ExoNet Database: Wearable Camera Images of Human Locomotion Environments

Advances in artificial intelligence and robotic vision have enabled 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. To address these shortcomings, we have developed the ExoNet Database, the first open-source database of high-resolution wearable RGB camera images of human locomotion environments. Using a lightweight wearable smartphone camera system, over 5.6 million images of outdoor and indoor real-world walking environments were collected throughout the summer, autumn, and winter seasons. Approximately 940,000 images of the ExoNet Database were human-annotated using a 12-classs hierarchical image classification architecture. Images were uploaded to IEEE DataPort and are publicly available for download. The ExoNet Database provides an unprecedented community platform for training, developing, and comparing next-generation image classification algorithms (e.g., convolutional neural networks) for control of lower-limb exoskeletons and prostheses.