ExoNet Database: Wearable Camera Images of Human Locomotion Environments

Recent advances in computer vision and artificial intelligence have allowed researchers to develop environment recognition systems for robotic lower-limb exoskeletons and prostheses. However, inadequate and private training datasets have impeded the widespread development and dissemination of image classification algorithms for environment recognition. To address these shortcomings, we developed “ExoNet” - the first open-source, large-scale hierarchical database of high-resolution wearable camera images of human locomotion environments. Unparalleled in both scale and diversity, ExoNet comprises over 5.6 million images of different indoor and outdoor real-world walking environments, which were collected using a lightweight wearable smartphone camera system throughout the summer, fall, and winter seasons. Approximately 940,000 images in ExoNet were human-annotated using a 12-class hierarchical labelling architecture. Available publicly through the IEEE DataPort repository, ExoNet offers an unprecedented communal platform for training, developing, and comparing image classification algorithms for next-generation environment recognition systems. Beyond the control of lower-limb exoskeletons and prostheses, applications of ExoNet extend to humanoid and autonomous legged robotics.Reference: Laschowski B, McNally W, Wong A, and McPhee J. (2020). ExoNet Database: Wearable Camera Images of Human Locomotion Environments. Under Review.

近年来计算机视觉与人工智能领域的研究进展，使科研人员得以开发面向机器人下肢外骨骼与假肢的环境识别系统。然而，训练数据集不足且多为私有，这阻碍了环境识别图像分类算法的广泛开发与推广应用。为解决上述局限，我们开发了"ExoNet"——首个开源、大规模层级化的高分辨率人类步行环境可穿戴相机图像数据库。该数据库在规模与多样性上均无可比拟，包含超过560万张涵盖不同室内外真实步行环境的图像，采集工作依托轻量化可穿戴智能手机相机系统完成，覆盖夏、秋、冬三季。其中约94万张图像采用12类层级标注架构完成人工标注。"ExoNet"可通过IEEE DataPort知识库公开获取，为下一代环境识别系统的图像分类算法训练、开发与对比提供了前所未有的共享平台。除应用于下肢外骨骼与假肢的控制外，"ExoNet"的应用场景还可拓展至类人机器人与自主腿式机器人领域。参考文献：Laschowski B、McNally W、Wong A与McPhee J.（2020）. ExoNet数据库：人类步行环境可穿戴相机图像. 待刊。