(interesting_labels[i], split_ratio)\n",
        "    train_data = pd.concat([train_data, train])\n",
        "    test_data = pd.concat([test_data, test])\n",
        "\n",
        "print(\"Number of train samples: \", len(train_data))\n",
        "print(\"Number of test samples: \", len(test_data))\n",
        "\n",
        "\n",
        "train_label = train_data['label'].to_frame()\n",
        "test_label = test_data['label'].to_frame()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9ZCSA5xL9I0Z"
      },
      "source": [
        "## Data Segmentation (Sliding Window)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "err6EfHS9I0Z"
      },
      "outputs": [],
      "source": [
        "N_TIME_STEPS = 10 #sliding window length 120\n",
        "STEP = 2 # Sliding window step size 20\n",
        "N_FEATURES = 5\n",
        "\n",
        "def generate_sequence(x, y, n_time_steps, step):\n",
        "    \n",
        "    segments = []\n",
        "    labels = []\n",
        "    for i in range(0, len(x) - n_time_steps, step):\n",
        "\n",
        "        ID_S = x['ID_S'].values[i: i + n_time_steps]\n",
        "        Q_I = x['Q_I'].values[i: i + n_time_steps]\n",
        "        Q_J = x['Q_J'].values[i: i + n_time_steps]\n",
        "        Q_K = x['Q_K'].values[i: i + n_time_steps]\n",
        "        Q_R = x['Q_R'].values[i: i + n_time_steps]\n",
        "     \n",
        "        label = stats.mode(y['label'][i: i + n_time_steps])[0][0]\n",
        "        segments.append([ID_S, Q_I, Q_J, Q_K, Q_R])\n",
        "        labels.append(label)\n",
        "        \n",
        "    return segments, labels\n",
        "\n",
        "train_X, train_y = generate_sequence(train_data, train_label, N_TIME_STEPS, STEP)\n",
        "test_X, test_y = generate_sequence(test_data, test_label, N_TIME_STEPS, STEP)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Pt-HNaqG9I0Z"
      },
      "outputs": [],
      "source": [
        "# reshape input segments and one-hot encode labels\n",
        "def reshape_segments(x, y, n_time_steps, n_features):\n",
        "    \n",
        "    x_reshaped = np.asarray(x, dtype= np.float32).reshape(-1, n_time_steps, n_features)\n",
        "    y_reshaped = np.asarray(pd.get_dummies(y), dtype = np.float32)\n",
        "    return x_reshaped, y_reshaped\n",
        "\n",
        "X_train, y_train = reshape_segments(train_X, train_y, N_TIME_STEPS, N_FEATURES)\n",
        "X_test, y_test = reshape_segments(test_X, test_y, N_TIME_STEPS, N_FEATURES)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "jJdwMt1L9I0a"
      },
      "outputs": [],
      "source": [
        "#y_test"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zjlqEMru9I0a"
      },
      "source": [
        "## Import DL Library"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PFFl4sX99I0a"
      },
      "outputs": [],
      "source": [
        "from keras.regularizers import l2\n",
        "from keras.models import Sequential, load_model\n",
        "from keras.layers import Dense, LSTM, Flatten, Bidirectional\n",
        "from tensorflow.keras.layers import Attention\n",
        "from keras.layers import Conv2D, MaxPooling2D, Dropout\n",
        "from keras import optimizers\n",
        "from keras import backend as K\n",
        "from tensorflow.python.tools import freeze_graph\n",
        "from tensorflow.python.tools import optimize_for_inference_lib\n",
        "\n",
        "from keras.callbacks import ModelCheckpoint, LearningRateScheduler, TensorBoard, EarlyStopping, ReduceLROnPlateau\n",
        "N_CLASSES = 3\n",
        "L2 = 0.00001"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HYaKUb7w9I0b"
      },
      "source": [
        "## LSTM Model "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v2ojrWNt9I0b",
        "outputId": "17e21d4e-5a62-402e-a8d9-656ec0779575"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " bidirectional (Bidirectiona  (None, 10, 400)          329600    \n",
            " l)                                                              \n",
