doc and eaxmple

README.md

@@ -38,6 +38,57 @@ This dataset is shared under the **apache-2.0** license, allowing use and modifi
 ## Citation
 If you use this dataset in your research, please cite it as follows:
 
+
+## How to Use the Dataset
+
+To get started with the **Fall Prediction Dataset for Humanoid Robots**, follow the steps below:
+
+### 1. Set Up a Virtual Environment
+
+It's recommended to create a virtual environment to isolate dependencies. You can do this with the following command:
+
+```bash
+python -m venv .venv
+```
+
+After creating the virtual environment, activate it:
+
+- On **Windows**:
+  ```bash
+  .venv\Scripts\activate
+  ```
+
+- On **macOS/Linux**:
+  ```bash
+  source .venv/bin/activate
+  ```
+
+### 2. Install Dependencies
+
+Once the virtual environment is active, install the necessary packages by running:
+
+```bash
+pip install -r requirements.txt
+```
+
+### 3. Run the Example Script
+
+To load and use the dataset for training a simple LSTM model, run the `usage_example.py` script:
+
+```bash
+python usage_example.py
+```
+
+This script demonstrates how to:
+- Load the dataset
+- Select the relevant sensor columns
+- Split the data into training and test sets
+- Train a basic LSTM model to predict falls
+- Evaluate the model on the test set
+
+Make sure to check the script and adjust the dataset paths if necessary. For further details, see the comments within the script.
+
+
 ---
 license: apache-2.0
 ---

requirements.txt

@@ -0,0 +1,3 @@
+pandas
+tensorflow
+scikit-learn

usage_example.py

@@ -0,0 +1,54 @@
+# Usage Example for the Fall Prediction Dataset
+# Please install dependencies before:
+# pip install -r requirements.txt
+
+# Import necessary libraries
+import pandas as pd
+import tensorflow as tf
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense, LSTM, Dropout
+from sklearn.model_selection import train_test_split
+
+# Load the dataset from Huggingface or a local file path
+# Example for local loading; replace with Huggingface dataset call if applicable
+real_data = pd.read_csv('dataset.csv.bz2', compression='bz2')
+
+# Preview the dataset
+print(real_data.head())
+
+# Select relevant columns (replace these with actual column names from your dataset)
+# Here we assume that the dataset contains sensor readings like gyroscope and accelerometer data
+relevant_columns = ['gyro_x', 'gyro_y', 'gyro_z', 'acc_x', 'acc_y', 'acc_z', 'upright']
+sensordata = real_data[relevant_columns]
+
+# Split the data into features (X) and labels (y)
+# 'fall_label' is assumed to be the column indicating whether a fall occurred
+X = sensordata.drop(columns=['upright'])  # Replace 'fall_label' with the actual label column
+y = sensordata['upright']
+
+# Split data into training and test sets
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Reshape data for LSTM input (assuming time-series data)
+# Adjust the reshaping based on your dataset structure
+X_train = X_train.values.reshape(X_train.shape[0], X_train.shape[1], 1)
+X_test = X_test.values.reshape(X_test.shape[0], X_test.shape[1], 1)
+
+# Define a simple LSTM model
+model = Sequential()
+model.add(LSTM(64, input_shape=(X_train.shape[1], 1)))
+model.add(Dropout(0.2))
+model.add(Dense(1, activation='sigmoid'))
+
+# Compile the model
+model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
+
+# Train the model
+history = model.fit(X_train, y_train, epochs=10, batch_size=64, validation_data=(X_test, y_test))
+
+# Evaluate the model on the test set
+loss, accuracy = model.evaluate(X_test, y_test)
+print(f"Test Accuracy: {accuracy * 100:.2f}%")
+
+# You can save the model if needed
+# model.save('fall_prediction_model.h5')