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Head movement is widely used as a uniform type of input for human-computer interaction. However, there are fundamental differences between head movements coupled with gaze in support of our visual system, and head movements performed as gestural expression. Both Head-Gaze and Head Gestures are of utility for interaction but differ in their affordances. To facilitate the treatment of Head-Gaze and Head Gestures as separate types of input, we developed HeadBoost as a novel classifier, achieving high accuracy in classifying gaze-driven versus gestural head movement (F1-Score: 0.89). We demonstrate the utility of the classifier with three applications: gestural input while avoiding unintentional input by Head-Gaze; target selection with Head-Gaze while avoiding Midas Touch by head gestures; and switching of cursor control between Head-Gaze for fast positioning and Head Gesture for refinement. The classification of Head-Gaze and Head Gesture allows for seamless head-based interaction while avoiding false activation.

Knowledge of passenger flow underpins any optimal public transport application, such as new facilities design and operations. The interactions between passengers and sensors may provide the foundation to measure this flow and dismiss users and staff from the measures’ validation loop. Removing humans and their errors may impact significantly and improve measures’ cost and quality. The literature considered smartphones the leading enabler due to market penetration and embodied sensors. Smartphones allow users’ localization, identification, authentication, and billing. Via Bluetooth, smartphones detect short-range implicit interactions, device-to-device. We model passenger states on buses, either be-in or be-out (BIBO). The BIBO use case identifies a fundamental building block of continuously-valued passenger flow, which this paper describes through a Human-Computer interaction experimental setting involving two autonomous buses and a proprietary smartphone-Bluetooth sensing platform. The resulting dataset of 14,000 observations/sensors contains two ground-truth levels: the first is the passengers’ validation; the second is validation by video cameras surveilling buses and tracks. This study verifies separately classification based on Bluetooth and GPS signals, as well as an inertial navigation system, evaluating signals and related machine learning (ML) classifiers against measurement and ground-truth noise. The paper contributes a Monte Carlo simulation of labels-flip to emulate human errors in the labeling process, as in smartphone surveys, and a novel unsupervised variational auto-encoder classifier. Experimental results indicate error-free human validation is unlikely. The impact of mistakes on model performance bias can be significant. This use case supports the potential substitution of human validation with independent Bluetooth validation.

Type 2 diabetes (T2D) is a large disease burden worldwide and represents an increasing and complex challenge for all societies. For the individual, (T2D) is a complex, multi-dimensional, and long-term challenge to manage, and it is challenging to establish and maintain good communication between the patient and healthcare professionals. This paper presents DiaFocus, which is a mobile health (mHealth) sensing application for long-term ambulatory management of T2D. DiaFocus supports an adaptive collection of physiological, behavioral, and contextual data in combination with ecological assessments of psycho-social factors. This data is used for improving patient-clinician communication during consultations. DiaFocus is built using a generic data collection framework for mobile and wearable sensing and is highly extensible and customizable. We deployed DiaFocus in a 6-week feasibility study involving 12 patients with T2D. The patients found the DiaFocus approach and system useful and usable for diabetes management. Most patients would use such a system, if available as part of their treatment. Analysis of the collected data shows that mobile sensing is feasible for longitudinal ambulatory assessment of T2D, and helped identify the most appropriate target users being early diagnosed and technically literate T2D patients.

This study aimed to investigate the reproducibility of pupil size measurements over time and between reading methods when comparing human-assisted reading to automated reading. Pupillary data were analyzed on a subset of myopic children enrolled in a multicenter randomized clinical trial on myopia control with low-dose atropine. Pupil size measurements were obtained prior to randomization at two time points (screening and baseline visits) using a dedicated pupillometer under mesopic and photopic conditions. A customized algorithm was built to perform automated readings, allowing comparisons between human-assisted and automated readings. Reproducibility analyses followed the principles of Bland and Altman and included the calculation of the mean difference between measurements and limits of agreement (LOA). We included 43 children. Mean (standard deviation) age was 9.8 (1.7) years and 25 (58%) children were girls. Using human-assisted readings, reproducibility over time showed mesopic mean difference of 0.02 mm with LOA from −0.87 mm to 0.91 mm, whereas photopic mean difference was −0.01 mm with LOA from −0.25 mm to 0.23 mm. Reproducibility between human-assisted and automated readings was also higher under photopic conditions, with mean difference of 0.03 mm and LOA from −0.03 mm to 0.10 mm at screening and mean difference of 0.03 mm and LOA from −0.06 mm to 0.12 mm at baseline. Using a dedicated pupillometer, we found that examinations performed under photopic conditions demonstrated higher reproducibility over time and between reading methods. We speculate whether mesopic measurements are sufficiently reproducible to be monitored over time. Furthermore, photopic measurements may be of greater relevance when evaluating the side effects of atropine treatment, such as photophobia.