            "                                                                 \n",
            " Flatten (Flatten)           (None, 4000)              0         \n",
            "                                                                 \n",
            " Dense_3 (Dense)             (None, 128)               512128    \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 128)               0         \n",
            "                                                                 \n",
            " Dense_4 (Dense)             (None, 3)                 387       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 842,115\n",
            "Trainable params: 842,115\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/300\n",
            "13/13 [==============================] - ETA: 0s - loss: 0.9642 - accuracy: 0.5990\n",
            "Epoch 1: val_loss improved from inf to 0.97386, saving model to har_model_lstm_2021.h5\n",
            "13/13 [==============================] - 4s 78ms/step - loss: 0.9642 - accuracy: 0.5990 - val_loss: 0.9739 - val_accuracy: 0.4737\n",
            "Epoch 2/300\n",
            " 8/13 [=================>............] - ETA: 0s - loss: 0.6797 - accuracy: 0.6953\n",
            "Epoch 2: val_loss improved from 0.97386 to 0.71728, saving model to har_model_lstm_2021.h5\n",
            "13/13 [==============================] - 0s 15ms/step - loss: 0.6242 - accuracy: 0.7594 - val_loss: 0.7173 - val_accuracy: 0.7947\n",
            "Epoch 3/300\n",
            " 8/13 [=================>............] - ETA: 0s - loss: 0.4248 - accuracy: 0.8574\n",
            "Epoch 3: val_loss improved from 0.71728 to 0.53625, saving model to har_model_lstm_2021.h5\n",
            "13/13 [==============================] - 0s 14ms/step - loss: 0.3971 - accuracy: 0.8771 - val_loss: 0.5362 - val_accuracy: 0.8105\n",
            "Epoch 4/300\n",
            " 8/13 [=================>............] - ETA: 0s - loss: 0.2357 - accuracy: 0.9355\n",
            "Epoch 4: val_loss improved from 0.53625 to 0.51398, saving model to har_model_lstm_2021.h5\n",
            "13/13 [==============================] - 0s 13ms/step - loss: 0.2237 - accuracy: 0.9405 - val_loss: 0.5140 - val_accuracy: 0.7947\n",
            "Epoch 5/300\n",
            " 8/13 [=================>............] - ETA: 0s - loss: 0.2330 - accuracy: 0.9375\n",
            "Epoch 5: val_loss did not improve from 0.51398\n",
            "13/13 [==============================] - 0s 10ms/step - loss: 0.2152 - accuracy: 0.9392 - val_loss: 0.5416 - val_accuracy: 0.8526\n",
            "Epoch 6/300\n",
            " 8/13 [=================>............] - ETA: 0s - loss: 0.1658 - accuracy: 0.9551\n",
            "Epoch 6: val_loss improved from 0.51398 to 0.37076, saving model to har_model_lstm_2021.h5\n",
            "13/13 [==============================] - 0s 14ms/step - loss: 0.1591 - accuracy: 0.9508 - val_loss: 0.3708 - val_accuracy: 0.8947\n",
            "Epoch 7/300\n",
            " 8/13 [=================>............] - ETA: 0s - loss: 0.1390 - accuracy: 0.9648\n",
            "Epoch 7: val_loss improved from 0.37076 to 0.30846, saving model to har_model_lstm_2021.h5\n",
            "13/13 [==============================] - 0s 14ms/step - loss: 0.1187 - accuracy: 0.9715 - val_loss: 0.3085 - val_accuracy: 0.9053\n",
            "Epoch 8/300\n",
            " 8/13 [=================>............] - ETA: 0s - loss: 0.1147 - accuracy: 0.9688\n",
            "Epoch 8: val_loss did not improve from 0.30846\n",
            "13/13 [==============================] - 0s 11ms/step - loss: 0.1077 - accuracy: 0.9715 - val_loss: 0.4874 - val_accuracy: 0.8316\n",
            "Epoch 9/300\n",
            " 8/13 [=================>............] - ETA: 0s - loss: 0.1521 - accuracy: 0.9609\n",
            "Epoch 9: val_loss improved from 0.30846 to 0.25013, saving model to har_model_lstm_2021.h5\n",
