Care Robots Research Articles

Staging the Robot: Performing Techno-Politics of Innovation for Care Robotics in Japan

In response to the challenges posed by a rapidly aging society and its associated socio-economic difficulties, the Japanese government has encouraged the adoption of AI and robotics technologies for care. Conspicuous investments in these technologies in Japan underscore the dominance of techno-politics of innovation and the advocacy for the robotization of care practices. Such narratives — disseminated by the Japanese state, industry, media, and academia — often overlook the perspectives of the expected users of these technologies. This paper, rooted in a 14-month-long ethnographic study conducted at robotics labs in Japan and the UK in 2022–2023, examines the performance and ethical implications of technoscientific imaginaries portraying Socially Assistive Robots (SARs) as already reliable, safe, and efficient. It sheds light on the intricate relationship between science, technology, the state, and society, emphasizing their use as instruments of power for state-led national development objectives. Moreover, it exposes how technology is presented, creating an illusion of efficiency while neglecting the necessity of involving society in co-designing and co-producing these technologies. The paper ultimately advocates for responsible innovation, emphasizing in particular the need for user involvement to ensure these technologies are not only more efficient and reliable, but also more accessible, inclusive, and fairer.

Comparison of assistance preferences of older adults with different functional dependence levels on domestic tasks performed by robots

BackgroundRobots have the potential to assist older adults in their home-based daily living tasks. Previous studies indicated that older adults generally accept robot assistance. However, the preferences of older adults with different functional dependence levels are lacking. These older adults encounter varying levels of difficulty in daily living and may have distinct preferences for robot assistance. This study aimed to describe and compare the preferences for robot assistance on domestic tasks in older adults with different functional dependence levels.MethodsThis cross-sectional descriptive study recruited a convenience sample of 385 older adults in Hong Kong. They were categorized as independent, partially dependent, and dependent using the Katz Index of Independence in Activities of Daily Living. Their preferences for robot assistance on a list of 48 domestic tasks under six categories were assessed through the Assistance Preference Checklist. Differences in preferences between the three groups were compared using one-way ANOVA test.ResultsFindings revealed the differences and similarities in preferences between participants with different dependence levels. In most domestic tasks under the personal care category, dependent and partially dependent older adults reported a significantly lower preferences for human assistance or a higher preferences for robot assistance (p < 0.001), compared with the independent ones. The effect size varied from medium to large (eta squared = 0.07 to 0.52). However, participants, regardless of functional dependence levels, preferred human to assist in some domestic tasks under the health and leisure activities category and preferred robot to assist in most of the domestic tasks under the chores, information management, and manipulating objects category.ConclusionsOlder adults with different levels of functional dependence exhibit different preferences for robotic assistance. To effectively use robots and assist older adults as they age, the specific preferences of older adults must be considered before designing and introducing robots in domestic care.

Contagious humans: A pandemic's positive effect on attitudes towards care robots

History has shown that attitudes toward new technologies can change abruptly following disruptive events. During the COVID-19 pandemic, it became apparent that care robots enable increased social isolation. This feature of robotic care usually raises strong ethical concerns about potentially decreased comfort for the care-dependent. In a large-scale online study, we tested the influence of the pandemic on people's affective attitudes toward care robots. In vignettes on different care scenarios, we measured participants' perceived comfort levels in situations with care robots and human caregivers while controlling for their fear of infection with a viral disease. We found that people generally feel less comfortable with a care robot than with a human caregiver. However, those who had a strong fear of being infected during the pandemic did not devalue a care robot compared to a human caregiver. While care robots remain ethically contested, this study shows that affective attitudes toward care robots may change significantly if they can address an urgent need.

Emerging epidermal electrodes towards digital health and on-skin digitalization

Epidermal electrodes can be directly attached to the human skin for high-fidelity electrophysiological monitoring owing to their preponderance in thinness, lightweight, conformability, biocompatibility, self-adhesiveness, mechanical flexibility, gas-permeability, etc . These devices have attracted immense attention due to their emerging applications in personalized health care, human/brain-machine interfaces, and soft robotics. This Perspective focuses on the most recent significant progress in this area, especially materials, properties, and applications. Challenges and prospects are summarized to underscore the unexploited areas and future directions toward digital health and on-skin digitalization.

