text stringclasses 4 values
tqdm
pandas
tensorflow
scikit-learn

Fall Prediction Dataset for Humanoid Robots

Dataset Summary

This dataset consists of 37.9 hours of real-world sensor data collected from 20 Nao humanoid robots over the course of one year in various test environments, including RoboCup soccer matches. The dataset includes 18.3 hours of walking data, featuring 2519 falls. It captures a wide range of activities such as omni-directional walking, collisions, standing up, and falls on various surfaces like artificial turf and carpets.

The dataset is primarily designed to support the development and evaluation of fall prediction algorithms for humanoid robots. It includes data from multiple sensors, such as gyroscopes, accelerometers, and force-sensing resistors (FSR), recorded at a high frequency to track robot movements and falls with precision.

Using this dataset, the RePro-TCN model was developed, which outperforms existing fall prediction methods under real-world conditions. This model leverages temporal convolutional networks (TCNs) and incorporates advanced training techniques like progressive forecasting and relaxed loss formulations.

Dataset Structure

Duration: 37.9 hours total, 18.3 hours of walking
Falls: 2519 falls during walking scenarios
Data Types: Gyroscope (roll, pitch), accelerometer (x, y, z), body angle, and force-sensing resistors (FSR) per foot.

Use Cases

Humanoid robot fall prediction and prevention
Robot control algorithm benchmarking
Temporal sequence modeling in robotics

Licensing

This dataset is shared under the apache-2.0 license, allowing use and modification with proper attribution, as long as derivatives are shared alike.

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:

0. Clone the repository

Please make sure that you have installed git large file support (git-lfs) before cloning this repository.

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:

python -m venv .venv

After creating the virtual environment, activate it:

On Windows:
```
.venv\Scripts\activate
```
On macOS/Linux:
```
source .venv/bin/activate
```

2. Install Dependencies

Once the virtual environment is active, install the necessary packages by running:

pip install -r requirements.txt

If you have trouble downloading the requirements, check your internet connection. Alternatively, try increasing the pip timeout or upgrading your pip installation:

# Increase the timeout by 120 seconds
pip install --default-timeout=120 -r requirements.txt
# or upgrade pip
python -m pip install --upgrade pip

3. Run the Example Script

To load and use the plain csv dataset for training a simple LSTM model, run the plain_dataset_usage_example.py script (RAM utilisation exceeds 16 GB):

python plain_dataset_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

To load and use a already prepared dataset, with reduced RAM utilisation, for training a simple LSTM model, run the lightweight_dataset_usage_example.py script (RAM utilisation less than 2 GB):

python lightweight_dataset_usage_example.py