Background: Self-management of the progressive disease type 2 diabetes mellitus (T2DM) becomes part of the daily life of patients starting from the time of diagnosis. However, despite the availability of technical innovations, the uptake of digital solutions remains low. One reason that has been reported is that digital solutions often focus purely on clinical factors that may not align with the patient's perspective. Objective: The aim of this study was to develop digital solutions that address the needs of patients with T2DM, designed from the user's perspective. The goal was to address the patients' expressed real-world needs by having the users themselves choose the scope and format of the solutions. Methods: Using participatory methods, we conducted 3 cocreation workshops in collaboration with the Danish Diabetes Association, with 20 persons with T2DM and 11 stakeholders across workshops: user experience designers, researchers, and diabetes experts including a diabetes nurse. The overall structure of the 3 workshops was aligned with the 4 phases of the double diamond: initially discovering and mapping out key experienced issues, followed by a workshop on thematic mapping and definition of key concepts, and succeeded by an exploration and development of 2 prototypes. Subsequently, high-fidelity interactive prototypes were refined as part of the delivery phase, in which 7 formative usability tests were conducted. Results: The workshops mapped experiential topics over time from prediagnosis to the current state, resulting in a detailed exploration and understanding of 6 themes related to and based on the experiences of patients with T2DM: diabetes care, diabetes knowledge, glucose monitoring, diet, physical activity, and social aspects of diabetes. Two prototypes were developed by the participants to address some of their expressed needs over time related to the 6 themes: an activity-based continuous glucose monitoring ap

Recordings of the pupillary response have been used in numerous studies to assess listening effort during a speech-in-noise task. Most studies focused on averaged responses across listeners, whereas less is known about pupil dilation as an indicator of the individuals’ listening effort. The present study investigated the reliability of several pupil features as potential indicators of individual listening effort and the impact of different normalization procedures on the reliability. The pupil diameters of 31 normal-hearing listeners were recorded during multiple visits while performing a speech-in-noise task. The signal-to-noise ratios (SNRs) of the stimuli ranged from (Formula presented.) 12 dB to (Formula presented.) 4 dB. All listeners were measured twice at separate visits, and 11 were re-tested at a third visit. To examine the reliability of the pupil responses across visits, the intraclass correlation coefficient was applied to the peak and mean pupil dilation and to the temporal features of the pupil response, extracted using growth curve analysis. The reliability of the pupillary response was assessed in relation to SNR and different normalization procedures over multiple visits. The most reliable pupil features were the traditional mean and peak pupil dilation. The highest reliability results were obtained when the data were baseline-corrected and normalized to the individual pupil response range across all visits. Moreover, the present study results showed only a minor impact of the SNR and the number of visits on the reliability of the pupil response. Overall, the results may provide an important basis for developing a standardized test for pupillometry in the clinic.

Micro-interventions are quick focused behavioural interventions aimed at matching users’ current capacity for engagement. Mobile devices are powerful, interactive, and sensor-rich platforms for delivering micro-interventions and for determining when and where to do so. This paper presents a novel smart-phone based approach to micro-interventions based on established ‘Episodic Future Thinking (EFT)’ research. The paper both presents the background for EFT-based micro-interventions and the design of an ‘EFT’ smartphone application implementing this. The approach and technology were evaluated in a feasibility study including 14 participants using the system for 14 days. Results demonstrate the feasibility of implementing EFT as micro-interventions, with participants willing to use the application, providing positive feedback and constructive suggestions. The paper concludes with a thorough discussion of the implications for smartphone-based EFT delivered as micro-interventions and how these can be used as part of a larger behaviour change and health improvement initiatives.

