Dr Waleed Umer

Assistant Professor

Department: Mechanical and Construction Engineering

My interdisciplinary research integrates advancements in health sciences, artificial intelligence & machine learning, and digital technologies to address critical issues in the construction industry. With my collaborators, I have explored several key areas: (1) real-time location monitoring using ultra-wide band sensors, (2) ergonomic evaluation of workers using electromyography & motion sensors, (3) investigating factors leading to fall accidents using force-plate and inertial measurement units, (4) mental fatigue monitoring with eye-tracking technology, electroencephalography, and computer vision sensors, and (5) physical fatigue prediction for construction workers using physiological and computer vision sensors. Some notable outputs from these research endeavors include: (1) Development of a tool to monitor balance of construction workers, (2) A novel methodology to assess fatigue using heart rate variability metrics, (3) Fatigue monitoring by fusing vision and foot-pressure sensors using inverse dynamic modelling. My current work focuses on: (1) augmented reality and Visual Learning Models (VLMs) powered solutions, (2) developing human-robot collaboration tools, and (3) exploring innovative methods for accurate fatigue monitoring.

I have worked on several funded research projects as PI and co-PI with a combined funding of over £300,000. These projects were funded by Research Grant Council of Hong Kong and Ministry of Higher Education, Saudi Arabia.

Email Waleed

Key Publications

Please visit the Pure Research Information Portal for further information
Evaluation of sweat-based biomarkers using wearable biosensors for monitoring stress and fatigue: a systematic review, Jie, M., Li, H., Anwer, S., Umer, W., Antwi-Afari, M., Xiao, E. 6 Apr 2024, In: International Journal of Occupational Safety and Ergonomics
Monitoring Mental Fatigue of Construction Equipment Operators: A Smart Cushion–Based Method with Deep Learning Algorithms, Wang, L., Li, H., Wu, H., Yao, Y., Yu, C., Umer, W., Han, D., Ma, J. 10 Jul 2024, In: Journal of Management in Engineering
Non-invasive detection of mental fatigue in construction equipment operators through geometric measurements of facial features, Mehmood, I., Li, H., Umer, W., Ma, J., Saad Shakeel, M., Anwer, S., Antwi-Afari, M., Tariq, S., Wu, H. 1 Jun 2024, In: Journal of Safety Research
Real-Time Monitoring of Mental Fatigue of Construction Workers Using Enhanced Sequential Learning and Timeliness, Fang, X., Yang, X., Xing, X., Wang, J., Umer, W., Guo, W. 1 Mar 2024, In: Automation in Construction
Critical success factors for implementing blockchain technology in construction, Sun, W., Antwi-Afari, M., Mehmood, I., Anwer, S., Umer, W. 1 Dec 2023, In: Automation in Construction
Deep learning-based construction equipment operators’ mental fatigue classification using wearable EEG sensor data, Mehmood, I., Li, H., Qarout, Y., Umer, W., Anwer, S., Wu, H., Hussain, M., Fordjour Antwi-Afari, M. Apr 2023, In: Advanced Engineering Informatics
Identification and Classification of Physical Fatigue in Construction Workers Using Linear and Nonlinear Heart Rate Variability Measurements, Anwer, S., Li, H., Umer, W., Antwi-Afari, M., Mehmood, I., Yu, Y., Haas, C., Wong, A. 1 Jul 2023, In: Journal of Construction Engineering and Management
Machine learning-based identification and classification of physical fatigue levels: A novel method based on a wearable insole device, Antwi-Afari, M., Anwer, S., Umer, W., Mi, H., Yu, Y., Moon, S., Hossain, M. 1 Jan 2023, In: International Journal of Industrial Ergonomics
Multimodal integration for data-driven classification of mental fatigue during construction equipment operations: Incorporating electroencephalography, electrodermal activity, and video signals, Mehmood, I., Li, H., Umer, W., Arsalan, A., Anwer, S., Mirza, M., Ma, J., Antwi-Afari, M. 1 Oct 2023, In: Developments in the Built Environment
Towards automated physical fatigue monitoring and prediction among construction workers using physiological signals: An on-site study, Umer, W., Yu, Y., Fordjour Antwi-Afari, M., Anwer, S., Jamal, A. 1 Oct 2023, In: Safety Science

Qualifications

Construction Managment PhD
Construction Managment MSc
Civil Engineering BEng (Hons)
Fellow of Higher Education Academy FHEA

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