Human Gait Recognition Based on SOM-K-Means Clustering for Lunar Landing Spacesuit

Abstract

Whether the lunar landing spacesuit can accurately recognize the astronaut’s gait and provide timely buffering upon lower limb impact is a critical factor in ensuring the safety of extravehicular operations. This study addresses this issue by segmenting the human gait phases and collecting gait data in a simulated lunar surface low-gravity environment. Z-score normalization and Gaussian filtering are applied for data preprocessing. The self-organizing map (SOM)-K-means clustering algorithm is adopted to establish the gait recognition model, and the model is then evaluated. Experimental results and comparisons with other clustering algorithms validate the effectiveness of the SOM-K-means algorithm for gait recognition and the generalizability of the model. The results demonstrate that the model achieves satisfactory clustering performance and accurate gait recognition, providing technical support for the design of high-performance lunar landing spacesuits.

Keywords

lunar landing spacesuit / gait recognition / self-organizing map (SOM) / K-means clustering

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