Learning-Aided User Intent Estimation for Smart Rollators

Abstract

With the aging population and rising rates of mobility disability, the demand for advanced smart rollators is increasing. To design control systems which improve safety and reliability, accurate prediction of human intent is required. In this paper, we present a classification method to predict intent of the rollator user using indirect inputs. The proposed classification algorithm uses data collected from an inertial measurement unit and an encoder implemented into a rollator. The developed intent estimation method is experimentally verified on our modified robotic platform. For our experiment with 7 healthy young adults, KNN classification algorithm was able to predict 3 intents (turn left, turn right and walk straight) with 92.9 % accuracy.