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<p>Non-standardized dual-tasks have recently gained attention in gait analysis. Currently, there is a lack of publicly available non-standardized dual-task gait datasets collected with multiple sensors. To fill this gap, we present a dataset (NONSD-Gait) consisting of back and forth 7 m walks under three non-standardized dual-task conditions (texting, browsing the web, and holding a cup) from 23 healthy adults. These data were collected simultaneously by three common types of sensors: an optical motion capture (MOCAP) system, a depth camera and an inertial measurement unit (IMU). MOCAP captured the 3D trajectories of 22 markers using 8 optical cameras, while the depth camera recorded the 3D trajectories of 25 joints. The IMU was placed on the left ankle to record acceleration and angular velocity data. Moreover, we extracted 10 spatio-temporal gait parameters and 168 kinematic parameters. This dataset enables gait analysis under non-standardized dual-task conditions, supporting research on rehabilitation training for cognitive and motor impairments. Additionally, it facilitates cross-device comparisons, facilitating the exploration of low-cost sensor alternatives.</p>

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A Multi-sensor Gait Dataset Collected Under Non-standardized Dual-Task Conditions

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月1日

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  • Yuanyuan Liao,
  • Junjie Cao,
  • Lisha Yu,
  • Jianbang Xiang,
  • Yang Zhao

摘要

Non-standardized dual-tasks have recently gained attention in gait analysis. Currently, there is a lack of publicly available non-standardized dual-task gait datasets collected with multiple sensors. To fill this gap, we present a dataset (NONSD-Gait) consisting of back and forth 7 m walks under three non-standardized dual-task conditions (texting, browsing the web, and holding a cup) from 23 healthy adults. These data were collected simultaneously by three common types of sensors: an optical motion capture (MOCAP) system, a depth camera and an inertial measurement unit (IMU). MOCAP captured the 3D trajectories of 22 markers using 8 optical cameras, while the depth camera recorded the 3D trajectories of 25 joints. The IMU was placed on the left ankle to record acceleration and angular velocity data. Moreover, we extracted 10 spatio-temporal gait parameters and 168 kinematic parameters. This dataset enables gait analysis under non-standardized dual-task conditions, supporting research on rehabilitation training for cognitive and motor impairments. Additionally, it facilitates cross-device comparisons, facilitating the exploration of low-cost sensor alternatives.