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User feedback and remote supervision for assisted living with mobile robots: A field study in long-term autonomy
Applied Intelligent System Lab, Department of Computer Science, University of Milan, Milano, Italy.
University of Manchester, Manchester, UK.
Machine Perception and Intelligent Robotics Group (MAPIR), Department of System Engineering and Automation, University of Malaga, Spain; Instituto de Investigación Biomédica de Málaga–IBIMA BIONAND Platform, University of Málaga, Spain.
Örebro University, School of Science and Technology. Machine Perception and Interaction Lab.ORCID iD: 0000-0002-2385-9470
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2022 (English)In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 155, article id 104170Article in journal (Refereed) Published
Abstract [en]

In an ageing society, the at-home use of Socially Assistive Robots (SARs) could provide remote monitoring of their users' well-being, together with physical and psychological support. However, private home environments are particularly challenging for SARs, due to their unstructured and dynamic nature which often contributes to robots' failures. For this reason, even though several prototypes of SARs for elderly care have been developed, their commercialisation and wide-spread at-home use are yet to be effective. In this paper, we analyse how including the end users' feedback impacts the SARs reliability and acceptance. To do so, we introduce a Monitoring and Logging System (MLS) for remote supervision, which increases the explainability of SAR-based systems deployed in older adults' apartments, while also allowing the exchange of feedback between caregivers, technicians, and older adults. We then present an extensive field study showing how long-term deployment of autonomous SARs can be accomplished by relying on such a feedback loop to address any potential issue. To this end, we provide the results obtained in a 130-week long study where autonomous SARs were deployed in the apartments of 10 older adults, with the aim of possibly serving and assisting future practitioners, with the knowledge collected from this extensive experimental campaign, to fill the gap that currently exists for the widespread adoption of SARs.

Place, publisher, year, edition, pages
Elsevier, 2022. Vol. 155, article id 104170
Keywords [en]
Socially Assistive Robots, Long-term autonomy, Field study
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:oru:diva-100333DOI: 10.1016/j.robot.2022.104170ISI: 000823233600002Scopus ID: 2-s2.0-85132884165OAI: oai:DiVA.org:oru-100333DiVA, id: diva2:1685115
Funder
European Commission, ICT-26-2016b - GA 732158
Note

Funding agencies:

Italian PON project SI-Robotics

Project Essence SC1-PHE-CORONAVIRUS-2020-2B-GA 101016112

Available from: 2022-08-01 Created: 2022-08-01 Last updated: 2022-08-01Bibliographically approved

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Renoux, Jennifer

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