Towards an Efficient Physiological-Based Worker Health Monitoring System in Construction: An Adaptive Filtering Method for Removing Motion Artifacts in Physiological Signals of Workers

Construction workers are vulnerable to physical and mental health challenges, causing illnesses, injuries, and fatalities. This fact stresses the need to closely assess and monitor the health and safety conditions of construction workers. Recently, researchers have used biosensor technology to develop several health monitoring frameworks that can monitor workers' safety and health status through the acquisition and analysis of workers' physiological signals. Despite the potential of these frameworks in monitoring subjects' health status in a controlled lab environment, there is a concern regarding the performance of these frameworks in the field environment. One of the main limiting factors affecting the field performance of these frameworks is motion artifacts in the captured physiological signals. The frequent movements of workers while performing construction tasks can cause motion artifacts during signal acquisition, which will significantly reduce the quality of the captured physiological signals and thus degrade the performance of health monitoring frameworks. To address this gap, this study developed a motion artifacts removal method based on least mean squares adaptive filtering algorithms. To examine the performance, 12 subjects were asked to perform a material delivery construction task while their physiological signals were captured via a wristband-type biosensor, and the proposed method was applied to the signal acquisition process. Results reported that the proposed method removed 61.9% of motion artifacts from the captured EDA, PPG, and ST signals and improved the corresponding signal-to-noise ratio by 51.6%. This study contributes to the establishment of efficient physiological-based health-monitoring frameworks for construction workers.