Search for: All records

Background As the older adult population increases there is a great need of developing smart healthcare technologies to assist older adults. Robot-based homecare systems are a promising solution to achieving this goal. This study aims to summarize the recent research in homecare robots, understand user needs and identify the future research directions. Methods First, we present an overview of the state-of-the-art in homecare robots, including the design and functions of our previously developed ASCC Companion Robot (ASCCBot). Second, we conducted a user study to understand the stakeholders’ opinions and needs regarding homecare robots. Finally, we proposed the future research directions in this research area in response to the existing problems. Results Our user study shows that most of the interviewees emphasized the importance of medication reminder and fall detection functions. The stakeholders also emphasized the functions to enhance the connection between older adults and their families and friends, as well as the functions to improve the efficiency and productivity of the caregivers. We also identified three major future directions in this research area: human-machine interface, learning and adaptation, and privacy protection. Conclusions The user study discovered some new useful functions that the stakeholders want to have and also validated the developed functions of the ASCCBot. The three major future directions in the homecare robot research area were identified. 

Social robots are coming to our homes and have already been used to help humans in a number of ways in geriatric care. This article aims to develop a framework that enables social robots to conduct regular clinical screening interviews in geriatric care, such as cognitive evaluation, falls' risk evaluation, and pain rating. We develop a social robot with essential features to enable clinical screening interviews, including a conversational interface, face tracking, an interaction handler, attention management, robot skills, and cloud service management. Besides, a general clinical screening interview management (GCSIM) model is proposed and implemented. The GCSIM enables social robots to handle various types of clinical questions and answers, evaluate and score responses, engage interviewees during conversations, and generate reports on their well-being. These reports can be used to evaluate the progression of cognitive impairment, risk of falls, pain level, and so on by caregivers or physicians. Such a clinical screening capability allows for early detection and treatment planning in geriatric care. The framework was developed and implemented on our 3-D-printed social robot. It was tested on 30 older adults with different ages, achieved satisfying results, and received their high confidence and trust in the use of this robot for human well-being assessment.