Abstract 

The observation of gait phases provides essential information for controller design and performance evaluation of lower

extremity wearable robots. Specifically, gait events are often defined and detected to distinguish the transition of gait phases.

To achieve this, rule-based gait event detection algorithms detect gait events by utilizing the repetitive features in human

walking with very few sensors and simple logic. Besides, many of these algorithms define gait events as characteristic features

that are detectable from the sensor measurements. However, conventional methods have not fully considered the correlation

between the sensor measurement and characteristics of the human motion. Moreover, these methods were only accurate for a

limited condition of human motion, for example, walking only or running without sensor noise. Therefore, in this paper, we

propose a gait event detection algorithm considering the full kinematic characteristics of the lower extremity under various

gait conditions. The proposed algorithm demonstrates robust performance for both walking and running. Besides, to minimize

the time delay and the false information in the detected gait events, this paper also proposes a robust signal dithering

algorithm that reduces the sensor noise with a limited phase delay. Overall, the performances of the proposed methods are

verified through gait experiments with human subjects.

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