Environmental Context Prediction for Lower Limb Prostheses With Uncertainty Quantification

Abstract

Reliable environmental context prediction is critical for wearable robots (e.g., prostheses and exoskeletons) to assist terrain-adaptive locomotion. This article proposed a novel vision-based context prediction framework for lower limb prostheses to simultaneously predict human’s environmental context for multiple forecast windows. By leveraging the Bayesian neural networks (BNNs), our framework can quantify the uncertainty caused by different factors (e.g., observation noise, and insufficient or biased training) and produce a calibrated predicted probability for online decision-making. We compared two wearable camera locations (a pair of glasses and a lower limb device), independently and conjointly. We utilized the calibrated predicted probability for online decision-making and fusion. We demonstrated how to interpret deep neural networks with uncertainty measures and how to improve the algorithms based on the uncertainty analysis. The inference time of our framework on a portable embedded system was less than 80 ms/frame. The results in this study may lead to novel context recognition strategies in reliable decision-making, efficient sensor fusion, and improved intelligent system design in various applications. Note to Practitioners—This article was motivated by two practical problems in computer vision for wearable robots: First, the performance of deep neural networks is challenged by real-life disturbances. However, reliable confidence estimation is usually unavailable and the factors causing failures are hard to identify. Second, evaluating wearable robots by intuitive trial and error is expensive due to the need for human experiments. Our framework produces a calibrated predicted probability as well as three uncertainty measures. The calibrated probability makes it easy to customize prediction decision criteria by considering how much the corresponding application can tolerate error. This study demonstrated a practical procedure to interpret and improve the performance of deep neural networks with uncertainty quantification. We anticipate that our methodology could be extended to other applications as a general scientific and efficient procedure of evaluating and improving intelligent systems.