Monitoring and evaluating the status and behaviour of construction workers using wearable sensing technologies DOI
Mingzhu Wang, Jiayu Chen, Ma Jun

et al.

Automation in Construction, Journal Year: 2024, Volume and Issue: 165, P. 105555 - 105555

Published: June 20, 2024

Language: Английский

Fault diagnosis study of hydraulic pump based on improved symplectic geometry reconstruction data enhancement method DOI
Siyuan Liu,

Jixiong Yin,

Ming Hao

et al.

Advanced Engineering Informatics, Journal Year: 2024, Volume and Issue: 61, P. 102459 - 102459

Published: March 5, 2024

Language: Английский

Citations

19
TFormer: A time–frequency Transformer with batch normalization for driver fatigue recognition DOI
Ruilin Li, Minghui Hu, Ruobin Gao

et al.

Advanced Engineering Informatics, Journal Year: 2024, Volume and Issue: 62, P. 102575 - 102575

Published: May 20, 2024

Language: Английский

Citations

19
Real-Time Monitoring of Mental Fatigue of Construction Workers Using Enhanced Sequential Learning and Timeliness DOI
Xin Fang, Xincong Yang,

Xuejiao Xing

et al.

Automation in Construction, Journal Year: 2024, Volume and Issue: 159, P. 105267 - 105267

Published: Jan. 10, 2024

Language: Английский

Citations

13
Recent Advances in Wearable Healthcare Devices: From Material to Application DOI Creative Commons
Xiao Luo, Handong Tan, Weijia Wen

et al.

Bioengineering, Journal Year: 2024, Volume and Issue: 11(4), P. 358 - 358

Published: April 6, 2024

In recent years, the proliferation of wearable healthcare devices has marked a revolutionary shift in personal health monitoring and management paradigm. These devices, ranging from fitness trackers to advanced biosensors, have not only made more accessible, but also transformed way individuals engage with their data. By continuously signs, physical-based biochemical-based such as heart rate blood glucose levels, technology offers insights into human health, enabling proactive rather than reactive approach healthcare. This towards personalized empowers knowledge tools make informed decisions about lifestyle medical care, potentially leading earlier detection issues tailored treatment plans. review presents fabrication methods flexible applications care. The potential challenges future prospectives are discussed.

Language: Английский

Citations

12
EEG-based floor vibration serviceability evaluation using machine learning DOI
Jiang Li, Weizhao Tang, Jiepeng Liu

et al.

Advanced Engineering Informatics, Journal Year: 2025, Volume and Issue: 64, P. 103089 - 103089

Published: Jan. 7, 2025

Language: Английский

Citations

1
Advances in automated anesthesia: a comprehensive review DOI Creative Commons
Xiuding Cai, Xueyao Wang, Yaoyao Zhu

et al.

Anesthesiology and Perioperative Science, Journal Year: 2025, Volume and Issue: 3(1)

Published: Jan. 17, 2025

Abstract Anesthesia is a fundamental aspect of modern medical practice, ensuring patient safety and comfort during surgical procedures by effectively managing hypnosis analgesia. The rapid advancement artificial intelligence (AI) has facilitated the emergence automated anesthesia systems, significantly enhancing precision, efficiency, adaptability management in complex environments. This review provides comprehensive survey existing literature on anesthesia, focusing three key areas: physiological modeling, automatic control, performance evaluation. It critically examines strengths limitations current methodologies, including traditional statistical learning, machine learning deep approaches, while discussing future development trends field. By synthesizing recent technological advancements clinical applications, this work aims to provide valuable insights for researchers clinicians, promoting evolution intelligent practices. Ultimately, underscores transformative potential AI-driven solutions delivering personalized care, optimizing both analgesia, outcomes.

Language: Английский

Citations

1
Air traffic controllers' mental fatigue recognition: A multi-sensor information fusion-based deep learning approach DOI
Xiaoqing Yu, Chun‐Hsien Chen, Haohan Yang

et al.

Advanced Engineering Informatics, Journal Year: 2023, Volume and Issue: 57, P. 102123 - 102123

Published: Aug. 1, 2023

Language: Английский

Citations

20
Multimodal integration for data-driven classification of mental fatigue during construction equipment operations: Incorporating electroencephalography, electrodermal activity, and video signals DOI Creative Commons
Imran Mehmood, Heng Li, Waleed Umer

et al.

Developments in the Built Environment, Journal Year: 2023, Volume and Issue: 15, P. 100198 - 100198

Published: July 13, 2023

Construction equipment operations that require high levels of attention can cause mental fatigue, which lead to inefficiencies and accidents. Previous studies classified fatigue using single-modal data with acceptable accuracy. However, is a multimodal problem, no single modality superior. Moreover, none the previous in construction industry have investigated fusion for classifying whether such an approach would improve detection. This study proposes novel three machine learning models classify states. Electroencephalography, electrodermal activity, video signals were acquired during excavation operation, decision tree model sensor outperformed other 96.2% accuracy 96.175%–98.231% F1 scores. Multimodal aid development real-time system safety management at sites.

Language: Английский

Citations

18
Fatigue in construction workers: A systematic review of causes, evaluation methods, and interventions DOI
Haiyi Zong, Wen Yi, Maxwell Fordjour Antwi‐Afari

et al.

Safety Science, Journal Year: 2024, Volume and Issue: 176, P. 106529 - 106529

Published: April 10, 2024

Language: Английский

Citations

6
Non-invasive detection of mental fatigue in construction equipment operators through geometric measurements of facial features DOI
Imran Mehmood, Heng Li, Waleed Umer

et al.

Journal of Safety Research, Journal Year: 2024, Volume and Issue: 89, P. 234 - 250

Published: Feb. 22, 2024

Language: Английский

Citations

5