Feature extraction method for clock drawing test

Recently, the number of elderly persons with dementia has been increasing. In the past, we proposed a dementia evaluation system using daily conversations and developed the system with a conversational robot. However, the current system is not ready for practical use because it can only evaluate time/geographical orientation and short-term memory, and some methods to evaluate other orientations and functions is required as well. In this paper, we discuss a new dementia evaluation system using not only daily conversations but also drawing tests. The authors employed a Clock Drawing Test (CDT) as a new dementia evaluation test and implemented it in a tablet device. This paper discusses a feature extraction and recognition method to distinguish normal cases from dementia cases. After evaluation experiments, the proposed method could recognize 87.6% of the clock drawing images

Conversational affective social robots for ageing and dementia support

Socially assistive robots (SAR) hold significant potential to assist older adults and people with dementia in human engagement and clinical contexts by supporting mental health and independence at home. While SAR research has recently experienced prolific growth, long-term trust, clinical translation and patient benefit remain immature. Affective human-robot interactions are unresolved and the deployment of robots with conversational abilities is fundamental for robustness and humanrobot engagement. In this paper, we review the state of the art within the past two decades, design trends, and current applications of conversational affective SAR for ageing and dementia support. A horizon scanning of AI voice technology for healthcare, including ubiquitous smart speakers, is further introduced to address current gaps inhibiting home use. We discuss the role of user-centred approaches in the design of voice systems, including the capacity to handle communication breakdowns for effective use by target populations. We summarise the state of development in interactions using speech and natural language processing, which forms a baseline for longitudinal health monitoring and cognitive assessment. Drawing from this foundation, we identify open challenges and propose future directions to advance conversational affective social robots for: 1) user engagement, 2) deployment in real-world settings, and 3) clinical translation

Dementia detection using weighted direction index histograms and SVM for clock drawing test

AbstractIncreasing the number of elderly persons who have dementia is one of the severe social problems. In Japan, the Ministry of Health, Labor and Welfare expects that the number of dementia patients will be around 5 million in 2025. It is also easily estimated that they require various living supports. Therefore, early detection and prevention of dementia are important. The authors have been developing a new system for quantitative and accurate evaluation of dementia. The basic concept of our system is evaluating a patient's dementia types and progression without awareness. To realize this, we are now developing the system using daily conversations, drawings, facial expressions and so on. In this paper, we focused on Clock Drawing Test (CDT) and proposed a dementia evaluation method for CDT. In the proposed method, Weighted Direction Index Histogram Method was used to extract features from given images, and Support Vector Machine (SVM) detected dementia cases from them. As a result of evaluation experiments, the proposed method could detect 97.1% of dementia cases correctly

Care Robotics in Aging Japan: Creating Technical Solutions for the World’s Demographic Problem?

Japan is an ideal country for studying the effects of population aging that cause a wide range of societal issues, ranging from labor shortages and increasing pressure on the welfare state, to growing old age-related poverty and the need for improving productivity to sustain economic prosperity. The research question, which the scientific exploration at hand addresses, is what kind of technologies, generically referred to as robots, may be able to mitigate care problems and generate new solutions, and even further, improve the general health of the Japanese population or serve as a blueprint for other aging societies. Therefore, the case of Japan can be utilized to describe which strategies decision-makers face, as well as the challenges and opportunities caused by such a demographic transition to cope with the effects. The Japanese government prioritizes the large-scale introduction of robotics in areas of worsening labor shortages and daily life. The New Robot Strategy (NRS), a five-year policy-action plan compiled in 2015, is the new tool to coordinate the support for actors in the robotics industry, to finally leverage the predicted large market potential. Whereas policy-makers are concerned with creating a better infrastructure for the creation of versatile robots (e.g. regulative considerations, channeling of subsidies), the bureaucracy (e.g. METI, MHLW) is supposed to supervise the policy implementation and to link important public and private actors of robotics development (e.g. universities, robot-makers, research institutes). The coordination of this triangle of three stakeholder groups will be vital for the success of large-scale implementation of robotics to lessen the burden on caregivers, improve average health and wellbeing and exploit the economic potential of the silver market. Rapidly aging societies are a worldwide demographic phenomenon. Whatever feasible technical solution for care Japan invents for its own society is likely to have an impact elsewhere in the world. If the development of care robots works in Japan, it will likely be of fundamental relevance to other aging societies and may incidentally come to be one of the next export successes for Japan. It might be a chance for the government to kill two birds with one stone: taking care of Japan’s elderly and the Japanese economy at the same time. Whether there is a realistic chance this unique technical-driven approach to solving social problems to work out will be at the heart of this academic inquiry

