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A Multi-sensor Gait Dataset Collected Under Non-standardized Dual-Task Conditions.
Yuanyuan Liao1, Junjie Cao1, Lisha Yu2
1Sun Yat-sen University, School of Public Health(Shenzhen), Shenzhen, 518107, China.
Scientific Data
|July 1, 2025
Summary
This study introduces the NONSD-Gait dataset, featuring non-standardized dual-task walking data from 23 adults using multiple sensors. The dataset supports gait analysis and sensor comparison for rehabilitation research.
Area of Science:
- Biomechanics
- Human Movement Science
- Rehabilitation Engineering
Background:
- Non-standardized dual-tasks are increasingly important in gait analysis.
- A scarcity of multi-sensor, non-standardized dual-task gait datasets exists.
- This limits research in real-world gait scenarios.
Purpose of the Study:
- To introduce the NONSD-Gait dataset, a novel collection of gait data under non-standardized dual-task conditions.
- To provide a publicly available resource for gait analysis research.
- To facilitate cross-sensor comparisons and explore low-cost sensor alternatives.
Main Methods:
- Collected data from 23 healthy adults performing 7m walks under three non-standardized dual-task conditions (texting, web browsing, holding a cup).
- Utilized simultaneous data acquisition from an optical motion capture (MOCAP) system, a depth camera, and an inertial measurement unit (IMU).
- Extracted 10 spatio-temporal and 168 kinematic gait parameters.
Main Results:
- The dataset comprises comprehensive gait data under realistic dual-task conditions.
- Data includes 3D marker trajectories (MOCAP), 3D joint trajectories (depth camera), and IMU data (acceleration, angular velocity).
- Extracted parameters provide rich information for detailed gait analysis.
Conclusions:
- The NONSD-Gait dataset fills a critical gap in gait analysis research.
- It supports studies on gait under non-standardized dual-tasking, beneficial for cognitive and motor impairment rehabilitation.
- The dataset enables validation of different sensor technologies for gait monitoring.
