Instability detection method for construction workers working at altitude based on Gaussian mixture model

doi:10.16265/j.cnki.issn1003-3033.2023.04.0697

Abstract

Abstract:

In order to prevent the construction site high fall accident and achieve personalized correction management, based on considering the differentiation of motion signals caused by individual heterogeneity, a real-time detection method based on GMM was proposed, which can timely identify the instability state of construction workers working at height. This method used posture sensors to collect real-time acceleration and angular velocity data to describe the posture features of construction workers working at height. Based on GMM, it established a personalized instability detection method to obtain personalized thresholds for judging the instability state of construction workers working at heights. Finally, it compared two models constructed by individual and public data sets through experiments. The results show that the personalized detection model generated is far superior to the public data set model in accuracy (P), recall rate(R) and comprehensive evaluation value (F₁). It shows the better-personalized detection effect using the personalized detection model. This study can help identify personalized risks of instability working at height from workers' habitual working postures, provide new ideas and references for preventing falling accidents, and help realize personalized correction training for workers.

Key words: gaussian mixture model(GMM), construction worker, work at height, instability detection, real-time detection

FAN Wenhan, LIN Xinyan, ZUO Chao, XU Xiaoyuan, ZHOU Jianliang. Instability detection method for construction workers working at altitude based on Gaussian mixture model[J]. China Safety Science Journal, 2023, 33(4): 114-120.

Figures/Tables 5

References 17

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