Real-time recognition of walking-related activities is an important function that lower extremity assistive devices should possess. This article presents a real-time walking pattern recognition method for soft knee power assist wear. The recognition method employs the rotation angles of thighs and shanks as well as the knee joint angles collected by the inertial measurement units as input signals and adopts the rule-based classification algorithm to achieve the real-time recognition of three most common walking patterns, that is, level-ground walking, stair ascent, and stair descent. To evaluate the recognition performance, 18 subjects are recruited in the experiments. During the experiments, subjects wear the knee power assist wear and carry out a series of walking activities in an out-of-lab scenario. The results show that the average recognition accuracy of three walking patterns reaches 98.2%, and the average recognition delay of all transitions is slightly less than one step.

中文翻译：

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一种软膝助力穿戴的实时步行模式识别方法

步行相关活动的实时识别是下肢辅助设备应具备的重要功能。本文提出了一种用于软膝助力穿戴的实时步行模式识别方法。该识别方法以惯性测量单元采集的大腿和小腿的旋转角度以及膝关节角度作为输入信号，采用基于规则的分类算法实现对三种最常见的步行模式的实时识别，即：即平地行走、楼梯上升和楼梯下降。为了评估识别性能，在实验中招募了 18 名受试者。在实验过程中，受试者穿着膝部动力辅助装置并在实验室外场景中进行一系列步行活动。