            "13/13 [==============================] - 0s 14ms/step - loss: 0.1282 - accuracy: 0.9677 - val_loss: 0.2501 - val_accuracy: 0.8895\n",
            "Epoch 10/300\n",
            " 8/13 [=================>............] - ETA: 0s - loss: 0.1044 - accuracy: 0.9727\n",
            "Epoch 10: val_loss improved from 0.25013 to 0.21640, saving model to har_model_lstm_2021.h5\n",
            "13/13 [==============================] - 0s 14ms/step - loss: 0.1061 - accuracy: 0.9702 - val_loss: 0.2164 - val_accuracy: 0.9263\n",
            "Epoch 11/300\n",
            " 8/13 [=================>............] - ETA: 0s - loss: 0.0734 - accuracy: 0.9883\n",
            "Epoch 11: val_loss did not improve from 0.21640\n",
            "13/13 [==============================] - 0s 11ms/step - loss: 0.0828 - accuracy: 0.9845 - val_loss: 0.3741 - val_accuracy: 0.8895\n",
            "Epoch 12/300\n",
            " 8/13 [=================>............] - ETA: 0s - loss: 0.1942 - accuracy: 0.9238\n",
            "Epoch 12: val_loss did not improve from 0.21640\n",
            "13/13 [==============================] - 0s 11ms/step - loss: 0.1822 - accuracy: 0.9288 - val_loss: 0.3092 - val_accuracy: 0.9579\n",
            "Epoch 13/300\n",
            " 8/13 [=================>............] - ETA: 0s - loss: 0.1197 - accuracy: 0.9746\n",
            "Epoch 13: val_loss improved from 0.21640 to 0.20729, saving model to har_model_lstm_2021.h5\n",
            "13/13 [==============================] - 0s 14ms/step - loss: 0.1179 - accuracy: 0.9690 - val_loss: 0.2073 - val_accuracy: 0.9421\n",
            "Epoch 14/300\n",
            "12/13 [==========================>...] - ETA: 0s - loss: 0.0971 - accuracy: 0.9766\n",
            "Epoch 14: val_loss did not improve from 0.20729\n",
            "13/13 [==============================] - 0s 13ms/step - loss: 0.0971 - accuracy: 0.9767 - val_loss: 0.2417 - val_accuracy: 0.9105\n",
            "Epoch 15/300\n",
            " 7/13 [===============>..............] - ETA: 0s - loss: 0.1029 - accuracy: 0.9688\n",
            "Epoch 15: val_loss did not improve from 0.20729\n",
            "13/13 [==============================] - 0s 11ms/step - loss: 0.0837 - accuracy: 0.9793 - val_loss: 0.5536 - val_accuracy: 0.8579\n",
            "Epoch 16/300\n",
            " 8/13 [=================>............] - ETA: 0s - loss: 0.0974 - accuracy: 0.9707\n",
            "Epoch 16: val_loss did not improve from 0.20729\n",
            "13/13 [==============================] - 0s 11ms/step - loss: 0.0846 - accuracy: 0.9780 - val_loss: 0.2177 - val_accuracy: 0.9158\n",
            "Epoch 17/300\n",
            " 8/13 [=================>............] - ETA: 0s - loss: 0.0627 - accuracy: 0.9844\n",
            "Epoch 17: val_loss improved from 0.20729 to 0.19365, saving model to har_model_lstm_2021.h5\n",
            "13/13 [==============================] - 0s 15ms/step - loss: 0.0656 - accuracy: 0.9845 - val_loss: 0.1936 - val_accuracy: 0.9526\n",
            "Epoch 18/300\n",
            " 8/13 [=================>............] - ETA: 0s - loss: 0.0772 - accuracy: 0.9824\n",
            "Epoch 18: val_loss did not improve from 0.19365\n",
            "13/13 [==============================] - 0s 11ms/step - loss: 0.0734 - accuracy: 0.9858 - val_loss: 0.2397 - val_accuracy: 0.9211\n",
            "Epoch 19/300\n",
            " 8/13 [=================>............] - ETA: 0s - loss: 0.1300 - accuracy: 0.9570\n",
            "Epoch 19: val_loss did not improve from 0.19365\n",
            "13/13 [==============================] - 0s 11ms/step - loss: 0.1268 - accuracy: 0.9573 - val_loss: 0.2717 - val_accuracy: 0.9211\n",
            "Epoch 20/300\n",
            " 8/13 [=================>............] - ETA: 0s - loss: 0.0789 - accuracy: 0.9746\n",
            "Epoch 20: val_loss did not improve from 0.19365\n",
            "13/13 [==============================] - 0s 11ms/step - loss: 0.0801 - accuracy: 0.9741 - val_loss: 0.1975 - val_accuracy: 0.9684\n",
            "Epoch 21/300\n",
