Mark Chignell

University of Toronto, Mechanical and Industrial Engineering, Faculty Member

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I am a Professor of Mechanical and Industrial Engineering at the University of Toronto where I have been since 1990. Before that I was an Assistant Professor of Industrial and Systems Engineering at the University of Southern California for six years. I have a Ph.D in Psychology and a Masters in Industrial and Systems Engineering and I like to do research that is at the intersection of psychology, engineering and computer science. I carry out research in human factors and user interface design, with particular interest in aging, adaptive interfaces and healthcare, telecommunications, and driving.
Phone: 416-978-8951
Address: 5 King's College Road, Toronto, ON M5S 3G8, Canada

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University of Saskatchewan

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University of Leeds

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Harvard Medical School

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New England Complex Systems Institute

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University of Dhaka, Bangladesh

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Universidad Francisco Marroquin

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Nepal College of Information Technology

Anna Tito

University of Canberra

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Graduate Center of the City University of New York

Maxim Bakaev

Novosibirsk State Technical University

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Returning to Nature: VR Mediated States of Enhanced Wellness

A visit to a place of natural beauty is known to have restorative potential. Immersing oneself in... more

Returning to Nature: VR Mediated States of Enhanced Wellness

Lecture Notes in Computer Science, 2020

A visit to a place of natural beauty is known to have restorative potential. Immersing oneself in... more

ClickIR: Text Retrieval using a Dynamic Hypertext Interface

Text REtrieval Conference, 1998

Page 1. ClickIR: Text Retrieval using a Dynamic Hypertext Interface Richard C. Bodner and Mark H.... more

UI Design for an Engineering Process: Programming Experiments on a Liquid Handling Robot

In this paper we describe the development of a user interface for a liquid handling robot. We des... more In this paper we describe the development of a user interface for a liquid handling robot. We describe the user interface design process that we carried out, beginning with requirements analysis and rapid prototyping, followed by heuristic evaluation. We then demonstrate the resulting cloud interface where the robot is controlled remotely in a teleoperation mode to program common protocols in chemistry, and biology, laboratories. We describe the main characteristics of the web interface, focusing on solution strategies used to expose features for programming experiments, remotely. We also report on the System Usability Scale (SUS) scores obtained by testing the interface with 15 experts in the fields of chemistry, and biology.

Assessing unreliability in OTT video QoE subjective evaluations using clustering with idealized data

In this paper, we describe an Over-The-Top (OTT) video Quality of Experience (QoE) subjective eva... more In this paper, we describe an Over-The-Top (OTT) video Quality of Experience (QoE) subjective evaluation experiment that was carried out to examine variations in the way subjects assess viewing experiences. The experiment focuses on different level of impairment and failure types, using 5-point measurement scales. Clustering is used to differentiate between unreliable and reliable participants, where reliability is defined in terms of criteria such as consistency of rating and ability to distinguish between qualitative differences in level of impairments. The results show that clustering a data set that is augmented with unreliable pseudo-participants can provide a new and improved perspective on individual differences in video QoE assessment.

Subjective QoE assessment on video service: Laboratory controllable approach

This paper introduces research that addresses the subjective assessment of Quality of Experience ... more This paper introduces research that addresses the subjective assessment of Quality of Experience (QoE) during the entire life cycle of a video session. We define a video session life cycle as the time from when a user attempts to initiate playback, until such time that the video ends either from normal video conclusion or through a network-induced failure. We provide a detailed description of our assessment methodology designed to discern whether a user's QoE would be impacted by the presence of failures. To accomplish this, we carefully select various test conditions to take into consideration the rating scale used, the types of impairments and failures seen by the user, and whether impaired videos are seen together with failed videos in multi-video sessions. The selection and creation of source video sequences are also discussed, as well as the use of between-subjects and within-subjects approaches for running our experiments in a controlled laboratory setting. Statistical analysis was carried out to interpret our experimental results. We compared the results of the between-subjects measures and the results of the within-subjects measures, and concluded that the introduction of a scale with an extended lower bound enabled subjects to more clearly express their dissatisfaction of videos with failures when compared to the traditional ITU 5-point rating scale. In addition, we observed that videos that were simply impaired but concluded normally did not have a statistically significant difference when an extended scale was used.

Mark Chignell

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