A First-Person Optical Flow Video Dataset for the 3-Meter Timed Up and Go (TUG) Test

This dataset aims to support research on temporal segmentation of the Timed Up and Go (TUG) test using a first-person wearable camera. The data collection includes a training set of 8 participants and a test set of 60 participants. Among the 8 participants, the test was completed at both a normal walking pace and a simulated slower walking pace to mimic elderly movement patterns. The 60 participants were randomly divided into two groups: one group completed the test at a normal walking pace, and the other group simulated slower walking speed to mimic elderly movement patterns. Video data were captured using the Realtek AMB82-mini AI Camera and preprocessed using the Farneback algorithm to extract optical flow features for subsequent analysis.Each participant's dataset includes motion feature points and segments the TUG test into six distinct phases: standing up, walking (outbound), turning (outbound), walking (return), turning (return), and sitting down. This dataset can be used for the development and validation of machine learning models for six-phase classification, feature extraction algorithms, and signal analysis techniques.The dataset is fully anonymized and has been ethically approved by the Human Research Ethics Committee of National Chung Cheng University.

本数据集旨在支持基于第一人称可穿戴相机的定时起身行走测试（Timed Up and Go, TUG）时间分割研究。数据采集包含8名参与者的训练集和60名参与者的测试集。在8名参与者中，测试以正常步速和模拟慢速（模仿老年人运动模式）两种方式完成。60名参与者被随机分为两组：一组以正常步速完成测试，另一组模拟慢速行走以模仿老年人运动模式。视频数据通过Realtek AMB82-mini AI相机采集，并使用Farneback算法预处理以提取光流特征用于后续分析。每个参与者的数据集包含运动特征点，并将TUG测试划分为六个不同阶段：站立起身、外出行走、外出转身、返回行走、返回转身及坐下。本数据集可用于六阶段分类机器学习模型、特征提取算法及信号分析技术的开发与验证。该数据集已完全匿名，并获得国立中正大学人类研究伦理委员会的伦理批准。