Detecting Fear of Heights Response to a Virtual Reality Environment Using Functional Near-Infrared Spectroscopy

To enable virtual reality exposure therapy (VRET) that treats anxiety disorders by gradually exposing the patient to fear using virtual reality (VR), it is important to monitor the patient's fear levels during the exposure. Despite the evidence of a fear circuit in the brain as reflected by functional near-infrared spectroscopy (fNIRS), the measurement of fear response in highly immersive VR using fNIRS is limited, especially in combination with a head-mounted display (HMD). In particular, it is unclear to what extent fNIRS can differentiate users with and without anxiety disorders and detect fear response in a highly ecological setting using an HMD. In this study, we investigated fNIRS signals captured from participants with and without a fear of height response. To examine the extent to which fNIRS signals of both groups differ, we conducted an experiment during which participants with moderate fear of heights and participants without it were exposed to VR scenarios involving heights and no heights. The between-group statistical analysis shows that the fNIRS data of the control group and the experimental group are significantly different only in the channel located close to right frontotemporal lobe, where the grand average oxygenated hemoglobin Δ[HbO] contrast signal of the experimental group exceeds that of the control group. The within-group statistical analysis shows significant differences between the grand average Δ[HbO] contrast values during fear responses and those during no-fear responses, where the Δ[HbO] contrast values of the fear responses were significantly higher than those of the no-fear responses in the channels located towards the frontal part of the prefrontal cortex. Also, the channel located close to frontocentral lobe was found to show significant difference for the grand average deoxygenated hemoglobin contrast signals. Support vector machine-based classifier could detect fear responses at an accuracy up to 70% and 74% in subject-dependent and subject-independent classifications, respectively. The results demonstrate that cortical hemodynamic responses of a control group and an experimental group are different to a considerable extent, exhibiting the feasibility and ecological validity of the combination of VR-HMD and fNIRS to elicit and detect fear responses. This research thus paves a way toward the a brain-computer interface to effectively manipulate and control VRET.

Relevant Physiological Indicators for Assessing Workload in Conditionally Automated Driving, Through Three-Class Classification and Regression

In future conditionally automated driving, drivers may be asked to take over control of the car while it is driving autonomously. Performing a non-driving-related task could degrade their takeover performance, which could be detected by continuous assessment of drivers' mental load. In this regard, three physiological signals from 80 subjects were collected during 1 h of conditionally automated driving in a simulator. Participants were asked to perform a non-driving cognitive task (N-back) for 90 s, 15 times during driving. The modality and difficulty of the task were experimentally manipulated. The experiment yielded a dataset of drivers' physiological indicators during the task sequences, which was used to predict drivers' workload. This was done by classifying task difficulty (three classes) and regressing participants' reported level of subjective workload after each task (on a 0–20 scale). Classification of task modality was also studied. For each task, the effect of sensor fusion and task performance were studied. The implemented pipeline consisted of a repeated cross validation approach with grid search applied to three machine learning algorithms. The results showed that three different levels of mental load could be classified with a f1-score of 0.713 using the skin conductance and respiration signals as inputs of a random forest classifier. The best regression model predicted the subjective level of workload with a mean absolute error of 3.195 using the three signals. The accuracy of the model increased with participants' task performance. However, classification of task modality (visual or auditory) was not successful. Some physiological indicators such as estimates of respiratory sinus arrhythmia, respiratory amplitude, and temporal indices of heart rate variability were found to be relevant measures of mental workload. Their use should be preferred for ongoing assessment of driver workload in automated driving.

Bio-Inspired Multimodal Imaging in Reduced Visibility

The visual systems found in nature rely on capturing light under different modalities, in terms of spectral sensitivities and polarization sensitivities. Numerous imaging techniques are inspired by this variety, among which, the most famous is color imaging inspired by the trichromacy theory of the human visual system. We investigate the spectral and polarimetric properties of biological imaging systems that will lead to the best performance on scene imaging through haze, i.e., dehazing. We design a benchmark experiment based on modalities inspired by several visual systems, and adapt state-of-the-art image reconstruction algorithms to those modalities. We show the difference in performance of each studied systems and discuss it in front of our methodology and the statistical relevance of our data.

Eye Movement and Pupil Measures: A Review

Our subjective visual experiences involve complex interaction between our eyes, our brain, and the surrounding world. It gives us the sense of sight, color, stereopsis, distance, pattern recognition, motor coordination, and more. The increasing ubiquity of gaze-aware technology brings with it the ability to track gaze and pupil measures with varying degrees of fidelity. With this in mind, a review that considers the various gaze measures becomes increasingly relevant, especially considering our ability to make sense of these signals given different spatio-temporal sampling capacities. In this paper, we selectively review prior work on eye movements and pupil measures. We first describe the main oculomotor events studied in the literature, and their characteristics exploited by different measures. Next, we review various eye movement and pupil measures from prior literature. Finally, we discuss our observations based on applications of these measures, the benefits and practical challenges involving these measures, and our recommendations on future eye-tracking research directions.