Autonomous robots for hospital logistics and patient care: An effective way for elderly care and monitoring

Integrating robotics can revolutionize patient care, hospital logistics, and clinical diagnostics in the evolving healthcare landscape. This paper explores the development and deployment of a comprehensive robotic system capable of performing multiple critical tasks within a healthcare setting. The proposed system integrates autonomous robots for logistics, patient care, and elderly monitoring, along with AI-driven capabilities for automated diagnostic tasks. The objective is to enhance operational efficiency, improve patient outcomes, and alleviate the burden on healthcare professionals. By leveraging advanced robotics and artificial intelligence, this paper aims to create a seamless, adaptable system that can operate in various hospital environments. The study will evaluate the system's performance, safety, and impact on healthcare delivery. Through this integration, the paper envisions a future where healthcare systems are more responsive, efficient, and capable of meeting the demands of modern medical care.

Importance of Mutual Communication Through a Simple User Interface in Care Robots

Importance of Mutual Communication Through a Simple User Interface in Care Robots

From care practices to speculative vignettes-design considerations for robots in good care.

The care sector has become one of the test beds for developing robotic technologies, which have been promised to mitigate problems with aging populations and labor shortages. Despite these promises, the practical application of such technologies have been met with limited success. Apart from technical limitations, other challenges exist in the way we approach designing these technologies. Critical to the development in the care sector is understanding the complexity of the contexts, the needs and goals of diverse actors, and how these are socio-materially scaffolded. This paper presents a study conducted at the intersection of a value sensitive design and speculative design to understand these sensitivities. Based on the data collected in interviews (n = 6) and card workshops (n = 6) from care workers and residents in mobile care and care home contexts in Austria, we developed five themes capturing situated practices and understandings of good care as built on trust-developing routines, negotiations between different actors, affective and reciprocal dimension of care, care worker self-care, and material mediations. Subsequently, we created six speculative vignettes which serve as rhetorical devices to emphasize the tensions that arise with any technological intervention entering and reshaping existing care practices and relations. We argue that our approach can support robot designers to develop a rich understanding of the values and tensions in the specific context under study from the before design and development begin.

Object-Goal Navigation of Home Care Robot Based on Human Activity Inference and Cognitive Memory

Object-Goal Navigation of Home Care Robot Based on Human Activity Inference and Cognitive Memory

Anomaly detection of care robot users in standing up using CoG candidates

Anomaly detection of care robot users in standing up using CoG candidates

The perceptions of university students on technological and ethical risks of using robots in long-term care homes.

Introduction: The COVID-19 pandemic has disproportionately impacted long-term care (LTC) residents and exacerbated residents' risks of social isolation and loneliness. The unmet emotional needs of residents in LTC have driven researchers and decision-makers to consider novel technologies to improve care and quality of life for residents. Ageist stereotypes have contributed to the underuse of technologies by the older population. Telepresence robots have been found to be easy to use and do not require older adults to learn how to operate the robot but are remotely controlled by family members. The study aimed to understand the perspectives of multidisciplinary university students, including healthcare students, on using telepresence robots in LTC homes. The study would contribute to the future planning, implementation, and design of robotics in LTC. Methods: Between December 2021 and March 2022, our team conducted interviews with 15 multidisciplinary students. We employed a qualitative descriptive (QD) approach with semi-structured interview methods. Our study aimed to understand the perspectives of university students (under the age of 40) on using telepresence robots in LTC homes. Participants were invited to spend 15 min remotely driving a telepresence robot prior to the interview. A diverse team of young researchers and older adults (patient and family partners) conducted reflexive thematic analysis. Results: Six themes were identified: Robots as supplementary interaction; privacy, confidentiality, and physical harm; increased mental well-being and opportunities for interactions; intergenerational perspectives add values; staffing capacity; environmental and cultural factors influence acceptance. Conclusion: We identified a diverse range of perspectives regarding risk and privacy among participants regarding the implementation of telepresence robots in long-term care. Participants shared the importance of the voice of the resident and their own for creating more equitable decision-making and advocating for including this type of technology within LTC. Our study would contribute to the future planning, implementation, and design of robotics in LTC.

