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dc.contributor.authorCaro-Romero, Juan
dc.contributor.authorBallesteros, Joaquín
dc.contributor.authorGarcía-Lagos, Francisco 
dc.contributor.authorUrdiales, Cristina 
dc.contributor.authorSandoval-Hernandez, Francisco 
dc.date.accessioned2019-06-10T08:10:52Z
dc.date.available2019-06-10T08:10:52Z
dc.date.created2019
dc.date.issued2019-06-10
dc.identifier.urihttps://hdl.handle.net/10630/17785
dc.descriptionSlides from conferenceen_US
dc.description.abstractPersons with disabilities often rely on assistive devices to carry on their Activities of Daily Living. Deploying sensors on these devices may provide continuous valuable knowledge on their state and condition. Canes are among the most frequently used assistive devices, regularly employed for ambulation by persons with pain on lower limbs and also for balance. Load on canes is reportedly a meaningful condition indicator. Ideally, it corresponds to the time cane users support weight on their lower limb (stance phase). However, in reality, this relationship is not straightforward. We present a Multilayer Perceptron to reliably predict the Stance Phase in cane users using a simple support detection module on commercial canes. The system has been successfully tested on five cane users in care facilities in Spain. It has been optimized to run on a low cost microcontroller.en_US
dc.description.sponsorshipThis work has been supported by: Proyectos Puente and programa operativo de empleo juvenil (UMAJI58) and Plan Propio de Investigación at University of Malaga and the Swedish Knowledge Foundation (KKS) through the research profile Embedded Sensor Systems for Health (ESS−H) at Malardalen University, Sweden. Authors would like to ac- knowledge PONIENTE and LOS NARANJOS senior centers for their support during the tests. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Techen_US
dc.language.isoengen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectSensoresen_US
dc.subjectMovimientoen_US
dc.subject.otherNeural Networken_US
dc.subject.otherFinite State Machineen_US
dc.subject.otherGait analysisen_US
dc.subject.othersmart caneen_US
dc.subject.othergait monitoringen_US
dc.subject.otherSupport sensorsen_US
dc.titleA Neural Network for Stance Phase detection in smart cane usersen_US
dc.typeinfo:eu-repo/semantics/conferenceObjecten_US
dc.centroE.T.S.I. Telecomunicaciónen_US
dc.relation.eventtitleInternational Workshop on Artificial Neural Networks (IWANN)en_US
dc.relation.eventplaceLas Palmas (Gran Canaria)en_US
dc.relation.eventdate11/06/2019en_US


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