@article{jeelani_asadi_ramshankar_han_albert_2021, title={Real-time vision-based worker localization & hazard detection for construction}, volume={121}, ISSN={["1872-7891"]}, DOI={10.1016/j.autcon.2020.103448}, abstractNote={Despite training, construction workers often fail to recognize a significant proportion of hazards in construction environments. Therefore, there is a need for developing technology that assists workers and safety managers in identifying hazards in complex and dynamic construction environments. This study develops a framework for an automated system that detects hazardous conditions and objects in real-time to assist workers and managers. The framework consists of three independent pipelines for localization of workers, semantic segmentation of the visual scene around workers, and detection of static and dynamic hazards. The framework can be used to automate and augment the hazard detection ability of workers and safety managers in construction workplaces. In addition, the framework offers several computing contributions including an improved real-time worker localization method and an efficient architecture for integrating pipelines for entity localization and object detection. A system developed based on the proposed framework as a proof of concept and was tested in indoor and outdoor construction environments. It achieved over 93% accuracy.}, journal={AUTOMATION IN CONSTRUCTION}, author={Jeelani, Idris and Asadi, Khashayar and Ramshankar, Hariharan and Han, Kevin and Albert, Alex}, year={2021}, month={Jan} }
 @article{albert_jeelani_han_2020, title={Developing hazard recognition skill among the next-generation of construction professionals}, volume={38}, ISSN={["1466-433X"]}, DOI={10.1080/01446193.2020.1797133}, abstractNote={Abstract Globally, a large number of safety hazards remain unrecognised in construction workplaces. These unrecognised safety hazards are also likely to remain unmanaged and can potentially cascade into unexpected safety incidents. Therefore, the development of hazards recognition skill – particularly among the next-generation of construction professionals – is vital for injury prevention and safe work-operations. To foster the development of such skill, the current investigation examined the effect of administering a hazard recognition intervention to students seeking to enter the construction workforce. First, prior to introducing the intervention, the pre-intervention hazard recognition skill of the participating students was measured. Next, the intervention that included a number of programme elements was introduced. The programme elements included (1) visual cues to promote systematic hazard recognition, (2) personalised hazard recognition performance feedback, (3) visual demonstration of common hazard recognition search weaknesses, and (4) diagnosis of hazard search weaknesses using metacognitive prompts. Finally, the post-intervention skill demonstrated by the student participants was measured and compared against their pre-intervention performance. The results suggest that the intervention was effective in improving the hazard recognition skill demonstrated by the next-generation of construction professionals. The observed effect was particularly prominent among those that demonstrated relatively lower levels of skill in the pre-intervention phase. The research also unveiled particular impediments to hazards recognition that the participants experienced.}, number={11}, journal={CONSTRUCTION MANAGEMENT AND ECONOMICS}, author={Albert, Alex and Jeelani, Idris and Han, Kevin}, year={2020}, month={Nov}, pages={1024–1039} }
 @article{jeelani_albert_han_azevedo_2019, title={Are Visual Search Patterns Predictive of Hazard Recognition Performance? Empirical Investigation Using Eye-Tracking Technology}, volume={145}, ISSN={0733-9364 1943-7862}, url={http://dx.doi.org/10.1061/(ASCE)CO.1943-7862.0001589}, DOI={10.1061/(ASCE)CO.1943-7862.0001589}, abstractNote={AbstractPoor hazard recognition is a widespread issue in the construction industry. When construction hazards remain unrecognized, workers are more likely to indulge in unsafe behavior, experience ...}, number={1}, journal={Journal of Construction Engineering and Management}, publisher={American Society of Civil Engineers (ASCE)}, author={Jeelani, Idris and Albert, Alex and Han, Kevin and Azevedo, Roger}, year={2019}, month={Jan}, pages={04018115} }
 @article{jeelani_han_albert_2018, title={Automating and scaling personalized safety training using eye-tracking data}, volume={93}, ISSN={0926-5805}, url={http://dx.doi.org/10.1016/j.autcon.2018.05.006}, DOI={10.1016/j.autcon.2018.05.006}, abstractNote={Research has shown that a large proportion of hazards remain unrecognized, which expose construction workers to unanticipated safety risks. Recent studies have also found that a strong correlation exists between viewing patterns of workers, captured using eye-tracking devices, and their hazard recognition performance. Therefore, it is important to analyze the viewing patterns of workers to gain a better understanding of their hazard recognition performance. From the training standpoint, scan paths and attention maps, generated using eye-tracking technology, can be used effectively to provide personalized and focused feedback to workers. Such feedback is used to communicate the search process deficiency to workers in order to trigger self-reflection and subsequently improve their hazard recognition performance. This paper proposes a computer vision-based method that tracks workers on a construction site and automatically locates their fixation points, collected using a wearable eye-tracker, on a 3D point cloud. This data is then used to analyze their viewing behavior and compute their attention distribution. The presented case studies validate the proposed method.}, journal={Automation in Construction}, publisher={Elsevier BV}, author={Jeelani, Idris and Han, Kevin and Albert, Alex}, year={2018}, month={Sep}, pages={63–77} }
 @article{jeelani_albert_azevedo_jaselskis_2017, title={Development and Testing of a Personalized Hazard-Recognition Training Intervention}, volume={143}, ISSN={["1943-7862"]}, DOI={10.1061/(asce)co.1943-7862.0001256}, abstractNote={AbstractUnrecognized or unmanaged hazards can expose workers to unanticipated safety risk and can potentially result in catastrophic safety incidents. Unfortunately, recent research has demonstrate...}, number={5}, journal={JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT}, author={Jeelani, Idris and Albert, Alex and Azevedo, Roger and Jaselskis, Edward J.}, year={2017}, month={May} }
 @inproceedings{jeelani_han_albert_2017, title={Development of Immersive Personalized Training Environment for Construction Workers}, url={http://dx.doi.org/10.1061/9780784480830.050}, DOI={10.1061/9780784480830.050}, abstractNote={The ability of workers to recognize and manage construction hazards is essential for effective safety management. However, studies have unanimously demonstrated that a large proportion of construction hazards remain unrecognized in dynamic work environments. Such poor hazard recognition levels have been partly attributed to the pervasive use of unengaging and ineffective training practices within construction. To improve training effectiveness, recent efforts have focused on assessing the learning needs of particular workers, and customizing training experiences accordingly to maximize training outcomes. This paper builds upon the previous research by developing an immersive safety training environment that provide a more effective personalized training experience for workers. After development, the degree of realism and immersive experience offered by the training environment was measured and found to be 73% of the real environment. The findings of this study will be useful to practicing professionals seeking to improve training efforts are safety training outcomes.}, booktitle={Computing in Civil Engineering 2017}, author={Jeelani, I. and Han, K. and Albert, A.}, year={2017}, month={Jun}, pages={407–415} }
 @article{jeelani_albert_gambatese_2017, title={Why Do Construction Hazards Remain Unrecognized at the Work Interface?}, volume={143}, ISSN={["1943-7862"]}, DOI={10.1061/(asce)co.1943-7862.0001274}, abstractNote={AbstractProper hazard recognition is an essential prerequisite to effective safety management. However, recent research has demonstrated that a large proportion of safety hazards remain unrecognize...}, number={5}, journal={JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT}, author={Jeelani, Idris and Albert, Alex and Gambatese, John A.}, year={2017}, month={May} }