A Model for Predicting Construction Worker Fatigue

Updata:01-01-1970

Source:
By:Ahmed Senouci, Surya Anuradha Garimella, Kyungki Kim, Neil Eldin

DOI: https://doi.org/10.30564/jsbct.v4i2.4628

Abstract:
Fatigue impairs workers’ judgment, reduces their productivity, and jeop
ardizes their safety. The paper presents a tool to predict workers’ fatigue
based on their vital signs. An experimental study was conducted in which
the heart rate and sleep quality for three individuals were monitored using
fitness trackers (wearable sensors). The data collected were used to develop
two models based on regression analysis and Artificial Neural Networks
(ANN), to predict their fatigue level. A Borg’s scale was used to estimate
the Rating of Perceived Exertion (RPE) of the participants. The two models
were able to satisfactorily predict the RPE (workers fatigue level) with an
average validity of 75% and 80% for the regression ANN models, respec
tively. The developed models can provide project managers and superinten
dents with early warning to avoid potential worker overexertion, injuries,
and fatalities.
References:

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