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  1. Free, publicly-accessible full text available January 1, 2023
  2. Free, publicly-accessible full text available January 1, 2023
  3. Abstract Objective: The current cross-sectional study examined cognition and performance-based functional abilities in a continuing care senior housing community (CCSHC) that is comparable to other CCSHCs in the US with respect to residents’ demographic characteristics. Method: Participants were 110 older adult residents of the independent living unit. We assessed sociodemographics, mental health, neurocognitive functioning, and functional capacity. Results: Compared to normative samples, participants performed at or above expectations in terms of premorbid functioning, attention span and working memory, processing speed, timed set-shifting, inhibitory control, and confrontation naming. They performed below expectation in verbal fluency and verbal and visual learning and memory, with impairment rates [31.4% (>1 SD below the mean) and 18.49% (>1.5 SD below the mean)] well above the general population (16% and 7%, respectively). Within the cognitive test battery, two tests of delayed memory were most predictive of a global deficit score. Most cognitive test scores correlated with performance-based functional capacity. Conclusions: Overall, results suggest that a subset of older adults in the independent living sector of CCSHCs are cognitively and functionally impaired and are at risk for future dementia. Results also argue for the inclusion of memory tests in abbreviated screening batteries in this population. We suggestmore »that CCSHCs implement regular cognitive screening procedures to identify and triage those older adults who could benefit from interventions and, potentially, a transition to a higher level of care.« less
    Free, publicly-accessible full text available January 1, 2023
  4. Much research in healthcare robotics explores ex- tending rehabilitative interventions to the home. However, for adults, little guidance exists on how to translate human-delivered, clinic-based interventions into robot-delivered, home-based ones to support longitudinal interaction. This is particularly problematic for neurorehabilitation, where adults with cognitive impairments require unique styles of interaction to avoid frustration or overstimulation. In this paper, we address this gap by exploring the design of robot-delivered neurorehabilitation interventions for people with mild cognitive impairment (PwMCI). Through a multi-year collaboration with clinical neuropsychologists and PwMCI, we developed robot prototypes which deliver cognitive training at home. We used these prototypes as design probes to understand how participants envision long-term deployment of the intervention, and how it can be contextualized to the lives of PwMCI. We report our findings and specify design patterns and considerations for translating neurorehabilitation interventions to robots. This work will serve as a basis for future endeavors to translate cognitive training and other clinical interventions onto a robot, support longitudinal engagement with home-deployed robots, and ultimately extend the accessibility of longitudinal health interventions for people with cognitive impairments.
    Free, publicly-accessible full text available January 1, 2023
  5. 11% of adults report experiencing cognitive decline which can im- pact memory, behavior, and physical abilities. Robots have great potential to support people with cognitive impairments, their caregivers, and clinicians by facilitating treatments such as cognitive neurorehabilitation. Personalizing these treatments to individual preferences and goals is critical to improving engagement and adherence, which helps improve treatment efficacy. In our work, we explore the efficacy of robot-assisted neurorehabilitation and aim to enable robots to adapt their behavior to people with cognitive impairments, a unique population whose preferences and abilities may change dramatically during treatment. Our work aims to en- able more engaging and personalized interactions between people and robots, which can profoundly impact robot-assisted treatment, how people receive care, and improve their everyday lives.
  6. Dementia affects >50 million worldwide, causing progressive cognitive and physical disabilities. Its caregiving burden falls largely onto informal caregivers, who experience their own health problems, and face tremendous stress with little support–all exacerbated during COVID-19. In this paper, we present a new caregiver sup- port perspective, where the lenses of health equity and community health can shape future technology design. Through a 1.5 year long, in-depth research process with dementia community health workers, we learned how caregiving support technology can reflect key concepts in dementia community health practice. This paper makes two contributions: 1) We propose employing embodied cueing, such as imitation or action mimicry, as a communication modality that can align technology with community caregiving approaches, promote agency in people with dementia, and relieve caregiver burden, and 2) We suggest new avenues for HCI research to advance health equity in the context of dementia technology design.
