Psychophysiological insights and user perspectives: enhancing police de-escalation skills through full-body VR training.

In recent years, Virtual Reality (VR) has emerged as a promising tool for enhancing training responses in high-stress professions, notably among police officers. This study investigates the psychophysiological responses and subjective user experience of active police officers undergoing Mental Health Crisis Response (MHCR) training using an immersive full-body VR system. A total of 10 active police officers with Special Weapons and Tactics (SWAT) training participated in our controlled study. Officers independently took part in one VR training session lasting 7-12 min involving an avatar in crisis portrayed by an actor. Officers wore integrated cardiovascular and electrodermal activity measurement devices for physiological monitoring. VR user experience aspects such as induced symptoms or game mechanics were investigated upon completing the training, aiming to evaluate the officer's perceptions of the technology. We used the DePICT™ scale to evaluate the de-escalation skills of officers, coded by a research professional. Our findings revealed significant differences in heart rate and heart rate variability responses between baseline and VR scenario immersion, suggesting heightened stress regulation during the MHCR simulation using full-body VR. Arousal measurements also revealed measurable responses during the training in VR. Additionally, the user experience assessment indicated a positive reception to the VR training, with minimal VR-induced symptoms. A "Defensive-Dynamics-Dichotomy" was revealed highlighting dominant autonomic responses linked to defensive actions (e.g., officers who drew a weapon; those who kept their weapons holstered) and their respective implications for stress management and cognitive function. A unique constellation of de-escalation skills was revealed among officers who relied on weapons relative to those who did not, to resolve the scenario. The study highlighted the perceived utility of physiological monitoring technologies in enhancing police training outcomes. In conclusion, our research underscores the potential of VR as an effective tool for de-escalation training following MHCR simulated scenarios among active police officers, offering insights into its psychophysiological impact and user experience. The findings contribute to improving our understanding of the physiology associated with decision-making in police officers to draw a weapon, emphasizing the role of advanced simulation and physiological monitoring technology in developing evidence-based training programs for public safety.

Virtual reality as a tool to explore multisensory processing before and after engagement in physical activity.

Introduction: This pilot study employed a non-randomized control trial design to explore the impact of physical activity within a virtual reality (VR) environment on multisensory processing among community-dwelling older adults. Methods: The investigation compared both chronic (over 6 weeks) and acute effects of VR-based physical activity to a reading control group. The evaluation metrics for multisensory processing included audiovisual response time (RT), simultaneity judgments (SJ), sound-induced flash illusion (SIFI), and temporal order judgments (TOJ). A total of 13 older adults were provided with VR headsets featuring custom-designed games, while another 14 older adults were assigned to a reading-based control group. Results: Results indicated that acute engagement in physical activity led to higher accuracy in the SIFI task (experimental group: 85.6%; control group: 78.2%; p = 0.037). Additionally, both chronic and acute physical activity resulted in quicker response times (chronic: experimental group = 336.92; control group = 381.31; p = 0.012; acute: experimental group = 333.38; control group = 383.09; p = 0.006). Although the reading group showed a non-significant trend for greater improvement in mean RT, covariate analyses revealed that this discrepancy was due to the older age of the reading group. Discussion: The findings suggest that immersive VR has potential utility for enhancing multisensory processing in older adults. However, future studies must rigorously control for participant variables like age and sex to ensure more accurate comparisons between experimental and control conditions.

Fine-grained population mapping from coarse census counts and open geodata.

Fine-grained population maps are needed in several domains, like urban planning, environmental monitoring, public health, and humanitarian operations. Unfortunately, in many countries only aggregate census counts over large spatial units are collected, moreover, these are not always up-to-date. We present POMELO, a deep learning model that employs coarse census counts and open geodata to estimate fine-grained population maps with [Formula: see text]m ground sampling distance. Moreover, the model can also estimate population numbers when no census counts at all are available, by generalizing across countries. In a series of experiments for several countries in sub-Saharan Africa, the maps produced with POMELO are in good agreement with the most detailed available reference counts: disaggregation of coarse census counts reaches [Formula: see text] values of 85-89%; unconstrained prediction in the absence of any counts reaches 48-69%.

Immersive Virtual Reality Exergames to Promote the Well-being of Community-Dwelling Older Adults: Protocol for a Mixed Methods Pilot Study.

