Yeaser Abdullah, Tung James, Huissoon Jan, Hashemi Ehsan
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:3178-3183. doi: 10.1109/EMBC44109.2020.9175610.
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.
随着人口老龄化和行动不便率的上升，对先进智能助行器的需求正在增加。为了设计提高安全性和可靠性的控制系统，需要准确预测人类意图。在本文中，我们提出了一种使用间接输入来预测助行器使用者意图的分类方法。所提出的分类算法使用从惯性测量单元和集成在助行器中的编码器收集的数据。所开发的意图估计方法在我们改进的机器人平台上进行了实验验证。对于我们对7名健康年轻人进行的实验，KNN分类算法能够以92.9%的准确率预测3种意图（左转、右转和直行）。
