CARE - Extending a Digital Picture Frame with a Recommender Mode to Enhance Well-Being of Elderly People

Rist, Thomas; Seiderer, Andreas; Hammer, Stephan; Mayr, Marcus; André, Elisabeth

doi:10.4108/icst.pervasivehealth.2015.259255

Cited by 18 publications

(20 citation statements)

References 32 publications

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“…For elderly not familiar with the new technologies it will be almost completely unobtrusive, with even the coaching being rendered to the secondary users from the social network. Similar work has been done in the project CARE [16]. The former is a combination of the functionality of a digital image frame and an active recommender mode.…”

Section: Related Work 21 Loneliness Social Isolation and Living Alonementioning

confidence: 89%

Social activity modelling and multimodal coaching for active aging

Dimitrov¹,

Gospodinova

Wheeler³

et al. 2019

Proceedings of the 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments

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Social isolation is an important determinant of elderly people's health and well-being. Modern technologies could be a powerful ally in combating social isolation. However, instead of replacing human relationships they should help build them within a person's most natural social circles. This paper presents a framework for development of a technological coaching solution that can safeguard or even boost the everyday social life of the elderly. The modus operandi of this system spans from unobtrusive data collection through data processing and situation assessment of social behaviour, to selection and rendering of the most appropriate coaching actions to the elderly person including through members of their social circles. The assessment and decision-making process also integrates three important groups of external factors which influence the solution. These are: (i) personal profiles of the elderly and those members of the social circles who participate in the coaching process; (ii) objective external environment and (iii) the quality of the coaching actions. The latter is a crucial element of the system's learning abilities.

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Section: Related Work 21 Loneliness Social Isolation and Living Alonementioning

confidence: 89%

Social activity modelling and multimodal coaching for active aging

Dimitrov¹,

Gospodinova

Wheeler³

et al. 2019

Proceedings of the 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments

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“…While displays raise the problem of distraction from the robot's embodiment, they are the best trade-off to present additional information. For example, within the CARE system [19], exercises for physical and mental wellbeing are presented in the form of text and images on a digital picture frame without a robot. The authors further combine exercises and recommendations with gamification aspects to increase user appreciation [8].…”

Section: Robotic Companionsmentioning

confidence: 99%

“…They suggest reading books, airing and turning up the heating in the morning, making gymnastics, listening to music, watering the plants, drinking enough, and much more. Contents are adapted from the CARE system [19], where recommendations are presented with a digital picture frame with both text and images on the basis of context information acquired by sensors in the elderly user's domestic environment.…”

Section: Politeness Strategiesmentioning

confidence: 99%

Adaptive linguistic style for an assistive robotic health companion based on explicit human feedback

Ritschel

Seiderer

Janowski

et al. 2019

Proceedings of the 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments

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In the future, an increasing amount of social robots will be found in our domestic environments to support and facilitate everyday life. Especially in the context of assistive, health-related support, more and more robotic products are on the way to the consumer market. While one can observe that many commercial efforts are put into the visual appearance, embodiment, motion and sound of companion robots, this paper focuses on the robot's conversational skills. We investigate how to adapt the robot's linguistic style to the individual user's preferences. This includes two forms of robot persona in the context of information retrieval tasks and games, as well as politeness with regard to recommendations. Therefore, we present an autonomous companion robot, which adapts its spoken language based on explicit human feedback. It provides several functionalities for information retrieval, reminders, communication and entertainment as well as health-related recommendations. Results of the in-situ study with elderly participants indicate that human preferences vary with regard to the robot's employed politeness strategies. Furthermore, the participants preferred assistant persona over companion persona in the information retrieval context. CCS CONCEPTS • Computing methodologies → Intelligent agents; Reinforcement learning; • Human-centered computing → Haptic devices;

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“…Context-Aware Recommender system for the Elderly (CARE) presents a study that combines the functionality of a digital image frame with an active recommender mode [26]using data derived from personal user and incorporating contextual data collected with sensors integrated into the users' mobile phone. The recommendations are chosen on the basis of context information acquired by sensors embedded in the user's environment, and a well-being model to carefully decide on at which point in time what kind of activity is most suitable to suggest.…”

Section: Context-aware Healthcare Recommender Systemsmentioning

confidence: 99%

“…Table 1 provides a summary of the health Recommender Systems and their recommendation techniques. Although the need for context-based, individually tailored health information in personal health records is expressed [6], [26], [33], Context-Aware Healthcare Recommender Systems has received little attention in health Recommender Systems literature. To enhance the reliability of the recommendations, the framework is said to be capable of protecting the users from dishonest users and physicians.…”

Section: Context-aware Healthcare Recommender Systemsmentioning

confidence: 99%

A Survey of Context-Aware Healthcare Recommender Systems

2017

IJSR

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Abstract:The advent of social web has transformed users from sheer consumers to ardent producers of information. The rapid growth coupled with the reliance on the internet for information ranging from e-commerce, e-government and social networks has led to infobesity. Recommender Systems have become essential tool to aid users to overcome the problem of information overload and provide personalized relevant suggestions. The use of Recommender Systems have been successful in both industry and academia. Recommender systems have also found their way into the Healthcare domain with varied applications.Despite the growing practical demand, a few challenges are hindering the adoption ofRecommender Systems in the Healthcare.The purpose of this study is to understand the trend of Recommender Systems applications inHealthcareby examining the published literature, and to provide practitioners and researchers with insight and future direction.We conducted a focused literature review to identify research papers on Recommender Systems in the Healthcare in the last ten years. The key findings of this survey is that the application of Recommender Systems inHealthcare is evolving. The incorporation of contextual information is limited although it has been suggested as a key ingredient to improving the quality of the recommendations and the accuracy of the predications.It is important to point out that aside from the common filtering problems of sparsity, cold start and scalability, a socio-technical issue of privacy, security and trust is emerging.Collaborative Filtering technique is predominantly in use in Healthcare Recommender Systems. Incorporation of contextual information is limited.Privacy, security and trust concerns may hinder the widespread use and acceptance of the Healthcare Recommender Systems.

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CARE - Extending a Digital Picture Frame with a Recommender Mode to Enhance Well-Being of Elderly People

Cited by 18 publications

References 32 publications

Social activity modelling and multimodal coaching for active aging

Social activity modelling and multimodal coaching for active aging

Adaptive linguistic style for an assistive robotic health companion based on explicit human feedback

A Survey of Context-Aware Healthcare Recommender Systems

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