In this paper we present a system capable of collecting and annotating, human performed, robot understandable, everyday activities from virtual environments. The human movements are mapped in the simulated world using off-the-shelf virtual reality devices with full body, and eye tracking capabilities. All the interactions in the virtual world are physically simulated, thus movements and their effects are closely relatable to the real world. During the activity execution, a subsymbolic data logger is recording the environment and the human gaze on a per-frame basis, enabling offline scene reproduction and replays. Coupled with the physics engine, online monitors (symbolic data loggers) are parsing (using various grammars) and recording events, actions, and their effects in the simulated world.
Multimodal video and IMU kinematic dataset on daily life activities using affordable devices (VIDIMU)
University of Valladolid（巴利亚多利德大学）；University of Applied Sciences and Arts Western Switzerland (HES-SO) Valais-Wallis（瑞士西部应用科学与艺术大学 (HES-SO) 瓦莱-沃利斯分校）；University of Geneva（日内瓦大学）
Towards a Better Understanding Human Reading Comprehension with Brain Signals
Tsinghua University（清华大学）；Institute for Artificial Intelligence（人工智能研究院）；Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）
K2MUSE: A human lower-limb multimodal walking dataset spanning task and acquisition variability for rehabilitation robotics
State Key Laboratory of Robotics and Intelligent Systems, Shenyang Institute of Automation, Chinese Academy of Sciences（机器人与智能系统国家重点实验室，沈阳自动化研究所，中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）；College of Artificial Intelligence, Tianjin Key Laboratory of Intelligent Robotics, Nankai University（人工智能学院，天津智能机器人重点实验室，南开大学）；School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（人工智能与自动化学院，华中科技大学）