            " 7/13 [===============>..............] - ETA: 0s - loss: 0.0908 - accuracy: 0.9777\n",
            "Epoch 21: val_loss improved from 0.19365 to 0.16554, saving model to har_model_lstm_2021.h5\n",
            "13/13 [==============================] - 0s 14ms/step - loss: 0.0897 - accuracy: 0.9780 - val_loss: 0.1655 - val_accuracy: 0.9632\n",
            "Epoch 22/300\n",
            " 8/13 [=================>............] - ETA: 0s - loss: 0.0789 - accuracy: 0.9785\n",
            "Epoch 22: val_loss did not improve from 0.16554\n",
            "13/13 [==============================] - 0s 10ms/step - loss: 0.0692 - accuracy: 0.9832 - val_loss: 0.1838 - val_accuracy: 0.9684\n",
            "Epoch 23/300\n",
            " 8/13 [=================>............] - ETA: 0s - loss: 0.0455 - accuracy: 0.9883\n",
            "Epoch 23: val_loss did not improve from 0.16554\n",
            "13/13 [==============================] - 0s 11ms/step - loss: 0.0491 - accuracy: 0.9871 - val_loss: 0.2015 - val_accuracy: 0.9579\n",
            "Epoch 24/300\n",
            " 8/13 [=================>............] - ETA: 0s - loss: 0.0504 - accuracy: 0.9863\n",
            "Epoch 24: val_loss did not improve from 0.16554\n",
            "13/13 [==============================] - 0s 10ms/step - loss: 0.0455 - accuracy: 0.9884 - val_loss: 0.1839 - val_accuracy: 0.9526\n",
            "Epoch 25/300\n",
            " 8/13 [=================>............] - ETA: 0s - loss: 0.0497 - accuracy: 0.9883\n",
            "Epoch 25: val_loss did not improve from 0.16554\n",
            "13/13 [==============================] - 0s 10ms/step - loss: 0.0493 - accuracy: 0.9884 - val_loss: 0.3489 - val_accuracy: 0.9211\n",
            "Epoch 26/300\n",
            " 8/13 [=================>............] - ETA: 0s - loss: 0.0538 - accuracy: 0.9883\n",
            "Epoch 26: val_loss did not improve from 0.16554\n",
            "13/13 [==============================] - 0s 10ms/step - loss: 0.0581 - accuracy: 0.9858 - val_loss: 0.2040 - val_accuracy: 0.9684\n"
          ]
        }
      ],
      "source": [
        "# LSTM model\n",
        "from keras.backend import clear_session\n",
        "filepath = 'har_model_lstm_2021.h5'\n",
        "\n",
        "clear_session()\n",
        "model = Sequential()\n",
        "model.add(Bidirectional(LSTM(200, return_sequences=True,kernel_initializer='orthogonal',\n",
        "                             kernel_regularizer=l2(L2), recurrent_regularizer=l2(L2),\n",
        "                             bias_regularizer=l2(L2),name=\"LSTM_1\"),\n",
        "                             input_shape=(N_TIME_STEPS, N_FEATURES)))\n",
        "\n",
        "model.add(Flatten(name='Flatten'))\n",
        "model.add(Dense(128, activation='relu', kernel_regularizer=l2(L2), bias_regularizer=l2(L2), name=\"Dense_3\"))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Dense(N_CLASSES, activation='softmax', kernel_regularizer=l2(L2), bias_regularizer=l2(L2), name=\"Dense_4\"))\n",
        "\n",
        "\n",
        "model.summary()\n",
        "\n",
        "checkpoint = ModelCheckpoint(filepath=filepath,\n",
        "                             monitor='val_loss',\n",
        "                             verbose=1,\n",
        "                             save_best_only=True)\n",
        "stop = EarlyStopping(monitor='val_loss', patience =5,\n",
        "                      verbose=0, mode='auto', baseline=None, \n",
        "                      restore_best_weights=False)\n",
        "reduce =  ReduceLROnPlateau(monitor='val_loss',\n",
        "                    factor=0.5,\n",
        "                    patience=2,\n",
        "                    verbose=1)\n",
        "callbacks = [checkpoint,stop]\n",
        "\n",
        "model.compile(optimizer=\"adam\",loss='categorical_crossentropy',metrics=['accuracy'])\n",
        "\n",
        "BATCH_SIZE =64\n",
        "N_EPOCHS = 300\n",
        "\n",
        "lstm = model.fit(X_train, y_train,\n",
        "          batch_size=BATCH_SIZE, shuffle=True,epochs=N_EPOCHS,callbacks=callbacks,\n",
