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Afzali Borujeni, S H, Zare, M and Adelzade Saadabadi, L (2024) Comparison of factors affecting the acceptance of the trenchless technology and open-trench method using ANP and AHP: Case study in Iran. Journal of Construction Engineering and Management, 150(01).
Aghililotf, M, Ramezanianpour, A M, Arbabi, H and Maghrebi, M (2024) Identifying construction managers' challenges: A novel approach based on social network analysis. Journal of Construction Engineering and Management, 150(01).
Alikhani, H and Latifi, M (2024) Evaluation of the healing performance of hot-mix asphalt containing waste steel shavings under different microwave induction healing cycles. Journal of Construction Engineering and Management, 150(01).
Anwer, S, Li, H, Antwi-Afari, M F, Mirza, A M, Rahman, M A, Mehmood, I, Guo, R and Wong, A Y L (2024) Evaluation of data processing and artifact removal approaches used for physiological signals captured using wearable sensing devices during construction tasks. Journal of Construction Engineering and Management, 150(01).
- Type: Journal Article
- Keywords: artifact eradication; construction health; construction safety; digital construction; noise removal; physiological signals; sensing devices
- ISBN/ISSN: 0733-9364
- URL: http://doi.org/10.1061/JCEMD4.COENG-13263
- Abstract:
Wearable sensing devices (WSDs) have enormous promise for monitoring construction worker safety. They can track workers and send safety-related information in real time, allowing for more effective and preventative decision making. WSDs are particularly useful on construction sites since they can track workers' health, safety, and activity levels, among other metrics that could help optimize their daily tasks. WSDs may also assist workers in recognizing health-related safety risks (such as physical fatigue) and taking appropriate action to mitigate them. The data produced by these WSDs, however, is highly noisy and contaminated with artifacts that could have been introduced by the surroundings, the experimental apparatus, or the subject's physiological state. These artifacts are very strong and frequently found during field experiments. So, when there is a lot of artifacts, the signal quality drops. Recently, artifacts removal has been greatly enhanced by developments in signal processing, which has vastly enhanced the performance. Thus, the proposed review aimed to provide an in-depth analysis of the approaches currently used to analyze data and remove artifacts from physiological signals obtained via WSDs during construction-related tasks. First, this study provides an overview of the physiological signals that are likely to be recorded from construction workers to monitor their health and safety. Second, this review identifies the most prevalent artifacts that have the most detrimental effect on the utility of the signals. Third, a comprehensive review of existing artifact-removal approaches were presented. Fourth, each identified artifact detection and removal approach was analyzed for its strengths and weaknesses. Finally, in conclusion, this review provides a few suggestions for future research for improving the quality of captured physiological signals for monitoring the health and safety of construction workers using artifact removal approaches.
Arowoiya, V A, Oke, A E, Ojo, L D and Adelusi, A O (2024) Driving factors for the adoption of digital twin technology implementation for construction project performance in Nigeria. Journal of Construction Engineering and Management, 150(01).
D'Orazio, M, Bernardini, G and Di Giuseppe, E (2024) Improving sustainable management of university buildings based on occupancy data. Journal of Construction Engineering and Management, 150(01).
Da Silva, W O P, Farias, B A, Monteiro, I B, Pegorini, V, Casanova, D and Bisconsini, D R (2024) Development of global quality index of unpaved roads. Journal of Construction Engineering and Management, 150(01).
Ding, S, Hu, H, Chai, Z and Wang, W (2024) Secure and formalized blockchain-IPFS information sharing in precast construction from the whole supply chain perspective. Journal of Construction Engineering and Management, 150(01).
Garcia-Lopez, N P and Fischer, M (2024) Managing on-site production using an activity and flow-based construction model. Journal of Construction Engineering and Management, 150(01).
Jalloul, H, Choi, J, Manheim, D, Yesiller, N and Derrible, S (2024) Incorporating disaster debris into sustainable construction research and practice. Journal of Construction Engineering and Management, 150(01).
Koc, K, Budayan, C, Ekmekcioǧlu, Ö and Tokdemir, O B (2024) Predicting cost impacts of nonconformances in construction projects using interpretable machine learning. Journal of Construction Engineering and Management, 150(01).
Leung, M Y, Wei, X and Ojo, L D (2024) Developing a value-risk management model for construction projects. Journal of Construction Engineering and Management, 150(01).
Liu, Y, Wang, X, Guo, S, Shi, X and Wang, D (2024) Analyzing the optimization of subsidies for PPP urban rail transit projects: A choice between passenger demand, vehicle kilometer, or an improved efficiency-oriented framework. Journal of Construction Engineering and Management, 150(01).
Miao, K, Lou, W, Schonfeld, P and Xiao, Z (2024) Optimal earthmoving-equipment combination considering carbon emissions with an indicator-based multiobjective optimizer. Journal of Construction Engineering and Management, 150(01).
Ning, X, Zhai, F, Xia, N and Hu, X (2024) Protecting the ego: Anticipated image risk as a psychological deterrent to construction workers' safety citizenship behavior. Journal of Construction Engineering and Management, 150(01).
Olayiwola, J, Yusuf, A, Akanmu, A, Gonsalves, N and Abraham, Y (2024) Efficacy of annotated video-based learning environment for drawing students' attention to construction practice concepts. Journal of Construction Engineering and Management, 150(01).
Salih, F, Eissa, R and El-Adaway, I H (2024) Data-driven analysis of progressive design build in water and wastewater infrastructure projects. Journal of Construction Engineering and Management, 150(01).
Zhong, B, Shen, L, Pan, X, Zhong, X and He, W (2024) Dispute classification and analysis: Deep learning-based text mining for construction contract management. Journal of Construction Engineering and Management, 150(01).