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Detecting Near-Miss Actions and Estimating Physical Fatigue among Construction Workers Using Wearable Sensors

Labor shortages in the construction industry have become a serious issue in developed countries, particularly in Japan, where workforce aging and declining recruitment of young workers are significant challenges. In this context, ensuring worker safety has become increasingly critical. While occupational accidents in Japan's construction industry have decreased annually due to proper safety measures, the construction industry still has the highest number of fatalities among all industries. Falls from height and falls on the same level are the leading causes of injuries and fatalities. Therefore, detecting near-miss incidents (such as tripping and slipping) that precede falls, along with physical fatigue, could help prevent occupational accidents. This study investigated the feasibility of detecting near-miss incidents and estimating fatigue levels using wearable sensors suitable for continuous monitoring at construction sites. We conducted validation experiments simulating near-miss actions and fatigue conditions. Results showed that applying a Convolutional Neural Network (CNN) to data collected from an iPhone® placed in workers' trouser pockets achieved an F1-score of 0.95 in detecting near-miss actions. Additionally, by comparing body sway magnitudes before and after fatigue, we confirmed the potential for estimating physical fatigue.

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Article Interdisciplinary Sciences

Real-Time Physiological Monitoring for Management of Normobaric Hypoxic Training Toward Wearable System Implementation

As a method to prevent lifestyle diseases, normobaric hypoxic training has been attracting attention. However, its exercise load and safety in non-athletes remain unclear. In this study, 20 healthy university students underwent a 15-min exercise test in a normobaric hypoxic room set at two different oxygen concentrations (O2: 20% and 16%), and the exercise load and safety were evaluated. The test comprised walking within the upper and lower limits of the heart rate (HR) calculated via the Karvonen method. The results showed that in case of 16% O2, the same energy was consumed despite significantly lower walking speed and distance than those in case of 20% O2. Therefore, it is suggested that the Karvonen method is effective in setting the load for hypoxic training. In addition, real-time monitoring of arterial oxygen saturation (SpO2) could be used to evaluate the safety of hypoxic training. Based on these results, we have developed a wearable pulse oximeter that can measure both HR and SpO2 from the earlobe and a dedicated smartphone application for analysis. If these can be practically applied, hypoxic training can be conducted safely that will contribute to the prevention of lifestyle diseases and the consequent extension of healthy life expectancy.

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