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  1. Many robot-delivered health interventions aim to support people longitudinally at home to complement or replace in-clinic treat- ments. However, there is little guidance on how robots can support collaborative goal setting (CGS). CGS is the process in which a person works with a clinician to set and modify their goals for care; it can improve treatment adherence and efficacy. However, for home-deployed robots, clinicians will have limited availability to help set and modify goals over time, which necessitates that robots support CGS on their own. In this work, we explore how robots can facilitate CGS in the context of our robot CARMEN (Cognitively Assistive Robot for Motivation and Neurorehabilitation), which delivers neurorehabilitation to people with mild cognitive impairment (PwMCI). We co-designed robot behaviors for supporting CGS with clinical neuropsychologists and PwMCI, and prototyped them on CARMEN. We present feedback on how PwMCI envision these behaviors supporting goal progress and motivation during an intervention. We report insights on how to support this process with home-deployed robots and propose a framework to support HRI researchers interested in exploring this both in the context of cognitively assistive robots and beyond. This work supports design- ing and implementing CGS on robots, which will ultimately extend the efficacy of robot-delivered health interventions. 
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  2. 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 suggest 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. 
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  3. We study the problem of online multi-task learning where the tasks are performed within similar but not necessarily identical multi-armed bandit environments. In particular, we study how a learner can improve its overall performance across multiple related tasks through robust transfer of knowledge. While an upper confidence bound (UCB)-based algorithm has recently been shown to achieve nearly-optimal performance guarantees in a setting where all tasks are solved concurrently, it remains unclear whether Thompson sampling (TS) algorithms, which have superior empirical performance in general, share similar theoretical properties. In this work, we present a TS-type algorithm for a more general online multi-task learning protocol, which extends the concurrent setting. We provide its frequentist analysis and prove that it is also nearly-optimal using a novel concentration inequality for multi-task data aggregation at random stopping times. Finally, we evaluate the algorithm on synthetic data and show that the TS-type algorithm enjoys superior empirical performance in comparison with the UCB-based algorithm and a baseline algorithm that performs TS for each individual task without transfer. 
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  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. 
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  5. null (Ed.)
    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. 
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  6. null (Ed.)
    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. 
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  7. 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. 
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  8. 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. 
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