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   <dc:title>Navigation system using passive collaborative control adapted to user profile for a rollator device</dc:title>
   <dc:creator>Ballesteros-Gómez, Joaquín</dc:creator>
   <dc:contributor>Urdiales-García, Amalia Cristina</dc:contributor>
   <dc:subject>Robótica - Tesis doctorales</dc:subject>
   <dcterms:abstract>Rollators provide autonomy to persons with mobility impairments. These platforms can be used while people perform their Activities of Daily Living in order to provide support and/or balance. Also, they can be used during the rehabilitation process to strengthen the lower limbs or to provide balance before users can progress to canes or crutches.&#xd;
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Rollators have a limited set of personalization options, but they are usually related to the users' body size. Hence, people who need extra typically have to choose a wheelchair instead. This transition to a wheelchair limits users' movements and it increases their disuse syndrome because they do not exercise their lower limbs. Hence, it is a priority to extent the use of rollator platforms as much as possible by adapting help to people who can not use a conventional rollator on their own.&#xd;
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Technological enhancements can be added to rollator to expand their use to a larger population. For example, force sensors on handlebars provide information about users' weight bearing. This information can be used during rehabilitation to control their partial weight-bearing. Encoders on wheels may also provide useful information about the walking speed, which is a well know estimator of fall risk. In addition to monitorization, motors can be attached to the wheels for assistance, e.g. to reduce effort while ascending slopes.&#xd;
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This thesis focuses on creating a navigation system for a robotized rollator, which includes weight bearing sensors, encoders and wheel motors. The navigation system relies on passive collaborative control to continuously combine user and system commands in a seamless way. The main contribution of this work is adaptation to the user's needs through continuous, transparent monitorization and profile estimation.</dcterms:abstract>
   <dcterms:dateAccepted>2018-05-22T07:04:30Z</dcterms:dateAccepted>
   <dcterms:available>2018-05-22T07:04:30Z</dcterms:available>
   <dcterms:created>2018-05-22T07:04:30Z</dcterms:created>
   <dcterms:issued>2017-06</dcterms:issued>
   <dc:type>doctoral thesis</dc:type>
   <dc:identifier>https://hdl.handle.net/10630/15779</dc:identifier>
   <dc:language>eng</dc:language>
   <dc:rights>open access</dc:rights>
   <dc:publisher>UMA Editorial</dc:publisher>
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