A multi-camera and multimodal dataset for posture and gait analysis

Gait and posture analysis while using assisting robotic devices is of utmost importance to attain effective assistance. This work provides a multi-camera, multimodal, and detailed dataset for vision-based applications using a wheeled robotic walker equipped with a pair of affordable cameras. Depth data was acquired at 30 fps from a total of 14 healthy participants walking at 3 different gait speeds, across 3 different walking scenarios/paths at 3 different locations. Simultaneously, accurate skeleton joint data was recorded using an inertial-based commercial motion capture system that provides a reliable ground-truth for classical or novel (i.e., machine learning-based) vision-based applications. In total, the database contains approximately 166K frames of synchronized data, which amounts to 92 minutes of total recording time. This dataset may contribute to the development and evaluation of: i) classic or data-driven vision-based pose estimation algorithms; ii) applications in human detection and tracking, and movement forecasting; iii) and gait/posture metrics analysis using a rehabilitation device.

在使用辅助机器人装置时开展步态（gait）与姿态（posture）分析，对于实现有效的辅助效果至关重要。本工作提供了一套多摄像头（multi-camera）、多模态（multimodal）且细节丰富的数据集，适用于基于视觉的应用（vision-based applications），其采集设备为搭载一对平价摄像头的轮式助行机器人（wheeled robotic walker）。
本研究共招募14名健康受试者（healthy participants），让其分别以3种不同步态速度，在3个不同地点、沿3种不同行走场景/路径行进，期间以30帧每秒（fps）的帧率采集深度数据。与此同时，研究还通过一套基于惯性的（inertial-based）商用运动捕捉系统（motion capture system）同步采集精确的骨骼关节数据，该系统可为传统或新型（即基于机器学习的（machine learning-based））基于视觉的应用提供可靠的基准真值（ground-truth）。
该数据集总计包含约16.6万帧同步数据，总录制时长共计92分钟。
本数据集可助力以下方向的开发与评估：i）传统或数据驱动的（data-driven）基于视觉的姿态估计（pose estimation）算法；ii）人体检测与跟踪（human detection and tracking）、运动预测（movement forecasting）相关应用；iii）借助康复设备（rehabilitation device）开展的步态/姿态指标分析。