Development of a Safety Management System Tracking the Weight of Heavy Objects Carried by Construction Workers Using FSR Sensors

Seung Hyun Lee,Jaeho Son

doi:10.3390/app11041378

Abstract

It has been pointed out that the act of carrying a heavy object that exceeds a certain weight by a worker at a construction site is a major factor that puts physical burden on the worker’s musculoskeletal system. However, due to the nature of the construction site, where there are a large number of workers simultaneously working in an irregular space, it is difficult to figure out the weight of the object carried by the worker in real time or keep track of the worker who carries the excess weight. This paper proposes a prototype system to track the weight of heavy objects carried by construction workers by developing smart safety shoes with FSR (Force Sensitive Resistor) sensors. The system consists of smart safety shoes with sensors attached, a mobile device for collecting initial sensing data, and a web-based server computer for storing, preprocessing and analyzing such data. The effectiveness and accuracy of the weight tracking system was verified through the experiments where a weight was lifted by each experimenter from +0 kg to +20 kg in 5 kg increments. The results of the experiment were analyzed by a newly developed machine learning based model, which adopts effective classification algorithms such as decision tree, random forest, gradient boosting algorithm (GBM), and light GBM. The average accuracy classifying the weight by each classification algorithm showed similar, but high accuracy in the following order: random forest (90.9%), light GBM (90.5%), decision tree (90.3%), and GBM (89%). Overall, the proposed weight tracking system has a significant 90.2% average accuracy in classifying how much weight each experimenter carries.

Highlights

We developed smart safety shoes with a conductive fiber sensor (FSR sensor) that can track pressure strength
It has been verified through several experiments whether it is possible to track the weight of a heavy object in order to prevent excess burden on the musculoskeletal system when a construction worker lifts a heavy object on a construction job site
We developed a prototype weight tracking system consisting of smart safety shoes with FSR sensors, a mobile application for primary data collection, and a Machine Learning (ML)-based classification model based on a server computer for data storage and analysis

Summary

Introduction

Musculoskeletal disorders are health disorders that occur due to factors such as improper posture, repetitive movements, or excessive use of force. According to Ode Hengel’s report, more than half of the workers in the construction industry are exposed to musculoskeletal disorders, which leads to a reduction in the workers’ capacity and willingness to maximize productivity [4]. When the Korea Occupational Safety and Health Agency survey evaluated the exposure to risk factors in the construction industry, the results revealed that the work of handling heavy objects was the biggest risk factor. The process of lifting or transporting heavy objects accounted for 62.1% of the risk exposure [5]

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