ExoNet: Large-Scale Hierarchical Image Database of Human Locomotion Environments

Recent advances in robotic vision and artificial intelligence have enabled researchers to develop environment recognition systems for lower-limb exoskeletons and prostheses. However, insufficient and private training databases have impeded the widespread development and dissemination of image classification algorithms for environment recognition. To address these shortcomings, we have 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, collected using a lightweight wearable smartphone camera system throughout summer, autumn, and winter seasons. Approximately 940,000 images in ExoNet were human-annotated using a 12-classs 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 robotic lower-limb exoskeletons and prostheses, applications of ExoNet extend to other assistive technologies (e.g., powered wheelchairs) and humanoid and autonomous legged robotics.Reference: Laschowski B, McNally W, Wong A, and McPhee J. (2020). ExoNet: Large-Scale Hierarchical Image Database of Human Locomotion Environments. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. In Preparation.