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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. 

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. 

Over 22 million people worldwide are affected by Parkinson's disease, stroke, and Bell's palsy (BP), which can cause facial paralysis (FP). People with FP have trouble having their expressions understood: both laypersons and clinicians have difficulty understanding them and often misinterpret them, which can result in poor social interactions and poor care delivery. One way to address this problem is through better education and training, of which computational tools may prove invaluable. Thus, in this paper, we explore how to build systems that can recognize and synthesize asymmetrical facial expressions. We introduce a novel computational model of asymmetric facial expressions for BP, which we can synthesize on either virtual and robotic patient simulators. We explore this within the context of clinical education, and built a patient simulator with synthesized FP in order to help clinicians perceive facial paralysis in patients. We conducted both computational and human-focused evaluations of the model, including the feedback from clinical experts. Our results suggest that our BP model is realistic, and comparable to the expressions of people with BP. Thus, this work has the potential to provide a practical training tool for clinical learners to better understand the expressions of people with BP. Our work can also help researchers in the facial recognition community to explore new methods for asymmetric facial expression analysis and synthesis.