DEMENTIA CAREGIVERS’ ETHICAL CONSIDERATIONS OF HOME-MONITORING ASSISTIVE ROBOTS IN DEMENTIA CARE

Abstract Understanding dementia caregivers’ perspectives on the ethical dimensions of socially assistive robot technology is essential for its responsible and sustainable implementation. This study provides comprehensive insights into the ethical considerations of using home-monitoring assistive robots in dementia care, as seen through the perspectives of caregivers. A focus group and a follow-up interview were conducted with seven participant caregivers following their exploration of a home-monitoring assistive robot sharing their insights and experiences related to its use in dementia care. The data were subjected to thematic analysis, identifying recurring patterns and themes within the participants’ accounts. Key findings included Responsibility and Safety: Need for safety outweighed privacy concerns of caregivers. They highlighted features such as reminders, danger alerts, and integration with security systems; Autonomy and Informed Consent: our findings emphasized promoting robot technology while respecting the autonomy of individuals with dementia in deciding technology use for their own care, acknowledging the variability in their responses; Technology Maturity and Expectation Management: Caregivers expressed expectations regarding robot functionalities, highlighting the importance of technological reliability, setup processes, and technical support. Dilemma in Monitoring and Privacy: Striking a balance between monitoring and privacy emerged as a crucial concern, sparking discussions on camera usage, sensor placement, and maintaining a suitable equilibrium; Affordability Challenges: Caregivers recognized the affordability challenges associated with robot technology as a significant hurdle. The themes highlight the complexity of integrating Socially assistive robots for dementia care responsibly, thereby informing future development and implementation strategies to ensure its beneficial and ethical use.

FACTORS AFFECTING THE ACCEPTANCE OF CARE ROBOTS BY OLDER ADULTS: FOCUSING ON TECHNICAL FACTORS

Abstract Care robots have the potential to support the healthy and independent lives of older adults and alleviate the burden on care workers. However, the potential benefits of care robots can only be realized if they are accepted. Given that most older adults do not have experience with care robots and lack digital literacy, an understanding of the factors affecting the acceptance of care robots is important. The goal of this study was to analyze the factors that affect older adults’ acceptance of care robots by distinguishing the factors into sociodemographic, health, and technical factors. Data were collected from older adults aged 60 or over living in the community of Korea in 2022 (N= 506, mean age=70.11±7.74, women=54.55%). Hierarchical regression analysis was conducted to identify the factors related to acceptance of care robots. The results showed that factors influencing older adults’ acceptance of care robots were significant in sociodemographic factors (education and place of residence), health factors (self-reported health and instrumental activities for daily living), and technical factors (digital literacy and technostress). The findings indicate that interventions are needed to increase the acceptance of care robots by vulnerable groups with low levels of education, living in rural areas, and poor health conditions, and that it is also important to increase their digital literacy and lower their technostress. This study would provide valuable guidelines to stakeholders such as service providers, and policymakers to increase the acceptance of care robots and improve the quality of life of older adults.

“A Safe Space for Sharing Feelings”: Perspectives of Children with Lived Experiences of Anxiety on Social Robots

Social robots have the potential to support health and quality of life for children experiencing anxiety. We engaged families with lived experiences of pediatric anxiety in social robot development to explore desired design features, application areas, and emotion functionalities of social robots in anxiety care. We conducted 10 online co-creation workshops with (1) children with anxiety aged 7–13 (n = 24) with their family members (n = 20), and (2) youth with anxiety aged 14–18 (n = 12). Workshop participation included a validated robot expectations scale, anonymous polls, and discussion. Transcripts and text responses were subjected to content analysis. A lived experience expert group provided feedback throughout the research. Participants desired a pet-like robot with a soft texture, expressive eyes, and emotion detection to support activities of daily living. Specific anxiety-related applications included breathing exercises, managing distressing thoughts, and encouragement. Emotional alignment, the design of a robot’s emotional display, and the emotional impacts of an interaction were discussed. Privacy and the replacement of human interaction were concerns. We identify pediatric anxiety-specific design features, applications, and affective considerations for existing and future social robots. Our findings highlight the need for customizability and robust emotional functionality in social robot technologies intended to support the health and care of children living with anxiety.

