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Whether the lunar landing spacesuit can accurately recognize the astronautn lower limb impa's gait and provide timely buffering upoct 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 lowgravity environment. Z-score normalization and Gaussian filtering are applied for data preprocessing. The selforganizing map(SOM)-K-means clustering algorithm is adopted to establish the gait recognition modeluated. Experimental re, and the model is then evalsults 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 recognitionproviding technical support for the design of high,-performance lunar landing spacesuits.
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Basic Information:
DOI：10.19884/j.1672-5220.202501008
China Classification Code:V445.3;TP311.13
Citation Information:
[1]HAO Boda,LÜ Hongzhan.Human Gait Recognition Based on SOM-K-Means Clustering for Lunar Landing Spacesuit[J].Journal of Donghua University (English Edition)().DOI:10.19884/j.1672-5220.202501008.
Fund Information:
Natural Science Foundation of Shanghai “Science and Technology Innovation Action Plan”,China.(No. 20ZR1401300)
2026-08-31
2026-08-31
2026-08-31