Evaluation of a Visual Localization System for Environment Awareness in Assistive Devices

A major hurdle for the widespread use of wearable assistive devices is determining, moment-by-moment, the control mode appropriate for a given terrain when faced with a complex, multi-terrain environment. Current control strategies focus mainly on measurements of user behavior and less on environment information. Here we demonstrate the application of location estimates from a vision-based localization system to obtain environment awareness by delineating various terrains into regions. Given the current location and region occupied by the user, a controller could be built to select appropriate modes, predict transitions, or to add error correction. We quantify the positional accuracy of location estimates, how well these estimates translate to classifying current region, and transitions. Performance was evaluated on eight participants without amputation wearing the sensor on the shank of the leg. We investigated the performance of an instantaneous region classifier, which used location estimates alone, and a time-history based region classifier, which used a Neural Network on a time history of location and height estimates to accomplish environment awareness. Four types of regions and six types of transitions were tested. The classifier using height estimates and time history provided accurate region labels at least 96% of the time, and accurately detected region transitions within 110 milliseconds. These results demonstrate the promise of localization for control of robotic assistive technology.