# HuGaDB: Human Gait Database for Activity Recognition from Wearable Inertial Sensor Networks

arXiv: https://arxiv.org/abs/1705.08506 · HTML: https://mlsanity.com/paper/1705.08506/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 May 2017.

## Built on by (most cited first, 16 of 16)

- [Motion Question Answering via Modular Motion Programs](https://mlsanity.com/paper/2305.08953/) (2023)
- [Skeleton-Based Action Segmentation with Multi-Stage Spatial-Temporal Graph Convolutional Neural Networks](https://mlsanity.com/paper/2202.01727/) (2022)
- [CLIMB: Data Foundations for Large Scale Multimodal Clinical Foundation Models](https://mlsanity.com/paper/2503.07667/) (2025)
- [Human Activity Recognition Using Tools of Convolutional Neural Networks: A State of the Art Review, Data Sets, Challenges and Future Prospects](https://mlsanity.com/paper/2202.03274/) (2022)
- [Skeleton Motion Words for Unsupervised Skeleton-Based Temporal Action Segmentation](https://mlsanity.com/paper/2508.04513/) (2025)
- [Human Body Pose Estimation for Gait Identification: A Comprehensive Survey of Datasets and Models](https://mlsanity.com/paper/2305.13765/) (2023)
- [CurvFed: Curvature-Aligned Federated Learning for Fairness without Demographics](https://mlsanity.com/paper/2404.19725/) (2024)
- [Feature Fusion for Human Activity Recognition using Parameter-Optimized Multi-Stage Graph Convolutional Network and Transformer Models](https://mlsanity.com/paper/2406.16638/) (2024)
- [A study of animal action segmentation algorithms across supervised, unsupervised, and semi-supervised learning paradigms](https://mlsanity.com/paper/2407.16727/) (2024)
- [Machine Learning in Biomechanics: Key Applications and Limitations in Walking, Running, and Sports Movements](https://mlsanity.com/paper/2503.03717/) (2025)
- [WHAR Datasets: An Open Source Library for Wearable Human Activity Recognition](https://mlsanity.com/paper/2508.16604/) (2025)
- [Self-supervised Extraction of Human Motion Structures via Frame-wise Discrete Features](https://mlsanity.com/paper/2309.05972/) (2023)
- [Integrating Features for Recognizing Human Activities through Optimized Parameters in Graph Convolutional Networks and Transformer Architectures](https://mlsanity.com/paper/2408.16442/) (2024)
- [Towards Green Wearable Computing: A Physics-Aware Spiking Neural Network for Energy-Efficient IMU-based Human Activity Recognition](https://mlsanity.com/paper/2604.10458/) (2026)
- [Unsupervised Skeleton-Based Action Segmentation via Hierarchical Spatiotemporal Vector Quantization](https://mlsanity.com/paper/2604.15196/) (2026)
- [WHAR Arena: Benchmarking the State of the Art in Efficient Wearable Human Activity Recognition](https://mlsanity.com/paper/2606.13194/) (2026)

## Abstract (arXiv)

This paper presents a human gait data collection for analysis and activity recognition consisting of continues recordings of combined activities, such as walking, running, taking stairs up and down, sitting down, and so on; and the data recorded are segmented and annotated. Data were collected from a body sensor network consisting of six wearable inertial sensors (accelerometer and gyroscope) located on the right and left thighs, shins, and feet. Additionally, two electromyography sensors were used on the quadriceps (front thigh) to measure muscle activity. This database can be used not only for activity recognition but also for studying how activities are performed and how the parts of the legs move relative to each other. Therefore, the data can be used (a) to perform health-care-related studies, such as in walking rehabilitation or Parkinson's disease recognition, (b) in virtual reality and gaming for simulating humanoid motion, or (c) for humanoid robotics to model humanoid walking. This dataset is the first of its kind which provides data about human gait in great detail. The database is available free of charge https://github.com/romanchereshnev/HuGaDB.