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摄像头的计时起身行走（Timed Up and Go, TUG）测试时序分割相关研究。数据采集包含8名参与者的训练集与60名参与者的测试集。其中8名参与者分别以正常步行速度与模拟老年运动模式的慢速步行速度完成测试。60名参与者被随机分为两组：一组以正常步行速度完成测试，另一组以模拟慢速步行速度复刻老年运动模式。视频数据采用瑞昱（Realtek）AMB82-mini AI摄像头采集，并通过法尔内克（Farneback）算法进行预处理以提取光流特征，用于后续分析。每位参与者的数据集包含运动特征点，并将TUG测试划分为六个明确阶段：起身、出发行走、出发转向、返回行走、返回转向、就座。本数据集可用于六阶段分类机器学习模型、特征提取算法以及信号分析技术的开发与验证。本数据集已完成完全匿名化处理，并通过国立中正大学人类研究伦理委员会的伦理审查。