This paper presents the EACare project, an ambitious multi-disciplinary collaboration with the aim to develop an embodied system, capable of carrying out neuropsychological tests to detect early signs of dementia, e.g., due to Alzheimer's disease. The system will use methods from Machine Learning and Social Robotics, and be trained with examples of recorded clinician-patient interactions. The interaction will be developed using a participatory design approach. We describe the scope and method of the project, and report on a first Wizard of Oz prototype.
One of the main motivations of developing assistive robots for the elderly is the fact that this technology can promote longer independent living @cite_23 . However, in-home experiments are still scarce because of privacy concerns and limited robot autonomy. One of the few exceptions is the work by de @cite_22 , who investigated reasons for technology abandonment in a study where 70 autonomous robots were deployed in people’s homes for about six months. Regardless of age (their participant pool ranged from 8 to 77 years), they found that the main reasons why people stopped using the robot were lack of enjoyment and perceived usefulness. These findings indicate that involving the target users from the early stages of development can be crucial for the success and acceptance of social robots.
@cite_22 @cite_23
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Research on why people refuse or abandon the use of technology in general, and robots specifically, is still scarce. Consequently, the academic understanding of people's underlying reasons for non-use remains weak. Thus, vital information about the design of these robots including their acceptance and refusal or abandonment by its users is needed. We placed 70 autonomous robots within people's homes for a period of six months and collected reasons for refusal and abandonment through questionnaires and interviews. Based on our findings, the challenge for robot designers is to create robots that are enjoyable and easy to use to capture users in the short-term, and functionally-relevant to keep those users in the longer-term. understanding the thoughts and motives behind non-use may help to identify obstacles for acceptance, and therefore enable developers to better adapt technological designs to the benefit of the users. The population of older adults in America is expected to reach an unprecedented level in the near future. Some of them have difficulties with performing daily tasks and caregivers may not be able to match pace with the increasing need for assistance. Robots, especially mobile manipulators, have the potential for assisting older adults with daily tasks enabling them to live independently in their homes. However, little is known about their views of robot assistance in the home. Twenty-one independently living older Americans (65–93 years old) were asked about their preferences for and attitudes toward robot assistance via a structured group interview and questionnaires. In the group interview, they generated a diverse set of 121 tasks they would want a robot to assist them with in their homes. These data, along with their questionnaire responses, suggest that the older adults were generally open to robot assistance but were discriminating in their acceptance of assistance for different tasks. They preferred robot assistance over human assistance for tasks related to chores, manipulating objects, and information management. In contrast, they preferred human assistance to robot assistance for tasks related to personal care and leisure activities. Our study provides insights into older adults’ attitudes and preferences for robot assistance with everyday living tasks in the home which may inform the design of robots that will be more likely accepted by older adults. Successful management of medications is critical to maintaining healthy and independent living for older adults. However, medication non-adherence is a common problem with a high risk for severe consequences [5], which can jeopardize older adults' chances to age in place [1]. Well-designed robots assisting with medication management tasks could support older adults' independence. Design of successful robots will be enhanced through understanding concerns, attitudes, and preferences for medication assistance tasks. We assessed older adults' reactions to medication hand-off from a mobile manipulator with 12 participants (68-79 years). We identified factors that affected their attitudes toward a mobile manipulator for supporting general medication management tasks in the home. The older adults were open to robot assistance; however, their preferences varied depending on the nature of the medication management task. For instance, they preferred a robot (over a human) to remind them to take medications, but preferred human assistance for deciding what medication to take and for administering the medication. Factors such as perceptions of one's own capability and robot reliability ifluenced their attitudes. Many activities of daily living, such as picking up glasses, holding a fork or opening a door, which most people do without thinking, can become insurmountable for people who have upper extremity disabilities. The alternative to asking for human help is to use some assistive devices to compensate their loss of mobility; however, many of those devices are limited in terms of functionality. Robotics may provide a better approach for the development of assistive devices, by allowing greater functionality. In this paper, we present results of a study (n=31) which objectives were to evaluate the efficacy of a new joystick-controlled seven-degree of freedom robotic manipulator and assess its potential economic benefits. Results show that JACO is easy to use as the majority of the participants were able to accomplish the testing tasks on their first attempt. The economic model results inferred that the use of the JACO arm system could potentially reduce caregiving time by 41 . These study results are expected to provide valuable data for interested parties, such as individuals with disabilities, their family or caregivers.