Accurate gait event detection is necessary for control strategies of gait rehabilitation robots. However, due to personal diversity between individuals, it is a challenge for robots to detect a gait event at various stride frequencies. This paper proposes a novel method for gait event detection of a gait rehabilitation robot using a single inertial sensor mounted on the thigh. A self-adaptive threshold for detecting heel strike is obtained in real time via a linear regression model. Observable thresholds for toe off detection are constant at various stride frequencies. Experiments are conducted based on 20 healthy subjects and six hemiplegic patients wearing a gait rehabilitation robot and walking at various kinds of stride frequencies. The experimental results show that the proposed method can detect heel strike and toe off gait events within an average 2% gait cycle temporal errors and never miss two-gait event detection. Compared to the conventional thresholding method, this work presents a simple and robust application for gait event detection in healthy and hemiplegic subjects by one inertial sensor. The linear regression model can be applicable to different subjects walking at various stride frequencies.

中文翻译：

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一种用于步态康复机器人步态事件检测的自适应方法。

准确的步态事件检测对于步态康复机器人的控制策略是必要的。但是，由于个人之间的个体差异，机器人要检测各种步幅频率的步态事件是一个挑战。本文提出了一种使用安装在大腿上的单个惯性传感器检测步态康复机器人步态事件的新方法。通过线性回归模型实时获得用于检测脚跟撞击的自适应阈值。在不同的步幅频率下，可观察到的脚趾偏离阈值是恒定的。实验是针对20名健康受试者和6名偏瘫患者佩戴步态康复机器人并以各种步幅走动进行的。实验结果表明，该方法能够在平均2％的步态周期时间误差内检测到脚后跟撞击和脚趾跳动事件，并且不会遗漏两步态事件检测。与常规阈值方法相比，这项工作为通过一个惯性传感器在健康和偏瘫受试者中进行步态事件检测提供了一种简单而强大的应用。线性回归模型可以适用于以不同步幅频率行走的不同对象。