A Succinct Overview of Virtual Reality Technology Use in Alzheimer’s Disease <s> Conclusion <s> Remote detection of cognitive load has many powerful applications, such as measuring stress in the workplace. Cognitive tasks have an impact on breathing and heart rate variability (HRV). We show that changes in physiological parameters during cognitive stress can be captured remotely (at a distance of 3m) using a digital camera. A study (n=10) was conducted with participants at rest and under cognitive stress. A novel five band digital camera was used to capture videos of the face of the participant. Significantly higher normalized low frequency HRV components and breathing rates were measured in the stress condition when compared to the rest condition. Heart rates were not significantly different between the two conditions. We built a person-independent classifier to predict cognitive stress based on the remotely detected physiological parameters (heart rate, breathing rate and heart rate variability). The accuracy of the model was 85% (35% greater than chance). <s> BIB001 </s> A Succinct Overview of Virtual Reality Technology Use in Alzheimer’s Disease <s> Conclusion <s> There is a pressing demand for improving the quality and efficacy of health care and social support services needed by the world’s growing elderly population, especially by those affected by mild cognitive impairment (MCI) and Alzheimer’s disease (AD)-type early-stage dementia. Meeting that demand can significantly benefit from the deployment of innovative, computer-based applications capable of addressing specific needs, particularly in the area of cognitive impairment mitigation and rehabilitation. In that context, we present here our perspective viewpoint on the use of virtual reality (VR) tools for cognitive rehabilitation training, intended to assist medical personnel, health care workers, and other caregivers in improving the quality of daily life activities of people with MCI and AD. We discuss some effective design criteria and developmental strategies and suggest some possibly useful protocols and procedures. The particular innovative supportive advantages offered by the immersive interactive cha... <s> BIB002 </s> A Succinct Overview of Virtual Reality Technology Use in Alzheimer’s Disease <s> Conclusion <s> This paper offers a portrayal of how affective computing and persuasive technologies can converge into an effective tool for interfacing biomedical engineering with behavioral sciences and medicine. We describe the characteristics, features, applications, present state of the art, perspectives, and trends of both streams of research. In particular, these streams are analyzed in light of the potential contribution of their convergence for improving computer-mediated health-care systems, by facilitating the modification of patients' attitudes and behaviors, such as engagement and compliance. We propose a framework for future research in this emerging area, highlighting how key constructs and intervening variables should be considered. Some specific implications and challenges posed by the convergence of these two technologies in health care, such as paradigm change, multimodality, patients' attitude improvement, and cost reduction, are also briefly addressed and discussed. <s> BIB003
We have presented a glimpse at VR applications for diagnostic assessment and cognitive training in MCI and AD. Instead of presenting an exhaustive account of all VR applications currently available, this mini-review has focused on representative state of the art examples of the most significant types, which we have classified into specific categories to aid in their systematic scrutiny. This analysis reveals that most VR applications for AD do not offer today VEs with sufficient levels of immersion or interaction, but simpler non-immersive or semi-immersive VR scenarios. The present general tendency to develop personalized ICTbased healthcare applications (PMC -Personalized Medicine Coalition, 2014), together with the considerable recent advances in sensor, VR, and 3D technologies, highlights the urgency of continuing the improvement of existing early stage medical VR applications. The improvements should translate in better VRbased applications for AD, with more immersive VEs, based on the latest innovative technologies available (e.g., novel HMDs, 3D smart televisions, etc.). The incorporation of emerging display and interactive technologies will enable innovative designs and implementations of more effective and versatile supportive VR applications for diagnosis and cognitive training of AD patients. We suggest that future developments of VR cognitive assessment and training applications for MCI and AD should prioritize the specificity of the particular needs of AD patients and their symptoms' evolution. VR platform designs must be able to incorporate emerging know-how and techniques, not only to better fulfill the intended specific purposes of VR applications for AD, but also to equip those future applications with adequate capacity to supply assistive support to clinicians and caregivers, to significantly contribute to the improvement of the quality of life of MCI and AD patients and their families. Advances in this field should also contemplate providing easy transfer of the applications in a simple and affordable way into in-home and nursing home environments. Furthermore, a vital feature that could make an important impact on future VR applications for AD usefulness is the capacity to timely gather and transmit relevant information BIB002 . Such information should consist, not only of ongoing patient performance data, but also of pertinent psychophysiological data, such as heart rate variability, respiration, ECG, EEG, etc., and any other multimodal information useful for affective (emotional) state recognition BIB003 and cognitive stress detection BIB001 . This data gathering feature should also include immediately accessible realtime feedback, to enable computer as well as specialist controlled intervention during the course of the session. Collected data would provide valuable up-to-date information about the patient's performance evolution, adherence to training and rehabilitation routines, as well as about synchronous physiological reactions of the patient. This feedback capacity could prove to be a valuable tool for implementing other future closed-loop applications, and also become a source of reliable data to build systematic and robust knowledge bases that are indispensable for advancing analytical research and future development.