        "          validation_data=(X_test, y_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 534
        },
        "id": "nsTDLchI9I0c",
        "outputId": "a4d33f0b-f41d-428c-b9a1-b9e25f8a42f5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
            " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
            " 2 2 2 2 2]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# use LSTM model to predict\n",
        "\n",
        "y_pred_ohe = model.predict(X_test)\n",
        "y_pred_labels = np.argmax(y_pred_ohe, axis=1)\n",
        "y_true_labels = np.argmax(y_test, axis=1)\n",
        "confusion_matrix = metrics.confusion_matrix(y_true=y_true_labels, y_pred=y_pred_labels)\n",
        "print(y_true_labels)\n",
        "LABELS = ['seated', 'stand','walk']\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.set(style='whitegrid', palette='muted', font_scale=1.5)\n",
        "sns.heatmap(confusion_matrix, xticklabels=LABELS, yticklabels=LABELS, annot=True, fmt=\"d\");\n",
        "plt.title(\"Confusion matrix\")\n",
        "plt.ylabel('True label')\n",
        "plt.xlabel('Predicted label')\n",
        "plt.show();"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predIdxs = model.predict(X_test, batch_size=BATCH_SIZE)\n",
        "predIdxs = np.argmax(predIdxs, axis=1)"
      ],
      "metadata": {
        "id": "36L7EIPbLGa0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# show a classification report\n",
        "print(classification_report(y_test.argmax(axis=1), predIdxs,\n",
        "\ttarget_names=['seated', 'stand','walk']))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3wZSziZzKX2A",
        "outputId": "5a298141-4a46-4431-b05d-9a1db5575ec2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "      seated       1.00      1.00      1.00        68\n",
            "       stand       0.98      0.92      0.95        63\n",
            "        walk       0.92      0.98      0.95        59\n",
            "\n",
            "    accuracy                           0.97       190\n",
            "   macro avg       0.97      0.97      0.97       190\n",
            "weighted avg       0.97      0.97      0.97       190\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 619
        },
        "id": "OaCxG9Un9I0c",
        "outputId": "fe629cd1-96a8-49f2-92a9-c622ee2e84a9"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.plot(lstm.history['accuracy'])\n",
        "plt.plot(lstm.history['val_accuracy'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.ylim(0,1)\n",
        "plt.legend(['train', 'val'], loc='best')\n",
        "plt.show()\n",
        "\n",
        "plt.plot(lstm.history['loss'])\n",
        "plt.plot(lstm.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'val'], loc='best')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ez0TA6im9I0d"
      },
      "source": [
        "## Save the model into .pb file"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Save LSTM model\n",
        "\n",
        "output_node_name = \"Dense_2/Softmax\"\n",
        "MODEL_LSTM = str(N_TIME_STEPS)+\"_\"+str(STEP)+\"_\"+str(N_FEATURES)+\"_\"+str(N_CLASSES)+\"_nomagnew\"\n",
        "\n",
        "tf.train.write_graph(K.get_session().graph_def, './models/LSTM/', \\\n",
        "        MODEL_LSTM + '_graph.pbtxt')\n",
        "saver = tf.train.Saver()\n",
        "saver.save(K.get_session(), './models/LSTM/' + MODEL_LSTM + '.chkp')\n",
        "\n",
        "freeze_graph.freeze_graph('./models/LSTM/' +MODEL_LSTM+ '_graph.pbtxt', None, \\\n",
        "    False, './models/LSTM/' + MODEL_LSTM+ '.chkp', output_node_name, \\\n",
        "    \"save/restore_all\", \"save/Const:0\", \\\n",
        "    './models/LSTM/' +MODEL_LSTM + '.pb', True, \"\")"
      ],
      "metadata": {
        "id": "IE2brr6tuFHc"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
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    "language_info": {
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