Digital citizenship at school: Democracy, pragmatism and RRI

This paper presents a strategy for fostering digital citizenship at school that transcends the mere use of digital devices or instructional methods focused solely on their use. The core premise of this proposal rests on the need for an ethical-political debate concerning digitization in education. In addition, it emphasizes the need to cultivate a form of digital literacy that blends science and technology with the humanities, and erases the traditional boundaries between making and thinking. The proposed approach encapsulates two primary concerns: firstly, it asserts that digital literacy serves as a foundation for meaningful participation in digital societies; secondly, it underscores the importance of democratizing digital technologies by incorporating the perspectives, needs, and concerns of children. Drawing inspiration from the theories of pragmatism and responsible research and innovation (RRI), we present a conceptual framework for digital citizenship. To operationalize this approach, we adapt John Dewey's pragmatic model of inquiry as a method that can be applied within the school setting. This pragmatic methodology serves as a conduit for developing hands-on experience geared towards developing digital citizenship. The practical implementation of this methodology is illustrated through an actualized experience with 10- and 11-year-old children in a public primary school, regarding the issue of care robots. This paper advocates for a symbiotic relationship between theoretical understanding and practical application, and puts forward a concrete proposal for the integration of digital citizenship in schools in the form of a four-phase procedural model, based on the creation of what we term ‘the encounter’ between the educational community and the research and development community.

A Novel Prediction Method of Transfer-Assisted Action Oriented to Individual Differences for the Excretion Care Robot.

The excretion care robot's (ECR) accurate recognition of transfer-assisted actions is crucial during its usage. However, transfer action recognition is a challenging task, especially since the differentiation of actions seriously affects its recognition speed, robustness, and generalization ability. We propose a novel approach for transfer action recognition assisted by a bidirectional long- and short-term memory (Bi-LSTM) network combined with a multi-head attention mechanism. Firstly, we utilize posture sensors to detect human movements and establish a lightweight three-dimensional (3D) model of the lower limbs. In particular, we adopt a discrete extended Kalman filter (DEKF) to improve the accuracy and foresight of pose solving. Then, we construct an action prediction model that incorporates a fused Bi-LSTM with Multi-head attention (MHA Bi-LSTM). The MHA extracts key information related to differentiated movements from different dimensions and assigns varying weights. Utilizing the Bi-LSTM network effectively combines past and future information to enhance the prediction results of differentiated actions. Finally, comparisons were made by three subjects in the proposed method and with two other time series based neural network models. The reliability of the MHA Bi-LSTM method was verified. These experimental results show that the introduced MHA Bi-LSTM model has a higher accuracy in predicting posture sensor-based excretory care actions. Our method provides a promising approach for handling transfer-assisted action individual differentiation in excretion care tasks.

Graphite- and Graphene-Based Polymer Nanocomposites for Flexible Sensors and Actuators in Health Care and Soft Robotics Applications

Flexible sensors and actuators have broad applications in the fields of wearable electronics for health, sports, functional textiles, robotics and cobot applications. Graphene-or graphite-based polymer nanocomposites are promising materials for the development of soft sensors and actuators. This study investigates strain sensing properties of silicon rubber with various graphene filler concentrations (8wt%-12wt%). Current-voltage characteristics have been measured under various strains. We obtain that the sensor’s electrical resistance, for a given voltage, can be approximated by a linear fit of the logarithmic resistance as function of the extension ratio of the sensor. The obtained mechanically induced logarithmic resistor behavior of the polymer nanocomposite is highly promising for the development of electronic sensing and control. Furthermore, thin film graphite layers were investigated on highly stretchable silicone membranes.

Research on Improved Dual Channel Medical Short Text Intention Recognition Algorithm

Abstract The increasing application of medical robots in the healthcare sector underscores the critical importance of intent recognition in enhancing the interaction and assistance capabilities of these robots. Traditional intent recognition methods utilize convolutional neural networks (CNNs) for text analysis but often fall short in capturing global features, resulting in incomplete information. To address this challenge, this paper introduces an innovative approach by combining an enhanced CNN with bidirectional gated recurrent units (BiGRU) to construct a dual-channel short-text intent recognition model. This model effectively leverages both local and global features to more accurately comprehend user needs and intentions. Experimental results demonstrate that this model excels, achieving an accuracy rate of 96.68% and an F1 score of 96.67% on the THUCNews_Title dataset. In comparison to conventional intent recognition models, it exhibits significantly improved performance, thereby providing substantial support for medical robots in patient care and assisting healthcare professionals.

