skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: Autoencoder-based Photoplethysmography (PPG) signal reliability enhancement in construction health monitoring
Prior research has validated Photoplethysmography (PPG) as a promising biomarker for assessing stress factors in construction workers, including physical fatigue, mental stress, and heat stress. However, the reliability of PPG as a stress biomarker in construction workers is hindered by motion artifacts (MA) - distortions in blood volume pulse measurements caused by sensor movement. This paper develops a deep convolutional autoencoder-based framework, trained to detect and reduce MA in MA-contaminated PPG signals. The framework's performance is evaluated using PPG signals acquired from individuals engaged in specific construction tasks. The results demonstrate the framework has effectiveness in both detecting and reducing MA in PPG signals with a detection accuracy of 93% and improvement in signal-to-noise ratio by over 88%. This research contributes to a more reliable and error-reduced usage of PPG signals for health monitoring in the construction industry.  more » « less
Award ID(s):
2401745
PAR ID:
10516270
Author(s) / Creator(s):
;
Publisher / Repository:
Elsevier B.V.
Date Published:
Journal Name:
Automation in Construction
Volume:
165
Issue:
C
ISSN:
0926-5805
Page Range / eLocation ID:
105537
Subject(s) / Keyword(s):
Construction worker health monitoring Convolutional neural network Photoplethysmography Motion artifact Autoencoder Anomaly detection
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; (, Construction Research Congress 2024)
    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. 
    more » « less
  2. ; ; (, Construction Research Congress 2024)
    Recent advancements in wearable physiological sensing and artificial intelligence have made some remarkable progress in workers’ health monitoring in construction sites. However, the scalable application is still challenging. One of the major complications for deployment has been the distribution shift observed in the physiological data obtained through sensors. This study develops a deep adversarial domain adaptation framework to adapt to out-of-distribution data(ODD) in the wearable physiological device based on photoplethysmography (PPG). The domain adaptation framework is developed and validated with reference to the heart rate predictor based on PPG. A heart rate predictor module comprising feature generating encoder and predictor isinitially trained with data from a given training domain. An unsupervised adversarial domain adaptation method is then implemented for the test domain. In the domain adaptation process, the encoder network is adapted to generate domain invariant features for the test domain using discriminator-based adversarial optimization. The results demonstrate that this approach can effectively accomplish domain adaptation, as evidenced by a 27.68% reduction in heart rate prediction error for the test domain. The proposed framework offers potential for scaled adaptation in the jobsite by addressing the ODD problem. 
    more » « less
  3. ; (, Journal of Medical Devices)
    Abstract This work details the partially observable markov decision process (POMDP) and the point-based value iteration (PBVI) algorithms for use in multisensor systems, specifically, a sensor system capable of heart rate (HR) estimation through wearable photoplethysmography (PPG) and accelerometer signals. PPG sensors are highly susceptible to motion artifact (MA); however, current methods focus more on overall MA filters, rather than action specific filtering. An end-to-end embedded human activity recognition (HAR) System is developed to represent the observation uncertainty, and two action specific PPG MA reducing filters are proposed as actions. PBVI allows optimal action decision-making based on an uncertain observation, effectively balancing correct action choice and sensor system cost. Two central systems are proposed to accompany these algorithms, one for unlimited observation access and one for limited observation access. Through simulation, it can be shown that the limited observation system performs optimally when sensor cost is negligible, while limited observation access performs optimally when a negative reward for sensor use is considered. The final general framework for POMDP and PBVI was applied to a specific HR estimation example. This work can be expanded on and used as a basis for future work on similar multisensor system. 
    more » « less
  4. ; ; (, CIB Conferences)
    While drones have exhibited considerable potential to revolutionize the construction industry, previous studies across domains have proposed that human-drone interaction could cause adverse impacts on humans (e.g., collision and discomfort). Given that construction has been recognized as a hazardous and high-stress workplace, it deserves deep exploration regarding how newly introduced drones will influence worker well-being during the interaction. However, there is a paucity of research on worker stress when communicating with drones in construction. Successful human-drone interaction must necessitate seamless and comfortable communication between workers and drones. Therefore, this study investigates the impact of physically demanding response levels on construction workers’ mental and physical well-being throughout the communication cycle. Three levels of physical responses (low, medium, and high) required for drone communication were simulated in an extended reality roofing experiment. During the communication process, real-time stress levels were assessed through participants’ electrodermal activity. The results indicated that a higher physical level of communication significantly increased workers’ higher stress levels in both the response and decoding phases. Additionally, providing drones’ feedback in verbal human-drone communication is especially important to reduce workers’ confusion and mental stress. This study highlights the critical need for worker-centric design and communication strategies in drone integration within construction. 
    more » « less
  5. ; ; ; ; (, Construction Research Congress 2024)
    Exoskeletons, also known as wearable robots, are being studied as a potential solution to reduce the risk of work-related musculoskeletal disorders (WMSDs) in construction. The exoskeletons can help enhance workers’ postures and provide lift support, reducing the muscular demands on workers while executing construction tasks. Despite the potential of exoskeletons inreducing the risk of WMSDs, there is a lack of understanding about the potential effects ofexoskeletons on workers’ psychological states. This lack of knowledge raises concerns thatexoskeletons may lead to psychological risks, such as cognitive overload, among workers. Tobridge this gap, this study aims to assess the impact of back-support exoskeletons (BSE) onworkers’ cognitive load during material lifting tasks. To accomplish this, a physiologically basedcognitive load assessment framework was developed. This framework used wearable biosensorsto capture the physiological signals of workers and applied Autoencoder and Ensemble Learningtechniques to train a machine learning classifier based on the signals to estimate cognitive loadlevels of workers while wearing the exoskeleton. Results showed that using BSE increasedworkers’ cognitive load by 33% compared to not using it during material handling tasks. Thefindings can aid in the design and implementation of exoskeletons in the construction industry. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email