Autoencoder-Based Motion Artifact Reduction in Photoplethysmography (PPG) Signals Acquired from Wearable Sensors during Construction Tasks

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Abstract
Construction workers often experience high levels of physical and mental stress due to the demanding nature of their work on construction sites. Real-time health monitoring can provide an effective means of detecting these stressors. Previous research in this field has demonstrated the potential of photoplethysmography (PPG), which represents cardiac activities, as a biomarker for assessing various stressors, including physical fatigue, mental stress, and heat stress. However, PPG acquisition during construction tasks is subject to several external noises, of which motion artifact is a major one. To address this, the study develops and examines an autoencoder network—a special type of artificial neural network—to remove PPG signals’ motion artifacts during construction tasks, thereby enhancing the accuracy of health assessments. Artifact-free PPG signals are acquired through subjects in a stationary position, which is used as the reference for training the autoencoder network. The network’s performance is examined with PPG signals acquired from the same subjects performing multiple construction tasks. The developed autoencoder network can increase the signal-to-noise ratio (SNR) by up to 33% for the corrupted signals acquired in a construction setting. This research contributes to the extensive and resilient use of PPG signals in health monitoring for construction workers.

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