Quantum-Inspired Uncertainty Quantification

Reasonable quantification of uncertainty is a major issue of cognitive infocommunications, and logic is a backbone for successful communication. Here, an axiomatic approach to quantum logic, which highlights similarity to and differences to classical logic, is presented. The axiomatic method ensures that applications are not restricted to quantum physics. Based on this, algorithms are developed that assign to an incoming signal a similarity measure to a pattern generated by a set of training signals.

Data Checkers: A Grid-Based UI for Managing Patient-Generated Data Sharing to Support Collaborative Self-Care

Chronic health conditions are becoming increasingly prevalent. As part of chronic care, sharing patient-generated health data (PGHD) is likely to play a prominent role. Sharing PGHD is increasingly recognized as potentially useful for not only monitoring health conditions but for informing and supporting collaboration with caregivers and healthcare providers. In this paper, we describe a new design for the fine-grained control over sharing one's PGHD to support collaborative self-care, one that centers on giving people with health conditions control over their own data. The system, Data Checkers (DC), uses a grid-based interface and a preview feature to provide users with the ability to control data access and dissemination. DC is of particular use in the case of severe chronic conditions, such as spinal cord injuries and disorders (SCI/D), that require not just intermittent involvement of healthcare providers but daily support and assistance from caregivers. In this paper, after providing relevant background information, we articulate our steps for developing this innovative system for sharing PGHD including (a) use of a co-design process; (b) identification of design requirements; and (c) creation of the DC System. We then present a qualitative evaluation of DC to show how DC satisfied these design requirements in a way that provided advantages for care. Our work extends existing research in the areas of Human-Computer Interaction (HCI), Computer-Supported Cooperative Work (CSCW), Ubiquitous Computing (Ubicomp), and Health Informatics about sharing data and PGHD.

Communicating Photograph Content Through Tactile Images to People With Visual Impairments

Millions of people with a visual impairment across the world are denied access to visual images. They are unable to enjoy the simple pleasures of viewing family photographs, those in textbooks or tourist brochures and the pictorial embellishment of news stories etc. We propose a simple, inexpensive but effective approach, to make content accessible via touch. We use state-of-the-art algorithms to automatically process an input photograph into a collage of icons, that depict the most important semantic aspects of a scene. This collage is then printed onto swell paper. Our experiments show that people can recognise content with an accuracy exceeding 70% and create plausible narratives to explain it. This means that people can understand image content via touch. Communicating scene foreground is a step forward, but there are many other steps needed to provide the visually impaired with the fullest possible access to visual content.

How Older Adults Learn ICT—Guided and Self-Regulated Learning in Individuals With and Without Disabilities

Learning to use information and communication technologies (ICT) may be more difficult for older people due to decreases in fluid intelligence, generational effects, and other age-related effects. Especially older people with intellectual disabilities (ID) are at a high risk of digital exclusion. To enable all older adults to use ICT, individualized technology training may be provided. However, little is known about the ICT learning preferences among older people with ID. Based on semi-structured interviews with older adults (n = 7, mean age = 76.6 years) and older adults with ID (n = 14, mean age = 62.4 years), this paper analyzes learning strategies, preferences, and learning settings. The results from content analysis show that guided learning with personal explanations in a one-to-one setting is the most preferred learning format in both groups of older adults. While many older adults without ID additionally favor self-regulated learning (i.e., learning with manuals or videos), older adults with ID mostly rely on guided learning with personal assistance. The differences can be explained by different abilities (e.g., reading skills) and social networks (e.g., living situation, having children). Not all older adults have a family or an institutional support network to help them learn ICT and community organizations may provide additional support. Researchers and practitioners should be aware of the diverse knowledge backgrounds and competencies in the group of older adults. ICT training in old age should be ideally composed in a modular way embedding self-regulated learning formats into guided learning modules.

Predicting Activation Liking of People With Dementia

Physical, social and cognitive activation is an important cornerstone in non-pharmacological therapy for People with Dementia (PwD). To support long-term motivation and well-being, activation contents first need to be perceived positively. Prompting for explicit feedback, however, is intrusive and interrupts the activation flow. Automated analyses of verbal and non-verbal signals could provide an unobtrusive means of recommending suitable contents based on implicit feedback. In this study, we investigate the correlation between engagement responses and self-reported activation ratings. Subsequently, we predict ratings of PwD based on verbal and non-verbal signals in an unconstrained care setting. Applying Long-Short-Term-Memory (LSTM) networks, we can show that our classifier outperforms chance level. We further investigate which features are the most promising indicators for the prediction of activation ratings of PwD.