Recent years have seen numerous clinical deployments of digital technologies in support of new practices of healthcare. Mobile devices in particular offer many advantages in regard to their deployment for the purposes of shaping care. Yet, these systems and their implications for practice are not predetermined but crafted, often in unforeseen ways, by design. Amidst growing knowledge of complex clinical contexts, human–computer interaction researchers have come to understand the need to approach design as participatory, iterative process grounded in research, and informed by the experiences of stakeholders broadly defined. In this article we build upon prior efforts to support care, by making the case for a recentering of the artefacts we perceive as “designed,” “designable,” and “design-worthy” in the creation and implementation of the digital health intervention. In doing so, we draw on the example of a mobile health technology platform to support mental healthcare through Danish primary care

Contemporary dilemmas about the role and impact of digital technologies in society have motivated the inclusion of topics of computing ethics in university programmes. Many past works have investigated how different pedagogical approaches and tools can support learning and teaching such a subject. This brief research report contributes to these efforts by describing a pilot study examining how engineering students learn from and apply ethical principles when making design decisions for an introductory User Experience (UX) design project. After a short lecture, students were asked to design and evaluate the ethical implications of digital health intervention prototypes. This approach was evaluated through the thematic analysis of semi-instructed interviews conducted with 12 students, focused on the benefits and limitations of teaching ethics this way. Findings indicate that it can be very challenging to convey the importance of ethics to unaware and uninterested students, an observation that calls for a much stronger emphasis on moral philosophy education throughout engineering degrees. This paper finishes with a reflection on the hardships and possible ways forward for teaching and putting UX design ethics into practice. The lessons learned and described in this report aim to contribute to future pedagogical efforts to enable ethical thinking in computing education.

Pupillometry data are commonly reported relative to a baseline value recorded in a controlled pre-task condition. In this study, the influence of the experimental design and the preparatory processing related to task difficulty on the baseline pupil size was investigated during a speech intelligibility in noise paradigm. Furthermore, the relationship between the baseline pupil size and the temporal dynamics of the pupil response was assessed. The analysis revealed strong effects of block presentation order, within-block sentence order and task difficulty on the baseline values. An interaction between signal-to-noise ratio and block order was found, indicating that baseline values reflect listener expectations arising from the order in which the different blocks were presented. Furthermore, the baseline pupil size was found to affect the slope, delay and curvature of the pupillary response as well as the peak pupil dilation. This suggests that baseline correction might be sufficient when reporting pupillometry results in terms of mean pupil dilation only, but not when a more complex characterization of the temporal dynamics of the response is considered. By clarifying which factors affect baseline pupil size and how baseline values interact with the task-evoked response, the results from the present study can contribute to a better interpretation of the pupillary response as a marker of cognitive processing.

Mental fatigue is known to occur as a result of activities related to e.g. transportation, health-care, military operations and numerous other cognitive demanding tasks. Gaze tracking has wide-ranging applications, with the technology becoming more compact and processing power reducing. Though numerous techniques have been applied to measure mental fatigue using gaze tracking, smooth-pursuit movement, a natural eye movement generated when following a moving object with gaze, has not been explored with relation to mental fatigue. In this paper, we report the results from a smooth-pursuit movement based eye-typing experiment with varying task difficulty to generate cognitive load, performed in the morning and afternoon by 36 participants. We have investigated the effects of time-on-task and time of day on mental fatigue using self-reported questionnaires and smooth-pursuit performance, extracted from the gaze data. The self-reported mental fatigue increased due to time-on-task, but the time of day did not have an effect. The results illustrate that smooth-pursuit movement performance declined with time-on-task, with increased error in the gaze position and an inability to match the speed of the moving object. The findings exhibit the feasibility of mental fatigue detection using smoothpursuit movements during an eye-interactive task of eye-typing.