Personalized data analytics for internet-of-things-based health monitoring

The Internet-of-Things (IoT) has great potential to fundamentally alter the delivery of modern healthcare, enabling healthcare solutions outside the limits of conventional clinical settings. It can offer ubiquitous monitoring to at-risk population groups and allow diagnostic care, preventive care, and early intervention in everyday life. These services can have profound impacts on many aspects of health and well-being. However, this field is still at an infancy stage, and the use of IoT-based systems in real-world healthcare applications introduces new challenges. Healthcare applications necessitate satisfactory quality attributes such as reliability and accuracy due to their mission-critical nature, while at the same time, IoT-based systems mostly operate over constrained shared sensing, communication, and computing resources. There is a need to investigate this synergy between the IoT technologies and healthcare applications from a user-centered perspective. Such a study should examine the role and requirements of IoT-based systems in real-world health monitoring applications. Moreover, conventional computing architecture and data analytic approaches introduced for IoT systems are insufficient when used to target health and well-being purposes, as they are unable to overcome the limitations of IoT systems while fulfilling the needs of healthcare applications. This thesis aims to address these issues by proposing an intelligent use of data and computing resources in IoT-based systems, which can lead to a high-level performance and satisfy the stringent requirements. For this purpose, this thesis first delves into the state-of-the-art IoT-enabled healthcare systems proposed for in-home and in-hospital monitoring. The findings are analyzed and categorized into different domains from a user-centered perspective. The selection of home-based applications is focused on the monitoring of the elderly who require more remote care and support compared to other groups of people. In contrast, the hospital-based applications include the role of existing IoT in patient monitoring and hospital management systems. Then, the objectives and requirements of each domain are investigated and discussed. This thesis proposes personalized data analytic approaches to fulfill the requirements and meet the objectives of IoT-based healthcare systems. In this regard, a new computing architecture is introduced, using computing resources in different layers of IoT to provide a high level of availability and accuracy for healthcare services. This architecture allows the hierarchical partitioning of machine learning algorithms in these systems and enables an adaptive system behavior with respect to the user's condition. In addition, personalized data fusion and modeling techniques are presented, exploiting multivariate and longitudinal data in IoT systems to improve the quality attributes of healthcare applications. First, a real-time missing data resilient decision-making technique is proposed for health monitoring systems. The technique tailors various data resources in IoT systems to accurately estimate health decisions despite missing data in the monitoring. Second, a personalized model is presented, enabling variations and event detection in long-term monitoring systems. The model evaluates the sleep quality of users according to their own historical data. Finally, the performance of the computing architecture and the techniques are evaluated in this thesis using two case studies. The first case study consists of real-time arrhythmia detection in electrocardiography signals collected from patients suffering from cardiovascular diseases. The second case study is continuous maternal health monitoring during pregnancy and postpartum. It includes a real human subject trial carried out with twenty pregnant women for seven months

Acceptance and Applicability of Educational Robots. Evaluating Factors Contributing to a Successful Introduction of Social Robots into Education

Reich-Stiebert N. Acceptance and Applicability of Educational Robots. Evaluating Factors Contributing to a Successful Introduction of Social Robots into Education. Bielefeld: Universität Bielefeld; 2019.The use of robots in the area of education is rapidly gaining momentum. Education faces restructuring and modernization in the forthcoming age of robots, thus necessitating research meeting the requirements of this development. In this, focusing on robots’ acceptance and applicability in educational contexts, right from the very beginning, is crucial. Therefore, this dissertation thesis has addressed this issue. It has striven to evaluate factors which contribute to a successful introduction of robots into education in a systematic manner. The strengths of the current work lie in its interdisciplinary nature, theoretical fundament, and the application of empirical and experimental methods. In practical terms, a set of studies have offered insights on how the implementation and application of robots in education could be facilitated. To do so, they operated on three different levels: First, the focus was on end users’ attitudes toward educational robots. It was shown that their attitudes and willingness to use educational robots were moderate. However, the results also indicated that the acceptance of educational robots could be fostered by the promotion of people’s general technical interest and a targeted use of robots in individual or small-group learning activities, in domains related to science and technology. In addition, it was found that user involvement in an educational robot’s design process can increase people’s general acceptance of educational robots. Second, the work focused on how to effectively design a human-robot interaction (HRI) for learning purposes by building upon the cooperative learning paradigm found in educational literature. Actual HRI experiments confirmed that a robot’s physical presence was beneficial for the learning experience, and implied that positive interdependence with a robot, social support from it, and mutual feedback about the learning process were positively related to the learning experience and the learners’ perception of the robot. Third, when tackling the issue of the ideal educational robot design, it has become clear that people’s perception of robots is influenced by context- and person-specific factors. To trigger a higher acceptance of educational robots, robotics research should match potential end users’ educational robot design concepts, for example, machinelike appearance and functionality as well as privacy and safety requirements. Taken together, this dissertation presents a sound basis for identifying issues related to the implementation and application of educational robots. However, research is still far from having completed the development of strategies for implementing and using social robots in education meaningfully. Consequently, potential future research directions will be discussed in light of the obtained results

Enhancing the Quality of Care in Long-Term Care Settings

Quality of care in long-term care is a worldwide issue given the growing numbers of dependent older people. This book presents international research, 22 varied papers, exploring quality of care from several different angles. Important themes include: (1) workforce issues, such as staff training and support; job competencies, satisfaction, and intention to stay in work; staff burnout; effects of personal- and work-related factors on quality of care; (2) intervention studies: for depressive symptoms in nursing home residents; adjustment for new residents; social and psychological support; and loneliness and isolation; (3) methodology, including: developing and testing quality indicators; measuring residents' experience of quality; and assessing partnership between staff and families; and (4) older people's experiences, such as dry eyes and using ocular lubricants; associations between length of stay and end of life care; palliative care service use and comfort at end of of life; and causes of infection-related hospitalization. The book concludes with a systematic review of the current evidence base of care home research in Brazil

Sustainable Value Co-Creation in Welfare Service Ecosystems : Transforming temporary collaboration projects into permanent resource integration

The aim of this paper is to discuss the unexploited forces of user-orientation and shared responsibility to promote sustainable value co-creation during service innovation projects in welfare service ecosystems. The framework is based on the theoretical field of public service logic (PSL) and our thesis is that service innovation seriously requires a user-oriented approach, and that such an approach enables resource integration based on the service-user’s needs and lifeworld. In our findings, we identify prerequisites and opportunities of collaborative service innovation projects in order to transform these projects into sustainable resource integration once they have ended