Application of artificial intelligence in active assisted living for aging population in real-world setting with commercial devices - A scoping review.

Background: The aging population is steadily increasing, posing new challenges and opportunities for healthcare systems worldwide. Technological advancements, particularly in commercially available Active Assisted Living devices, offer a promising alternative. These readily accessible products, ranging from smartwatches to home automation systems, are often equipped with Artificial Intelligence capabilities that can monitor health metrics, predict adverse events, and facilitate a safer living environment.

Preserving the self with artificial intelligence using VIPCare-a virtual interaction program for dementia caregivers.

Introduction: Assistive technology is increasingly used to support the physical needs of differently abled persons but has yet to make inroads on support for cognitive or psychological issues. This gap is an opportunity to address another-the lack of contribution from theoretical social science that can provide insights into problems that cannot be seen. Using Affect Control Theory (ACT), the current project seeks to close that gap with an artificially intelligent application to improve interaction and affect for people with Alzheimer's Disease and Related Dementias (ADRD).

Heart rate prediction with contactless active assisted living technology: a smart home approach for older adults.

Background: As global demographics shift toward an aging population, monitoring their heart rate becomes essential, a key physiological metric for cardiovascular health. Traditional methods of heart rate monitoring are often invasive, while recent advancements in Active Assisted Living provide non-invasive alternatives. This study aims to evaluate a novel heart rate prediction method that utilizes contactless smart home technology coupled with machine learning techniques for older adults.

An investigation into the use of smart home devices, user preferences, and impact during COVID-19.

With the goal of designing smart environments that can support users' physical/mental well-being, we studied users' experiences and different factors that can influence success of smart home devices through an online study conducted during and after the COVID-19 restrictions in June 2021 (109 participants) and March 2022 (81 participants). We investigated what motivates users to buy smart home devices, and if smart home devices may have the potential to improve different aspects of users' well-being. As COVID-19 emphasized a situation where people spent a significant amount of time at home in Canada, we also asked if/how COVID-19 motivated purchase of smart-home devices and how these devices affected participants during the pandemic.

VIPCare: Understanding the support needed to create affective interactions between new caregivers and residents with dementia.

Introduction: In this paper, we study the support needed by professional caregivers of those with dementia, and present a first step toward development of VIPCare, a novel application with the goal of assisting new caregivers at care-centres in interacting with residents with dementia.

Methods: A mixed-methods study including two questionnaires, two focus groups, and seven co-design sessions with 17 professional caregivers was conducted to (a) understand caregivers' challenges/approaches used to reduce negative interactions with persons with dementia, (b) identify the existing gaps in supporting information for improving such interactions, and (c) co-design the user interface of an application that aims to help improve interactions between a new professional caregiver and persons with dementia. A pre-questionnaire assessed knowledge of smartphones and attitude toward technology.

The Zoomorphic Miro Robot's Affective Expression Design and Perceived Appearance.

This article proposes design guidelines for 11 affective expressions for the Miro robot, and evaluates the expressions through an online video study with 116 participants. All expressions were recognized significantly above the chance level. For six of the expressions, the correct response was selected significantly more than the others, while more than one emotion was associated to some other expressions.