###### 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.  
weight tracking system; musculoskeletal disorders; construction worker's health monitoring; FSR sensor; ML-based classification analysis +
Footnote †: journal: Applied Sciences  
workers simultaneously, it is difficult and inefficient for a site manager to monitor each worker individually and check the weight or number of transports each of them carry [7].  
In this study, in order to effectively solve these problems, we developed smart safety shoes with FSR (Force Sensitive Resistor) fiber sensors attached to the standard shoes always worn by construction site workers. The main purpose of this prototype system is to determine whether a construction worker lifts more than a specific weight. This is to induce construction workers not to carry more than the specified weight and ensure their safety. The suitability of this system was verified through experiments. The verification experiment was carried out on the assumption that the experimenters wearing a smart safety shoe with an FSR sensor and an Arduino Nano attached were carrying heavy objects that would sequentially increase in weight from +0 kg to +20 kg. The sensing information measured through the experiment was collected and stored through the developed system, and further analysis was performed for system verification. The first analysis of the sensing data was performed through scatter plots and a regression analysis to determine the propensity of the data, but due to a sensitive sensing data attribute, the weight estimate was relatively low, at an average accuracy of 46.4%. Accordingly, the secondary analysis was performed with an analysis model developed using valid machine learning (ML) classification algorithms, such as decision tree, random forest, GBM, and light GBM, programmed in Python, all of which were used to track the weight of the heavy objects carried by construction workers. The results show that the estimated weight of the heavy objects carried by construction workers have a relatively high average accuracy of 90.2%, verifying the suitability and applicability of the developed system.