ExoNet Database: Wearable Camera Images of Human Locomotion Environments

Abstract: Recent advances in computer vision and artificial intelligence are allowing researchers to develop environment recognition systems for powered lower-limb exoskeletons and prostheses. However, small-scale and private training datasets have impeded the widespread development and dissemination of image classification algorithms for classifying human walking environments. 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 scale and diversity, ExoNet includes over 5.6 million images of different indoor and outdoor real-world walking environments, which were collected using a lightweight wearable smartphone camera throughout the summer, fall, and winter seasons. Approximately 923,000 images in ExoNet were human-annotated using a 12-class hierarchical labelling architecture. Available publicly through IEEE DataPort, ExoNet offers an unprecedented communal platform to train, develop, and compare next-generation image classification algorithms for human locomotion environment recognition. Beyond the control of powered lower-limb exoskeletons and prostheses, prospective 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. Frontiers in Robotics and Artificial Intelligence. Under Review.

摘要：近年来计算机视觉与人工智能领域的最新进展，推动研究人员开发出面向动力式下肢外骨骼与义肢的环境识别系统。然而，规模有限且仅面向内部使用的训练数据集，阻碍了用于识别人类行走运动环境的图像分类算法的大规模开发与推广应用。为弥补这一短板，我们构建了ExoNet——首个开源、大规模层级化的人类行走运动环境可穿戴相机高清图像数据库。该数据集在规模与多样性上均无与伦比，包含超过560万张涵盖真实室内外各类行走环境的图像，这些图像通过轻量化可穿戴智能手机相机在夏、秋、冬三季采集完成。其中约92.3万张图像采用12类层级标注架构完成人工标注。ExoNet通过IEEE数据港（IEEE DataPort）公开上线，为训练、开发以及对比用于人类行走运动环境识别的下一代图像分类算法提供了前所未有的共享研究平台。除应用于动力式下肢外骨骼与义肢的控制外，ExoNet的潜在应用场景还可拓展至类人机器人与自主腿部机器人领域。参考文献：Laschowski B, McNally W, Wong A 及 McPhee J. (2020). ExoNet数据库：人类行走运动环境可穿戴相机图像. 《机器人学与人工智能前沿（Frontiers in Robotics and Artificial Intelligence）》, 待审。