“Remind Me Later” in Mobile Security Notifications: What Factors Lead to Users’ Deferred Security Coping Behavior?

Smartphone users often find mobile security notifications (MSNs) to be annoying and intrusive. MSNs are security warnings displayed on mobile interfaces designed to protect mobile phone users from security attacks. Traditionally, users are forced to choose between “Yes” (“Accept”) or “No” (“Ignore” or “Deny”) decisions in response to MSNs. However, in practice, to make MSNs less intrusive, a new “Remind Me Later” button is often added to MSNs as a third option. Consequently, this “Remind Me Later” option causes new problems of deferred security coping behaviors. In other words, hesitant users do not take appropriate actions immediately when security threats take place. Grounding our theoretical basis on choice deferral and dual-task inference, we designed two experiments to understand the key factors affecting users’ deferred security coping decisions in a three-option MSN scenario (“Yes”, “No”, “Remind Me Later”), to determine which MSN message and design features facilitate immediate security coping

Investigation of wearable health tracker version updates

BackgroundWearable fitness trackers are increasingly used in healthcare applications; however, the frequent updating of these devices is at odds with traditional medical device practices.ObjectiveOur objective was to explore the nature and frequency of wearable tracker updates recorded in device changelogs, to reveal the chronology of updates and to estimate the intervals where algorithm updates could impact device validations.MethodUpdates for devices meeting selection criteria (that included their use in clinical trials) were independently labelled by four researchers according to simple function and specificity schema.ResultsDevice manufacturers have diverse approaches to update reporting and changelog practice. Visual representations of device changelogs reveal the nature and chronology of device iterations. 13% of update items were unspecified and 32% possibly affected validations with as few as 5 days between updates that may affect validation.ConclusionManufacturers could aid researchers and health professionals by providing more informative device update changelogs

Investigation of wearable health tracker version updates

Background: Wearable fitness trackers are increasingly used in healthcare applications; however, the frequent updating of these devices is at odds with traditional medical device practices. Objective: Our objective was to explore the nature and frequency of wearable tracker updates recorded in device changelogs, to reveal the chronology of updates and to estimate the intervals where algorithm updates could impact device validations. Method: Updates for devices meeting selection criteria (that included their use in clinical trials) were independently labelled by four researchers according to simple function and specificity schema. Results: Device manufacturers have diverse approaches to update reporting and changelog practice. Visual representations of device changelogs reveal the nature and chronology of device iterations. 13% of update items were unspecified and 32% possibly affected validations with as few as 5 days between updates that may affect validation. Conclusion: Manufacturers could aid researchers and health professionals by providing more informative device update changelogs

Investigating Driver Experience and Augmented Reality Head-Up Displays in Autonomous Vehicles

Autonomous driving is on the horizon. Partially automated vehicles recently started to emerge in the market, and companies are dedicated to bringing more automated driving capabilities to the vehicles in the near future. Over the past twenty years, human factors research has increased our understanding of driver behavior and human-vehicle interaction, as well as human-automation interaction considerably. However, as the technological developments accelerate, there is an urgent need to conduct research to understand the challenges of driving a semi-automated vehicle, the role of cognitive and social factors and driver characteristics, and how interactive technology can be used to increase driving safety in this context. This thesis was an attempt to address some of these challenges. In this work, we present two studies on human factors of automated driving. In the first study, we present the results of a survey conducted with Tesla drivers who have been using partially automated driving features of Tesla cars. Our results revealed that current users of this technology are early adopters. Automation failures were common, but drivers were comfortable in dealing with these situations. Additionally, Tesla drivers have high levels of trust in the automated driving capability of their vehicles, and their trust increases as they experience these features more. The results also revealed that drivers don’t use owner manuals, and seek out information about their cars by using online sources. The majority of Tesla drivers check multiple information sources when their car software receives an update. Overall these findings show that driver needs are changing as the vehicles become smarter and connected. In the second study, we focused on a future technology, augmented reality head-up displays, and explored how this technology can fit into the smart, connected and autonomous vehicle context. Specifically, we conducted an experiment looking into how these displays can be used to monitor the status of automation in automated driving. Participants watched driving videos enhanced with augmented reality cues. Results showed that drivers adjust their trust in the automated vehicle better when information about the vehicle’s sensing capabilities are presented using augmented reality cues, and they have positive attitudes towards these systems. However, there were no major safety-related benefits associated with using these displays. Overall, this work provides several contributions to the knowledge about human-automation interaction in automated driving