---
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:

---
license: apache-2.0
---