Primary Areas of Interest
Research at Occupational Ergonomics Research Lab seeks improvements in industrialized construction by evaluating ergonomic risks and investigating corresponding corrective measures to secure the health and safety of workers and enhance workplace productivity.
Ergonomics and workplace design in industrialized construction
This research program is targeting to proactively reduce the workplace injuries and claims and improve productivity by bringing ergonomic thinking into workplace design. The outcome of this research will contribute to the integration of ergonomic thinking in workplace design. The early application of the ergonomic and productivity assessments will improve workplace design and reduce costs for Canada’s construction sector.
Ergonomics-centric human motion and productivity analysis in 3D visualization for a panelized construction manufacturing facility
NSERC Engage, ACQBUILT
ACQBUILT Inc. is a leading wooden-frame panelized home-builder in North America. Although the
company employs an advanced, semi-automated manufacturing line, intense manual labour and high
physical demands are still required. Due to increased demand for the prefabricated panelized homes, the
physical demand and the risk of developing WMSDs are even increasing. ACQBUILT has many
Workers’ Compensation Board (WCB) claims on work-related musculoskeletal disorders (WMSDs),
especially low back pain and shoulder stress. This study will help ACQBUILT to develop a systematic
ergonomic-based human motion and productivity analysis using a 3D visualization platform to
proactively identify ergonomic risks and propose modified work through the change of production
procedure and process, and workplace design. The ultimate goal is a healthier workplace with reduced
WMSDs, and improved productivity.
Intelligent Monitoring and Data Analytics for Construction Safety and Productivity Improvement
NSERC, Alberta Innovates
Workers in construction are exposed to disproportionately high physical and health risks due to the
labour-intensive nature of tasks and the unpredictable and uncontrollable nature of the working environment.
These conditions contribute to an elevated risk of the onset of work-related musculoskeletal disorders and the
occurrence of workplace lost-time injuries, thereby further hampering productivity. These issues remain a
challenge due to the lack of a robust method for timely monitoring and control of these activities. Numerous
efforts have been made to capture this information using wearable monitoring technologies and professional
sensors. However, the broad implementation and scaling of these technologies is inhibited by the high cost of
the sensors, as well as their adverse effect on ongoing construction activities. The aim of this project, then, is to
develop a cost-effective and contactless solution for real-time monitoring of workers and the working
environment during construction. The system will be developed based on advanced artificial intelligence and
computer vision algorithms. Combining computer vision with advanced ergonomic and safety risk assessment
methods, the system will be able to provide timely feedback and prediction regarding the unsafe behaviour of
workers and the presence of hazards in construction workplaces. This information will benefit construction
workers by reducing the risk to which they are exposed during work. It will benefit project owners and
managers by giving them more control over the inherent risk in construction activities, and helping them to
minimize the financial and productivity losses associated with incident-related injuries and machine failure.
The Development of An Enterprise Digital Twin for Robotic Building Panel Factory Operations
Landmark TekStart, Mitacs
The Internet of Things (IoT) is a network of connected physical objects embedded with sensors which allows a lot of devices to be connected and be able to communicate, analyze and share data about the physical world. Digital twin (DT) is critical to the growth of IoT as DT replicates IoT devices in a virtual environment and it enables current condition analysis and future scenario simulation and prediction. The main objective of developing this digital twin model is to document, analyze and simulate enterprise operation in dynamic environment in order to provide efficient operation control along with the effective support for the enterprise decision-making. The most fruitful strategy (combination of both physical system and data based structured DT) and methods (big data analytics, machine learning and numerical simulation) will be used in this proposed DT system. This DT model, composed of hardware and software, will collect, process, transmit, store and use information to help enterprise achieve their enterprise planning objectives.
Work smarter, work safer.