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 lower-limb exoskeletons and prostheses. However, insufficient 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 dataset 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. Approximately 940,000 images in ExoNet were human-annotated using a 12-class hierarchical classification architecture. Available publicly through IEEE DataPort, 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.