Dr. Saed Talib Amer
Dr. saed amer Senior Lecturer Industrial And Systems Engineering

Contact Information
saed.amer@ku.ac.ae 02 312 3919


Dr. Amer earned his Doctorate of Philosophy in Computer and Information Systems Engineering in August 2012 from Tennessee State University, USA. Dr. Amer is currently a faculty in the Department of Industrial and Systems Engineering at Khalifa University and leading research endeavors on the advances in Health Safety and Environment engineering, training, and HSE education.  Other research fields that Dr. Amer is involved in are Human Factors simulation and validation, Seat comfort analyses, and the assimilation of the human into Industry 4.0. Previous work includes sustainability assessments, systematic measures to enforce engineering sustainability education, and autonomous solutions for Unexploded Ordnance (UXO) remediation. Finally, Dr. Amer worked on simulation solutions for hybrid renewable energy research. 

  • Ph.D. in Computer and Information Systems Engineering, August 2012, Tennessee State University o Concentration on Computer Integrated Manufacturing Engineering o Received scholarship for academic excellence in research and development of Seat Comfort Analyses
  • Master of Engineering in Mechanical Engineering; Dec. 2007, Tennessee State University y

  • Ergonomics and Human Factors Engineering (CHEM660)
  • Industrial Hygiene Engineering (WENV602)
  • Introduction to HSE Engineering (PGEG221)
  • Lean Manufacturing (ISYE352)
  • QHSE Program Management (SCIN610)
  • Quantitative Methods in Physical Sciences (ISYE311)
  • Systems Project Management (ISYE362)

Affiliated Research Institutes/Centers

Research Projects

The main objective of this study is to propose a single tool that evaluates seat comfort while considering three main factors impacting the comfort level, which are: contact pressure, joint angles, and overall body posture. Seat comfort analyses are a recurring topic in many disciplines. Traditional methods usually depend on the subjective feedback of participants and consume numerous resources to assess seat comfort without considering the comfort of the body in the sitting posture. The proposed technique uses Computer-Aided Design (CAD) to model human mannequins and seats of different designs and then measures the contact pressure between them using Finite Element Analysis. Analysis of posture and joint angles are performed using comfort assessment tools available within human factors simulation software. The results obtained are validated in the real environment using a pressure mapping system and using both marker-less and inertial motion sensor-based motion capture systems. The contact pressure and rating obtained from the comfort assessment scales are integrated wherein the weight of each of the factors was calculated using Analytical Hierarchy Process (AHP) method. Based on the relative importance of different aspects of comfort, the most comfortable sitting configuration on an office chair is the one with backrests and armrests. The integrated system can detect different postures of the human body when sitting and assess the level of comfort of body parts and/ or joints using human factors simulation software. By varying the seat features, the system can explore different seat designs and propose the optimum combination for the best sitting comfort. The expected contributions of this study will enrich the comfort assessment process for new seats, and recommendations will be made on how to improve seat comfort assessment in the early design stages without the exploitation of resources. 

One of the effects of the internet becoming increasingly accessible to the common masses is the increase in daily e-commerce activities. Also due to pandemic of 2020 e-commerce has seen an unprecedented rise in its popularity. For example, in July 2020, Amazon sites had 213 million unique visitors and over 2.3 million active sellers. To cater to such huge demand along with the sheer variety of products e-commerce giants have long been trying to come up with strategies to save the cost of operation as well as reduce the time required to fulfill the demands of the consumer. As such low safety stock as well as fast deliveries have become the primary target for such companies. These goals are being challenged in what is known as the fourth industrial revolution also known as Industry 4.0. A subset of which is Logistic 4.0. 

Because of social media and influencers that are present online it has become near impossible to forecast the demand of the consumers. There are evidence that certain product endorsed by some social media influencers create a short-lived trend for those products because of which there is unstable demand with the consumers. To meet these challenges the revolution in logistic activities is termed Logistic 4.0 which can be defined as “The logistical system which enables the sustainable satisfaction of the individualized customer demands without an increase in costs and supports this development in industry and trade using digital technologies” (Winkelhaus and Grosse 2020). Internet of Things (IoT), and Augmented Reality (AR) are some of the means to meet these new requirements. Logistic 4.0. According to (Winkelhaus and Grosse 2020) Logistics 4.0 employs both digitalization and automation along with the interaction between humans and technologies.

Managing the workers’ health and safety faces many challenges due to the dependency on human assessments especially when it comes to human monitoring and detecting non-conformance. Conventionally, HSE decision-making is achieved by collecting information from the worker himself/herself or by an HSE officer making it mostly subjective and difficult to quantify and share. In this paper, we propose a   continuous approach to empirically monitor the workers using machine vision along with smart decision-making tools to detect, recognize and classify human behaviors. The input of the system is coherent and effective while the output is unbiased, quantifiable, repeatable and communicable, the needed ingredients to integrate the human factors into Industry 4.0. The scope of this work focuses on the worker’s health and safety by setting another building block in the Safety 4.0 vision. The proposed system consists of multiple integrated components including continuous video streaming devices, machine vision components, computer logic capability, communication schemes, and locally executed alarms. The system was tested on a simulated environment using a human factors simulation platform, then was validated with actual environments with workers acting with HSE non-conformance while performing different tasks. The results show the system’s ability to recognize the human location, posture, and speed and then compare it to the HSE guidelines. The results also show that the system was able to provide fast responses by giving warnings, reporting an incident to the management, or shutting the process down if an injury is recognized. Finally, the system generates data and reports that are ready to be transmitted onto the Internet of Things.

Recent fire incidents have caught the attention of authorities, engineers, architects, and other stakeholders in the construction industry regarding the application of aluminum composite panels. Major high-rise buildings worldwide have experienced fire incidents associated with aluminum composite panels necessitating research in regulatory requirements. The UK, the UAE, France, Australia, and South Korea are countries whose high-rise buildings have experienced fire incidents associated with fire spreading through ACPs. An investigation of specific incidents revealed substantial fire spread by ACPs, leading to losses. Aluminum composite panels are a group of sandwich panels that have hardwired applications in the construction industry in the last three decades. This research identifies glaring gaps in the regulatory framework for applying aluminum composite panels in the construction of high-rise buildings. It investigates the contribution of gaps in the regulatory framework today regarding safety and fire incidents associated with ACPs in different countries. After ascertaining the gaps, the research makes appropriate recommendations for the safe application of ACPs in the construction industry.

Research Staff and Graduate Students:

Shayaan Syed MSc in Engineering Systems and Management
Hala Mohammad Bermamet MSc in Engineering Systems and Management
Alyaziya Faisal Farouq Mohamed Al-Jaberi MSc in Engineering Systems and Management
Aisha Ismail Mohamed Salem Alhosani MSc in Engineering Systems and Management
Rashed Nasser Alzaabi MSc in Engineering Systems and Management