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dc.contributor.authorBallesteros Gómez, Joaquín
dc.contributor.authorUrdiales García, Cristina
dc.contributor.authorMartínez, Antonio
dc.contributor.authorPeula Palacios, Jose Manuel
dc.date.accessioned2018-10-04T10:15:30Z
dc.date.available2018-10-04T10:15:30Z
dc.date.created2018
dc.date.issued2018-10-04
dc.identifier.urihttps://hdl.handle.net/10630/16578
dc.description.abstractFall risk assessments provide a useful tool to prevent morbidity and mortality provoked by falls. Nowadays, these assessments are usually performed manually by the medical staff. This approach has three main drawbacks: i) it is time consuming, so it is only performed a few times per volunteer during their rehabilitation process; ii) it requires supervision by medical staff, so assessment at home or preferred environments is not feasible; and iii) fall risk is evaluated in a global way, so imminent fall risk is not available for decision making in assistive navigation. In this paper we propose an imminent fall risk estimator for rollator’s users that can be automatically obtained on the fly. Its main advantages are: i) it can be used in everyday conditions in any environment; ii) it does not require assistance of medical staff; and iii) it is suitable for a variety of users with minimal configuration changes. We have validated our estimator with a set of volunteers (n=10) presenting different physical and cognitive disabilities. Although the number of volunteer is limited, results show that our estimator is coherent to two traditional, well accepted assessments: the Tinetti Mobility Test and the walking speed.en_US
dc.description.sponsorshipUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.en_US
dc.language.isoengen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAparatos ortopédicosen_US
dc.subjectAyudas técnicas para personas con discapacidaden_US
dc.subject.otherRoboten_US
dc.subject.otherDiscapacidad motoraen_US
dc.subject.otherCaminadoren_US
dc.subject.otherEquilibrioen_US
dc.subject.otherSensoresen_US
dc.subject.otherSoporteen_US
dc.subject.otherControl compartidoen_US
dc.titleAutomatic fall risk assessment for challenged users obtained from a rollator equipped with force sensors and a RGB-D cameraen_US
dc.typeinfo:eu-repo/semantics/conferenceObjecten_US
dc.centroE.T.S.I. Telecomunicaciónen_US
dc.relation.eventtitleIEEE Int. Conf. on Intelligent Robots and Systemsen_US
dc.relation.eventplaceMadrid, Españaen_US
dc.relation.eventdate1-5 octubre 2018en_US


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