Contactless fall detection with radio frequency sensors for the aging population

AbstractBackgroundFalls remain a primary concern for People with Dementia (PwD) living in long‐term care facilities (LTCFs) despite many prevention and intervention methods that have tried. Current fall detection monitoring systems use wearable or vision‐based sensors. The problem is research has shown that many seniors will not consistently wear a device on their body or allow cameras in their living space because of privacy concerns that severely limit the technology user acceptance. The current standard of care is self‐report or eyewitness and therefore fall events are often undetected and underreported due to human error. Hence, we are proposing a solution to address this unmet need with a novel fall management system engineered with radio frequency (RF) sensors that do not require an individual to wear or interact with a device.MethodSix healthy participants simulated three types of falls, medial and lateral, anterior and posterior, and collapse in the Dementia and Elderly Care Robotics and Sensing (DECRS) Lab, University of Minnesota Duluth. The sensor was installed in the lab as it would be in the resident room of an LCTF. The simulated room included a bed, chair, and mattress on the floor to protect the participants.ResultOur study found that the contactless fall detection and activity system identified falls at overall sensitivity rate of 89% with a specificity of 100% in 748 tests and activity was identified at 90%. Our system identified “medial and lateral” falls with a 92% sensitivity, “anterior and posterior” falls with a 91% sensitivity, and “collapse” falls with a 75% sensitivity.ConclusionContactless sensing holds tremendous potential for monitoring individuals living with AD/ADRD and the elderly population for aging in place. LTCFs that are short staffed could benefit by utilizing tools that augment the capabilities of nurses with a continuous collection of patient‐generated health data (PGHD) and a notification system that is integrated with existing workflows and only send alerts for potential events that would require immediate attention.

고령자의 자기효능감이 돌봄로봇 수용에 미치는 영향

This study verified the effect of the older adults’ self-efficacy on acceptance of care robots according to the gender groups. This study used the data of the ’2022 Korea Older Adults Technology Acceptance Panel Survey’ conducted by the Department of Gerontology, Kyung Hee University, and multiple regression analysis was conducted by the all and gender groups on 506 older adults (203 males, 276 females) aged 60 or older nationwide. STATA 17.0 statistical program was used for analysis. As a result of the analysis by gender, in the case of men, the higher the technology self-efficacy and general self-efficacy, the higher the acceptance of care robot. In women, it was found that the higher the technology self-efficacy, the higher the education level and household income, and the better the Activities of Daily Living, the higher the acceptance of care robot. This study divided self-efficacy into general self-efficacy and technical self-efficacy and verified the impact of the two self-efficacy on the acceptance of care robots. This study has academic implications in that it divided self-efficacy into general self-efficacy and technical self-efficacy and verified the impact of the two self-efficacy on the acceptance of care robots. In addition, this study has practical implications in that it suggests separate action plans to increase the acceptance of care robots depending on the gender group of the older adults.

Contactless fall detection with radio frequency sensors for the aging population

AbstractBackgroundFalls remain a primary concern for People with Dementia (PwD) living in long‐term care facilities (LTCFs) despite many prevention and intervention methods that have tried. Current fall detection monitoring systems use wearable or vision‐based sensors. The problem is research has shown that many seniors will not consistently wear a device on their body or allow cameras in their living space because of privacy concerns that severely limit the technology user acceptance. The current standard of care is self‐report or eyewitness and therefore fall events are often undetected and underreported due to human error. Hence, we are proposing a solution to address this unmet need with a novel fall management system engineered with radio frequency (RF) sensors that do not require an individual to wear or interact with a device.MethodSix healthy participants simulated three types of falls, medial and lateral, anterior and posterior, and collapse in the Dementia and Elderly Care Robotics and Sensing (DECRS) Lab, University of Minnesota Duluth. The sensor was installed in the lab as it would be in the resident room of an LCTF. The simulated room included a bed, chair, and mattress on the floor to protect the participants.ResultOur study found that the contactless fall detection and activity system identified falls at overall sensitivity rate of 89% with a specificity of 100% in 748 tests and activity was identified at 90%. Our system identified “medial and lateral” falls with a 92% sensitivity, “anterior and posterior” falls with a 91% sensitivity, and “collapse” falls with a 75% sensitivity.ConclusionContactless sensing holds tremendous potential for monitoring individuals living with AD/ADRD and the elderly population for aging in place. LTCFs that are short staffed could benefit by utilizing tools that augment the capabilities of nurses with a continuous collection of patient‐generated health data (PGHD) and a notification system that is integrated with existing workflows and only send alerts for potential events that would require immediate attention.