---
title: "Fall Prediction Dataset for Humanoid Robots"
datasets:
- naos-fall-prediction
tags:
- humanoid-robotics
- fall-prediction
- machine-learning
- sensor-data
- robotics
- temporal-convolutional-networks
license: 
- apache-2.0
---

# 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:

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

```bash
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):

```bash
python lightweight_dataset_usage_example.py
```

This script demonstrates how to:
- Convert the csv dataset into a memory mapped file
- Load the memory mapped version of the dataset
- Train a basic LSTM model to predict falls
- Evaluate the model on the test set

The script `convert_and_load_dataset.py` used by the lightweight example demonstrates how to:
- Select the relevant sensor columns
- Split the data into training and test sets

Make sure to check the scripts and adjust the dataset paths if necessary. For further details, see the comments and docstrings within the scripts.


---
license: apache-2.0
---