BACKGROUND: Despite the proven benefits of exercise in older adults, challenges such as access and motivation can deter their engagement. Interactive virtual reality (VR) games combined with exercise (exergames) are a plausible strategy to encourage physical activity among this population. However, there has been little research on the feasibility, acceptability, and potential benefits of deploying at-home VR exergames among community-dwelling older adults. OBJECTIVE: The objectives of this study are to estimate the feasibility, usability, and acceptability of a co-designed VR exergame in community-dwelling older adults; examine intervention feasibility and assessment protocols for a future large-scale trial; and provide pilot data on outcomes of interest (physical activity, exercise self-efficacy, mood, cognition, perception, and gameplay metrics). METHODS: The study will be a remote, 6-week intervention comprising an experimental and a control group. A sample of at least 12 community-dwelling older adults (with no or mild cognitive impairment) will be recruited for each group. Both groups will follow the same study procedures and assessment methods. However, the experimental group will engage with a co-designed VR exergame (Seas The Day) thrice weekly for approximately 20 minutes using the Oculus Quest 2 (Facebook Reality Labs) VR headset. The control group will read (instead of playing Seas The Day) thrice weekly for approximately 20 minutes over the 6-week period. A mixed methods evaluation will be used. Changes in physical activity, exercise self-efficacy, mood, cognition, and perception will be compared before and after acute data as well as before and after the 6 weeks between the experimental (exergaming) and control (reading) groups. Qualitative data from postintervention focus groups or interviews and informal notes and reports from all participants will be analyzed to assess the feasibility of the study protocol. Qualitative data from the experimental group will also be analyzed to assess the feasibility, usability, and acceptability of at-home VR exergames and explore perceived facilitators of and barriers to uptaking VR systems among community-dwelling older adults. RESULTS: The screening and recruitment process for the experimental group started in May 2021, and the data collection process will be completed by September 2021. The timeline of the recruitment process for the control group is September 2021 to December 2021. We anticipate an estimated adherence rate of ≥80%. Challenges associated with VR technology and the complexity of remote assessments are expected. CONCLUSIONS: This pilot study will provide important information on the feasibility, acceptability, and usability of a custom-made VR exergaming intervention to promote older adults' well-being. Findings from this study will be useful to inform the methodology, design, study procedures, and assessment protocol for future large-scale trials of VR exergames with older adults as well as deepen the understanding of remote deployment and at-home use of VR for exercise in older adults. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/32955.

Seas the day: Co-designing immersive virtual reality exergames with exercise professionals and people living with dementia.

BACKGROUND: Physical activity (PA) is associated with physical and cognitive benefits among people living with dementia or mild cognitive impairment (PLWD/MCI) and is a meaningful activity that can improve their confidence in everyday life. Exercising in virtual reality environments (VR Exergame) is becoming an increasingly feasible and enjoyable way to promote PA and well-being in PLWD/MCI. Although co-design can significantly improve the design of technology, it is rarely done with PLWD/MCI. This study uses participatory design methods and collaborative approaches to involve key stakeholders to develop and test a VR Exergame "Seas the Day", a novel solution targeting PLWD/MCI well-being. METHODS: A multi-stage, user-centered co-design approach was used to custom-build VR Exergames tailored to the unique needs and abilities of PLWD/MCI based on a first generation of the prototype that was previously developed and tested with PLWD/MCI. This paper describes the next iteration of the prototype. Processes included concept ideation and brainstorming activities, iterative prototyping, and playtesting/input/feedback sessions with key stakeholders (PLWD/MCI, exercise professionals, engineers, VR game designers, content developers). RESULTS: The multidisciplinary and collaborative design process occurred over 15 months (overlapping with COVID-19 pandemic) with 7 PLWD/MCI (6 females; M=81.3 years) and 9 exercise professionals (7 females; M=38.1 years) to date. The game was designed to target movements identified by exercise professionals and researchers (aerobic exercises, range of motion, seated-balance, quick response to stimuli) and is structured in three exercise stages (warm-up, conditioning, cool-down). To ensure safety of participants while using VR headsets, only seated upper-limb exercises were targeted. Stakeholder feedback regarding game mechanics, aesthetics, and visual/auditory cues were gathered during brainstorming and playtesting sessions and implemented into specific game-related scenarios (tai-chi, rowing, fishing). CONCLUSION: We presented the process, outcomes, and challenges of adopting a participatory/collaborative approach with multiple stakeholder groups to co-design VR Exergames tailored to PLWD/MCI. Next steps will include a mixed-method evaluation of the VR Exergames among community-dwelling older adults and PLWD/MCI in retirement communities and long-term care to evaluate: i) feasibility and acceptability of use, ii) game user experience, iii) barriers/facilitators to uptake of VR Exergames; and iv) inform/validate VR Exergames gameplay metrics reflective of cognitive and motor performance.

A Psychophysiological Model of Firearms Training in Police Officers: A Virtual Reality Experiment for Biocybernetic Adaptation.

Crucial elements for police firearms training include mastering very specific psychophysiological responses associated with controlled breathing while shooting. Under high-stress situations, the shooter is affected by responses of the sympathetic nervous system that can impact respiration. This research focuses on how frontal oscillatory brainwaves and cardiovascular responses of trained police officers (N = 10) are affected during a virtual reality (VR) firearms training routine. We present data from an experimental study wherein shooters were interacting in a VR-based training simulator designed to elicit psychophysiological changes under easy, moderate and frustrating difficulties. Outcome measures in this experiment include electroencephalographic and heart rate variability (HRV) parameters, as well as performance metrics from the VR simulator. Results revealed that specific frontal areas of the brain elicited different responses during resting states when compared with active shooting in the VR simulator. Moreover, sympathetic signatures were found in the HRV parameters (both time and frequency) reflecting similar differences. Based on the experimental findings, we propose a psychophysiological model to aid the design of a biocybernetic adaptation layer that creates real-time modulations in simulation difficulty based on targeted physiological responses.

