374 research outputs found
From images via symbols to contexts: using augmented reality for interactive model acquisition
Systems that perform in real environments need to bind the internal state to externally
perceived objects, events, or complete scenes. How to learn this correspondence has been a long
standing problem in computer vision as well as artificial intelligence. Augmented Reality provides
an interesting perspective on this problem because a human user can directly relate displayed
system results to real environments. In the following we present a system that is able to bootstrap
internal models from user-system interactions. Starting from pictorial representations it learns
symbolic object labels that provide the basis for storing observed episodes. In a second step, more
complex relational information is extracted from stored episodes that enables the system to react
on specific scene contexts
Who am I talking with? A face memory for social robots
In order to provide personalized services and to
develop human-like interaction capabilities robots need to rec-
ognize their human partner. Face recognition has been studied
in the past decade exhaustively in the context of security systems
and with significant progress on huge datasets. However, these
capabilities are not in focus when it comes to social interaction
situations. Humans are able to remember people seen for a
short moment in time and apply this knowledge directly in
their engagement in conversation. In order to equip a robot with
capabilities to recall human interlocutors and to provide user-
aware services, we adopt human-human interaction schemes to
propose a face memory on the basis of active appearance models
integrated with the active memory architecture. This paper
presents the concept of the interactive face memory, the applied
recognition algorithms, and their embedding into the robot’s
system architecture. Performance measures are discussed for
general face databases as well as scenario-specific datasets
Vision systems with the human in the loop
The emerging cognitive vision paradigm deals with vision systems that apply machine learning and automatic reasoning in order to learn from what they perceive. Cognitive vision systems can rate the relevance and consistency of newly acquired knowledge, they can adapt to their environment and thus will exhibit high robustness. This contribution presents vision systems that aim at flexibility and robustness. One is tailored for content-based image retrieval, the others are cognitive vision systems that constitute prototypes of visual active memories which evaluate, gather, and integrate contextual knowledge for visual analysis. All three systems are designed to interact with human users. After we will have discussed adaptive content-based image retrieval and object and action recognition in an office environment, the issue of assessing cognitive systems will be raised. Experiences from psychologically evaluated human-machine interactions will be reported and the promising potential of psychologically-based usability experiments will be stressed
Persoonlijkheid en e-Health: De relatie tussen persoonlijkheidstypes en voorkeuren voor persuasieve strategieën in gezondheidsbevorderende mobiele applicaties
De afgelopen tien jaar is er een toenemend aantal gezondheidsbevorderende mobiele technologieën ontwikkeld die gebruik maken van diverse persuasieve strategieën om gedrag vorm te geven of te veranderen. Echter, er is nog weinig bekend over hoe deze strategieën afgestemd kunnen worden op verschillende gebruikers(groepen) om een gewenst resultaat te bereiken en het succes van zulke technologieën te optimaliseren. Dit artikel presenteert een onderzoek naar de relatie tussen persoonlijkheid en voorkeuren voor persuasieve strategieën. De gevonden significante samenhangen lieten zien dat persuasieve gezondheidsbevorderende applicaties beter aan de behoeften van diverse gebruikers zouden kunnen voldoen door rekening te houden met hun individuele persoonlijkheidstypes
Voices of Caregivers::Key Demands Towards AI-driven Home Monitoring in Community-based Dementia Care
How to create value with unobtrusive monitoring technology in home-based dementia care: a multimethod study among key stakeholders
BACKGROUND: There is a growing interest to support extended independent living of people with dementia (PwD) via unobtrusive monitoring (UM) technologies which allow caregivers to remotely monitor lifestyle, health, and safety of PwD. However, these solutions will only be viable if developers obtain a clear picture of how to create value for all relevant stakeholders involved and achieve successful implementation. The aim of this study was therefore to explore the value proposition of UM technology in home-based dementia care and preconditions for successful implementation from a multi-stakeholder perspective. METHODS: We conducted an expert-informed survey among potential stakeholders (n = 25) to identify key stakeholders for UM technology in home-based dementia care. Subsequently, focus groups and semi-structured interviews were conducted among 5 key stakeholder groups (n = 24) including informal caregivers (n = 5), home care professionals (n = 5), PwD (n = 4), directors and managers within home care (n = 4), and policy advisors within the aged care and health insurance sector (n = 6). The sessions addressed the value proposition- and business model canvas and were analyzed using thematic analysis. RESULTS: Stakeholders agreed that UM technology should provide gains such as objective surveillance, timely interventions, and prevention of unnecessary control visits, whereas pains mainly included information overload, unplannable care due to real-time monitoring, and less human interaction. The overall design-oriented need referred to clear situation classifications including urgent care (fall- and wandering detection), non-urgent care (deviations in eating, drinking, sleeping), and future care (risk predictions). Most important preconditions for successful implementation of UM technology included inter-organizational collaboration, a shared vision on re-shaping existing care processes, integrated care ICT infrastructures, clear eligibility criteria for end-users, and flexible care reimbursement systems. CONCLUSIONS: Our findings can guide the value-driven development and implementation of UM technology for home-based dementia care. Stakeholder values were mostly aligned, although stakeholders all had their own perspective on what UM technology should accomplish. Besides, our study highlights the complexity of implementing novel UM technology in home-based dementia care. To achieve successful implementation, organizational and financial preconditions, as well as digital data exchange between home care organizations, will be important. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12877-022-03550-1
- …