Honda Research Institute Europe GmbH, Offenbach, 63073, Germany.
Sci Data. 2022 Aug 3;9(1):473. doi: 10.1038/s41597-022-01580-3.
Human gait data have traditionally been recorded in controlled laboratory environments focusing on single aspects in isolation. In contrast, the database presented here provides recordings of everyday walk scenarios in a natural urban environment, including synchronized IMU-, FSR-, and gaze data. Twenty healthy participants (five females, fifteen males, between 18 and 69 years old, 178.5 ± 7.64 cm, 72.9 ± 8.7 kg) wore a full-body Lycra suit with 17 IMU sensors, insoles with eight pressure sensing cells per foot, and a mobile eye tracker. They completed three different walk courses, where each trial consisted of several minutes of walking, including a variety of common elements such as ramps, stairs, and pavements. The data is annotated in detail to enable machine-learning-based analysis and prediction. We anticipate the data set to provide a foundation for research that considers natural everyday walk scenarios with transitional motions and the interaction between gait and gaze during walking.
人体步态数据传统上是在专注于单一方面的受控实验室环境中记录的。相比之下，这里提供的数据库记录了在自然城市环境中的日常行走场景，包括同步的 IMU、FSR 和注视数据。二十名健康参与者（五名女性，十五名男性，年龄在 18 至 69 岁之间，身高 178.5±7.64cm，体重 72.9±8.7kg）穿着全身莱卡套装，配有 17 个 IMU 传感器、每只脚有八个压力感应单元的鞋垫和一个移动眼动追踪器。他们完成了三个不同的行走课程，每个试验都包括几分钟的行走，包括各种常见的元素，如斜坡、楼梯和人行道。这些数据经过详细注释，以实现基于机器学习的分析和预测。我们预计该数据集将为研究提供基础，这些研究考虑了具有过渡运动的自然日常行走场景以及行走过程中步态和注视之间的相互作用。
