ObjectiveWe focused on identifying the requirements and needs of people suffering from Alzheimer disease and early dementia stages with relation to robotic assistants.MethodsBased on focus groups performed in two centers (Poland and Spain), we created surveys for medical staff, patients, and caregivers, including: functional requirements; human–robot interaction, the design of the robotic assistant and user acceptance aspects. Using Likert scale and analysis made on the basis of the frequency of survey responses, we identified users’ needs as high, medium, and low priority.ResultsWe gathered 264 completed surveys (100 from medical staff, 81 from caregivers, and 83 from potential users). Most of the respondents, almost at the same level in each of the three groups, accept robotic assistants and their support in everyday life. High level priority functional requirements were related to reacting in emergency situations (calling for help, detecting/removing obstacles) and to reminding about medication intake, about boiling water, turning off the gas and lights (almost 60% of answers). With reference to human–robot interaction, high priority was given to voice operated system and the capability of robotic assistants to reply to simple questions.ConclusionOur results help in achieving better understanding of the needs of patients with cognitive impairments during home tasks in everyday life. This way of conducting the research, with considerations for the interests of three stakeholder groups in two autonomic centers with proven experience regarding the needs of our patient groups, highlights the importance of obtained results.

Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) represent a major challenge for health systems within the aging population. New and better instruments will be crucial to assess the disease severity and progression, as well as to improve its treatment, stimulation, and rehabilitation. With the purpose of detecting, assessing and quantifying cognitive impairments like MCI or AD, several methods are employed by clinical experts. Syndrom Kurztest neuropsychological battery (SKT) is a simple and short test to measure cognitive decline as it assesses memory, attention, and related cognitive functions, taking into account the speed of information processing. In this paper, we present a decision system to embed in robot that can set up a productive interaction with a patient, and can be employed by the caregiver to motivate and support him while performing cognitive exercises as SKT. We propose two different interaction loops. First, the robot interacts with the caregiver in order to set up the mental and physical impairments of the patient and indicate a goal for the exercise. This is used to determine the desired robot behavior (human-centric or robot-centric, and preferred interaction modalities). Second, the robot interacts with the patient and adapts its actions to engage and assist him to complete the exercise. Two batches of experiments were conducted, and the results indicated that the robot can take profit of the initial interaction with the caregiver to provide

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The majority of socially assistive robots interact with their users using multiple modalities. Multimodality is an important feature that can enable them to adapt to the user behavior and the environment. In this work, we propose a resource-based modality-selection algorithm that adjusts the use of the robot interaction modalities taking into account the available resources to keep the interaction with the user comfortable and safe. For example, the robot should not enter the board space while the user is occupying it, or speak while the user is speaking. We performed a pilot study in which the robot acted as a caregiver in cognitive training. We compared a system with the proposed algorithm to a baseline system that uses all modalities for all actions unconditionally. Results of the study suggest that a reduced complexity of interaction does not significantly affect the user experience, and may improve task performance.