The growing commercial success of smart speaker devices following recent advancements in speech recognition technology has surfaced new opportunities for collecting self-reported health and well-being data. Speech-enabled conversational agents (CAs) in particular, deployed in home environments using just such systems, may offer increasingly intuitive and engaging means of self-report. To date, however, few real-world studies have examined users' experiences of engaging in the self-report of mental health using such devices or the challenges of deploying these systems in the home context. With these aims in mind, this article recounts findings from a 4-week "in-the-wild"study during which 20 individuals with depression or bipolar disorder used a speech-enabled CA named "Sofia"to maintain a daily diary log, responding also to the World Health Organization-Five Well-Being Index WHO-5 scale every 2 weeks. Thematic analysis of post-study interviews highlights actions taken by participants to overcome CAs' limitations, diverse personifications of a speech-enabled agent, and unique forms of valuing of this system among users' personal and social circles. These findings serve as initial evidence for the potential of CAs to support the self-report of mental health and well-being, while highlighting the need to address outstanding technical limitations in addition to design challenges of conversational pattern matching, filling unmet interpersonal gaps, and the use of self-report CAs in the at-home social context. Based on these insights, we discuss implications for the future design of CAs to support the self-report of mental health and well-being.

Recent years have seen numerous clinical deployments of digital technologies in support of new practices of healthcare. Mobile devices in particular offer many advantages in regard to their deployment for the purposes of shaping care. Yet, these systems and their implications for practice are not predetermined but crafted, often in unforeseen ways, by design. Amidst growing knowledge of complex clinical contexts, human-computer interaction researchers have come to understand the need to approach design as participatory, iterative process grounded in research, and informed by the experiences of stakeholders broadly defined. In this article we build upon prior efforts to support care, by making the case for a recentering of the artefacts we perceive as "designed", "designable" and "design-worthy" in the creation and implementation of the digital health intervention. In doing so, we draw on the example of a mobile health technology platform to support mental healthcare through Danish primary care.

Gaming is a more accessible, engaging and popular past-time than ever before. Recent research highlights games as strikingly effective means of capturing and holding our attention-so effective, some argue, to the point of deleterious effect. An impassioned CHI2021 panel discussion directed these efforts towards the ethics and adoption of dark patterns. And yet, we know little as to how dark patterns are perceived and arise in the design, development and use of games. This paper seeks to address this knowledge gap by recounting findings from a design-led inquiry comprising interviews and workshops conducted with mobile game players, designers, developers, and business developers. We contribute an understanding of how dark patterns arise in the development, use and commercialisation of mobile games, their effects on players and industry professionals, and means for the consideration, negotiation and navigation of these strategies for gamer-engagement by design-in support of healthier, highly-engaging game experiences.

Recent advancements in speech recognition technology in combination with increased access to smart speaker devices are expanding conversational interactions to ever-new areas of our lives – including our health and wellbeing. Prior human-computer interaction research suggests that Conversational Agents (CAs) have the potential to support a variety of health-related outcomes, due in part to their intuitive and engaging nature. Realizing this potential requires however developing a rich understanding of users' needs and experiences in relation to these still-emerging technologies. To inform the design of CAs for health and wellbeing, we analyze 2741 critical reviews of 485 Alexa health and fitness Skills using an automated topic modeling approach; identifying 15 subjects of criticism across four key areas of design (functionality, reliability, usability, pleasurability). Based on these findings, we discuss implications for the design of engaging CAs to support health and wellbeing.

Adopting speech as their mode of interaction, speech-enabled conversational agent (CA) systems hold the potential to enable more natural and engaging self-report experiences than traditional media (e.g., pen and paper, web, or smartphone systems). Our recent research concerns the potential design and use of CAs to support mental health and wellbeing. In this article, we present findings from a study during which 22 individuals with affective disorders (e.g., depression, bipolar disorder) used either a speech-enabled CA or web app to self-report their emotional wellbeing. Analysis of users’ experiences and engagement with the system for daily self-report suggests that despite many technical limitations, users rated self-reporting via CA as a significantly novel and attractive experience yielding similar rates of engagement to a traditional web-based method.