  7. An estimated 11% of adults report experiencing some form of cognitive decline which may be associated with conditions such as stroke or dementia, and can impact their memory, cognition, behavior, and physical abilities. While there are no known pharmacological treatments for many of these conditions, behavioral treatments such as cognitive training can prolong the independence of people with cognitive impairments. These treatments teach metacognitive strategies to compensate for memory difficulties in their everyday lives. Personalizing these treatments to suit the preferences and goals of an individual is critical to improving their engagement and sustainment, as well as maximizing the treatment’s effectiveness. Robots have great potential to facilitate these training regimens and support people with cognitive impairments, their caregivers, and clinicians. This article examines how robots can adapt their behavior to be personalized to an individual in the context of cognitive neurorehabilitation. We provide an overview of existing robots being used to support neurorehabilitation, and identify key principles to working in this space. We then examine state-of-the-art technical approaches to enabling longitudinal behavioral adaptation. To conclude, we discuss our recent work on enabling social robots to automatically adapt their behavior and explore open challenges for longitudinal behavior adaptation. This work willmore »help guide the robotics community as they continue to provide more engaging, effective, and personalized interactions between people and robots.« less
  8. In many real-world applications, multiple agents seek to learn how to perform highly related yet slightly different tasks in an online bandit learning protocol. We formulate this problem as the ϵ-multi-player multi-armed bandit problem, in which a set of players concurrently interact with a set of arms, and for each arm, the reward distributions for all players are similar but not necessarily identical. We develop an upper confidence bound-based algorithm, RobustAgg(ϵ), that adaptively aggregates rewards collected by different players. In the setting where an upper bound on the pairwise dissimilarities of reward distributions between players is known, we achieve instance-dependent regret guarantees that depend on the amenability of information sharing across players. We complement these upper bounds with nearly matching lower bounds. In the setting where pairwise dissimilarities are unknown, we provide a lower bound, as well as an algorithm that trades off minimax regret guarantees for adaptivity to unknown similarity structure.
  9. Robots have great potential to support people with dementia (PwD) and their caregivers. They can provide support for daily living tasks, conduct household chores, provide companionship, and deliver cognitive stimulation and training. Personalizing these robots to an individual’s abilities and preferences can help enhance the quality of support they provide, increase their usability and acceptability, and alleviate caregiver burden. However, personalization can also introduce many risks, including risks to the safety and autonomy of PwD, the potential to exacerbate social isolation, and risks of being taken advantage of due to dark patterns in robot design. In this article, we weigh the risks and benefits by drawing on empirical data garnered from the existing ecosystem of robots used for dementia caregiving. We also explore ethical considerations for developing personalized cognitively assistive robots for PwD, including how a robot can practice beneficence to PwD, where responsibility falls when harm to a PwD occurs because of a robot, and how a robot can acquire informed consent from a PwD. We propose key technical and policy concepts to help robot designers, lawmakers, and others to develop personalized robots that protect users from unintended consequences, particularly for people with cognitive impairments.
  10. Robots have great potential to support people with dementia (PwD) and their caregivers. They can provide support for daily living tasks, conduct household chores, provide companionship, and deliver cognitive stimulation and training. Personalizing these robots to an individual’s abilities and preferences can help enhance the quality of support they provide, increase their usability and acceptability, and alleviate caregiver burden. However, personalization can also introduce many risks, including risks to the safety and autonomy of PwD, the potential to exacerbate social isolation, and risks of being taken advantage of due to dark patterns in robot design. In this article, we weigh the risks and benefits by drawing on empirical data garnered from the existing ecosystem of robots used for dementia caregiving. We also explore ethical considerations for developing personalized cognitively assistive robots for PwD, including how a robot can practice beneficence to PwD, where responsibility falls when harm to a PwD occurs because of a robot, and how a robot can acquire informed consent from a PwD. We propose key technical and policy concepts to help robot designers, lawmakers, and others to develop personalized robots that protect users from unintended consequences, particularly for people with cognitive impairments.