Enhancing Virtual Rehabilitation in Upper Limbs With Biocybernetic Adaptation: The Effects of Virtual Reality on Perceived Muscle Fatigue, Game Performance and User Experience.

Virtual rehabilitation has been used during decades to provide a more personalized, controlled, and enjoyable experience on upper-limb motor rehabilitation. Since novel virtual reality (VR) technologies are now accessible and highly immersive, the challenge for a wide dissemination of virtual rehabilitation in clinical scenarios has shifted from the hardware robustness to the software intelligence. A sophisticated technique that provides physiological intelligence to novel human-computer interaction (HCI) applications is biocybernetic adaptation. The concept emerges from the electrophysiological computing field, and it proposes using body signals to detect human states (e.g. workload or fatigue) and modulate the virtual activity accordingly. This paper evaluates the effects of using biocybernetic adaptation in a virtual rehabilitation game that aims to encourage users to exert at a desirable intensity level while interacting with the virtual environment. The system relies on surface-electromyography (sEMG) signals to detect fatigue levels in real-time and adapt the game challenge dynamically. Perceived fatigue levels, game user experience, and game performance parameters are assessed after playing the game, considering two different visualization modalities: non-immersive (conventional flat screen) and immersive (VR headset). Results revealed how the biocybernetic system in the immersive condition not only produced lower levels of perceived fatigue compared with the non-immersive, but also, created a more enjoyable and positive experience in a controlled experiment with 24 healthy subjects. Moreover, participants in the immersive condition showed a better performance in the virtual game and higher usability levels scored by users compared with the non-immersive condition. To conclude, we highlight the importance of combining novel immersive approaches with physiologically aware systems to enhance the benefits of virtual rehabilitation therapies.

An Iterative Spanning Forest Framework for Superpixel Segmentation.

Superpixel segmentation has emerged as an important research problem in the areas of image processing and computer vision. In this paper, we propose a framework, namely Iterative Spanning Forest (ISF), in which improved sets of connected superpixels (supervoxels in 3D) can be generated by a sequence of image foresting transforms. In this framework, one can choose the most suitable combination of ISF components for a given application-i.e., 1) a seed sampling strategy; 2) a connectivity function; 3) an adjacency relation; and 4) a seed pixel recomputation procedure. The superpixels in ISF structurally correspond to spanning trees rooted at those seeds. We present five ISF-based methods to illustrate different choices for those components. These methods are compared with a number of state-of-the-art approaches with respect to effectiveness and efficiency. Experiments are carried out on several datasets containing 2D and 3D objects with distinct texture and shape properties, including a high-level application, named sky image segmentation. The theoretical properties of ISF are demonstrated in the supplementary material and the results show ISF-based methods rank consistently among the best for all datasets.

Behavior Knowledge Space-Based Fusion for Copy-Move Forgery Detection.

The detection of copy-move image tampering is of paramount importance nowadays, mainly due to its potential use for misleading the opinion forming process of the general public. In this paper, we go beyond traditional forgery detectors and aim at combining different properties of copy-move detection approaches by modeling the problem on a multiscale behavior knowledge space, which encodes the output combinations of different techniques as a priori probabilities considering multiple scales of the training data. Afterward, the conditional probabilities missing entries are properly estimated through generative models applied on the existing training data. Finally, we propose different techniques that exploit the multi-directionality of the data to generate the final outcome detection map in a machine learning decision-making fashion. Experimental results on complex data sets, comparing the proposed techniques with a gamut of copy-move detection approaches and other fusion methodologies in the literature, show the effectiveness of the proposed method and its suitability for real-world applications.

Low cost implementation of a Motor Imagery experiment with BCI system and its use in neurorehabilitation.

The application of rehabilitation programs based on videogames with brain-computer interfaces (BCI) allows to provide feedback to the user with the expectation of stimulate the brain plasticity that will restore the motor control. The use of specific mental strategies such as Motor Imagery (MI) in neuroscientific experiments with BCI systems often requires the acquisition of sophisticated interfaces and specialized software for execution, which usually have a high implementation costs. We present a combination of low-cost hardware and open-source software for the implementation of videogame based on virtual reality with MI and its potential use as neurotherapy for stroke patients. Three machine learning algorithms for the BCI signals classification are shown: LDA (Linear Discriminant Analysis) and two Support Vector Machines (SVM) in order to determine which task of MI is being performed by the user in a particular moment of the experiment. All classification algorithms was evaluated in 8 healthy subjects, the average accuracy of the best classifier was 96.7%, which shows that it is possible to carry out serious neuroscientific experiments with MI using low-cost BCI systems and achieve comparable accuracies with more sophisticated and expensive devices.