BackgroundIn Europe, the population of older people is increasing rapidly. Many older people prefer to remain in their homes but living alone could be a risk for their safety. In this context, robotics and other emerging technologies are increasingly proposed as potential solutions to this societal concern. However, one-third of all assistive technologies are abandoned within one year of use because the end users do not accept them.ObjectiveThe aim of this study is to investigate the acceptance of the Robot-Era system, which provides robotic services to permit older people to remain in their homes.MethodsSix robotic services were tested by 35 older users. The experiments were conducted in three different environments: private home, condominium, and outdoor sites. The appearance questionnaire was developed to collect the users’ first impressions about the Robot-Era system, whereas the acceptance was evaluated through a questionnaire developed ad hoc for Robot-Era.ResultsA total of 45 older users were recruited. The people were grouped in two samples of 35 participants, according to their availability. Participants had a positive impression of Robot-Era robots, as reflected by the mean score of 73.04 (SD 11.80) for DORO’s (domestic robot) appearance, 76.85 (SD 12.01) for CORO (condominium robot), and 75.93 (SD 11.67) for ORO (outdoor robot). Men gave ORO’s appearance an overall score higher than women (P=.02). Moreover, participants younger than 75 years understood more readily the functionalities of Robot-Era robots compared to older people (P=.007 for DORO, P=.001 for CORO, and P=.046 for ORO). For the ad hoc questionnaire, the mean overall score was higher than 80 out of 100 points for all Robot-Era services. Older persons with a high educational level gave Robot-Era services a higher score than those with a low level of education (shopping: P=.04; garbage: P=.047; reminding: P=.04; indoor walking support: P=.006; outdoor walking support: P=.03). A higher score was given by male older adults for shopping (P=.02), indoor walking support (P=.02), and outdoor walking support (P=.03).ConclusionsBased on the feedback given by the end users, the Robot-Era system has the potential to be developed as a socially acceptable and believable provider of robotic services to facilitate older people to live independently in their homes.
Technological advances in the robotic and ICT fields represent an effective solution to address specific societal problems to support ageing and independent life. One of the key factors for these technologies is that they have to be socially acceptable and believable to the end-users. This paper aimed to present some technological aspects that have been faced to develop the Robot-Era system, a multi-robotic system that is able to act in a socially believable way in the environments daily inhabited by humans, such as urban areas, buildings and homes. In particular, this paper focuses on two services—shopping delivery and garbage collection—showing preliminary results on experiments conducted with 35 elderly people. The analysis adopts an end-user-oriented perspective, considering some of the main attributes of acceptability: usability, attitude, anxiety, trust and quality of life
Background: More than 70% of elderly people age 80 and older are experiencing problems in personal mobility. Assistive robotics can represent a concrete support providing also a support for caregivers, clinicians and nurses by reducing their burden. Methods: A total of 20 older people and 34 caregivers (formal and informal) were interviewed in Italy and the Netherlands to investigate and prioritize their needs concerning the personal mobility domains and their attitudes towards assistive robots. The data were analysed from a user point of view by means of thematic content analysis by underlying recurrent topics. Results: The results revealed four categories of needs from the perspective of the older individuals: instrumental needs, rehabilitation needs, personal safety and indoor activities of daily life. Additionally, the results underline how personal mobility issues influence different aspects of daily life. Complementarily, three categories of caregiver needs were also distinguished: instrumental needs, rehabilitation monitoring needs and checkup needs. The highest percentage of participants showed a positive expectation towards assistive robotics. Conclusions: The results were clustered according to the robot abilities (i.e., motion, interaction, manipulation, decision support and perception abilities) as a list of functional and technical requirements that should be developed to address all the needs related to the personal mobility. Robotic developer teams that work in this context could take advantage of this research. Additionally, this work can be used as a basis for clinicians and nurses working in geriatric units to understand how the robots can support and enhance their work.The incidence of personal mobility limitations affects 35% of adults age 70 and older and 72% of people over 80 years of age. Assistive robots can support elderly people during daily tasks: they could promote their personal mobility acting as a supporting tool. The results of the needs analysis revealed four categories of needs from the perspective of the older individuals: instrumental needs, rehabilitation needs, personal safety, and indoor activities of daily life. Three categories of caregiver needs were also distinguished: instrumental needs, rehabilitation monitoring needs, and check-up needs.
Service robots are built and developed for various applications to support humans as companion, caretaker, or domestic support. As the number of elderly people grows, service robots will be in increasing demand. Particularly, one of the main tasks performed by elderly people, and others, is the complex task of cleaning. Therefore, cleaning tasks, such as sweeping floors, washing dishes, and wiping windows, have been developed for the domestic environment using service robots or robot manipulators with several control approaches. This article is primarily focused on control methodology used for cleaning tasks. Specifically, this work mainly discusses classical control and learning-based controlled methods. The classical control approaches, which consist of position control, force control, and impedance control , are commonly used for cleaning purposes in a highly controlled environment. However, classical control methods cannot be generalized for cluttered environment so that learning-based control methods could be an alternative solution. Learning-based control methods for cleaning tasks can encompass three approaches: learning from demonstration (LfD), supervised learning (SL), and reinforcement learning (RL). These control approaches have their own capabilities to generalize the cleaning tasks in the new environment. For example, LfD, which many research groups have used for cleaning tasks, can generate complex cleaning trajectories based on human demonstration. Also, SL can support the prediction of dirt areas and cleaning motion using large number of data set. Finally, RL can learn cleaning actions and interact with the new environment by the robot itself. In this context, this article aims to provide a general overview of robotic cleaning tasks based on different types of control methods using manipulator. It also suggest a description of the future directions of cleaning tasks based on the evaluation of the control approaches.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.