5,037 research outputs found

    A Telerehabilitation System for the Selection, Evaluation and Remote Management of Therapies

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    Telerehabilitation systems that support physical therapy sessions anywhere can help save healthcare costs while also improving the quality of life of the users that need rehabilitation. The main contribution of this paper is to present, as a whole, all the features supported by the innovative Kinect-based Telerehabilitation System (KiReS). In addition to the functionalities provided by current systems, it handles two new ones that could be incorporated into them, in order to give a step forward towards a new generation of telerehabilitation systems. The knowledge extraction functionality handles knowledge about the physical therapy record of patients and treatment protocols described in an ontology, named TRHONT, to select the adequate exercises for the rehabilitation of patients. The teleimmersion functionality provides a convenient, effective and user-friendly experience when performing the telerehabilitation, through a two-way real-time multimedia communication. The ontology contains about 2300 classes and 100 properties, and the system allows a reliable transmission of Kinect video depth, audio and skeleton data, being able to adapt to various network conditions. Moreover, the system has been tested with patients who suffered from shoulder disorders or total hip replacement.This research was funded by the Spanish Ministry of Economy and Competitiveness grant number FEDER/TIN2016-78011-C4-2R

    Kires: a data-centric telerehabilitation system based on kinect

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    185 p.It is widely accepted that the worldwide demand for rehabilitation services. To meet these needs, there will have to be developed systems of telerehabilitation that will bring services to even the most remote locations, through Internet and related technologies.This thesis is addressing the area of remote health care delivery, in particular telerehabilitation. We present KiReS; a Kinect based telerehabilitation system which covers the needs of physiotherapists in the process of designing, managing and evaluating physiotherapy protocols and sessions and also covers the needs of the users providing them an intuitive and encouraging interface and giving useful feedback to enhance the rehabilitation process. As required for multi-disciplinary projects, physiotherapists were consulted and feedback from patients was incorporated at different development stages.KiReS aims to outcome limitations of other telerehabilitation systems and bring some novel features: 1) A friendly and helpful interaction with the system using Kinect and motivational interfaces based on avatars. 2) Provision of smart data that supports physiotherapists in the therapy design process by: assuring the maintenance of appropriate constraints and selecting for them a set of exercises that are recommended for the user. 3) Monitoring of rehabilitation sessions through an algorithm that evaluates online performed exercises and sets if they have been properly executed. 4) Extensibility, KiReS is designed to be loaded with a broad spectrum of exercises and protocols

    Ontological support for managing non-functional requirements in pervasive healthcare

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    We designed and implemented an ontological solution which makes provisions for choosing adequate devices/sensors for remote monitoring of patients who are suffering from post-stroke health complications. We argue that non-functional requirements in pervasive healthcare systems can be elicited and managed through semantics stored in ontological models and reasoning created upon them. Our contribution is twofold: we enrich the elicitation process and specification of non-functional requirements within the requirements engineering discipline and we address the pervasiveness of healthcare software systems through the way of choosing devices embedded in them and users expectations in terms of having access to pervasive services personalized to their needs

    Computer-based cognitive rehabilitation: the CoRe system

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    This work aims at providing a tool for supporting cognitive rehabilitation. This is a wide field, that includes a variety of diseases and related clinical pictures; for this reason the need arises to have a tool available that overcomes the difficulties entailed by what currently is the most common approach, that is, the so-called pen and paper rehabilitation. Methods: We first organized a big number of stimuli in an ontology that represents concepts, attributes and a set of relationships among concepts. Stimuli may be words, sounds, 2D and 3D images. Then, we developed an engine that automatically generates exercises by exploiting that ontology. The design of exercises has been carried on in synergy with neuropsychologists and speech therapists. Solutions have been devised aimed at personalizing the exercises according to both patients’ preferences and performance. Results: Exercises addressed to rehabilitation of executive functions and aphasia-related diseases have been implemented. The system has been tested on both healthy volunteers (n 1/4 38) and patients (n 1/4 9), obtaining a favourable rating and suggestions for improvements. Conclusions: We created a tool able to automate the execution of cognitive rehabilitation tasks. We hope the variety and personalization of exercises will allow to increase compliance, particularly from elderly people, usually neither familiar with technology nor particularly willing to rely on it. The next step involves the creation of a telerehabilitation tool, to allow therapy sessions to be undergone from home, thus guaranteeing continuity of care and advantages in terms of time and costs for the patients and the National Healthcare System (NHS).Postprint (published version

    Fuzzy-description logic for supporting the rehabilitation of the elderly

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    [EN] According to the latest statistics, the proportion of the elderly (+65) is increasing and is expected to double within the European Union in a period of 50 years. This ageing is due to the improvement of quality of life and advances in medicine in the last decades. Gerontechnology is receiving a great deal of attention as a way of providing the elderly with sustainable products, environments, and services combining gerontology and technology. One of the most important aspects to consider by gerontechnology is the mobility/rehabilitation technologies, because there is an important relationship between mobility and the elderly's quality of life. Telerehabilitation systems have emerged to allow the elderly to perform their rehabilitation exercises remotely. However, in many cases, the proposed systems assist neither the patients nor the experts about the progress of the rehabilitation. To address this problem, we propose in this paper, a fuzzy-semantic system for evaluating patient's physical state during the rehabilitation process based on well-known standard for patients' evaluation. Moreover, a tool called FINE has been developed that facilitates the evaluation be accomplished in a semi-automatic way first asking patients to carry out a set of standard tests and then inferencing their state by means of a fuzzy-semantic approach using the data captured during the rehabilitation tasks.This research was funded by the Spanish Ministry of Economy and Competitiveness and by EU FEDER funds under project grants TIN2016-79100-R and TIN2015-72931-EXP. It has also been funded by the Junta de Comunidades de Castilla¿La Mancha scholarship 2018-UCLM1-9131Moya, A.; Navarro, E.; Jaén Martínez, FJ.; González, P. (2020). Fuzzy-description logic for supporting the rehabilitation of the elderly. Expert Systems. 37(2):1-16. https://doi.org/10.1111/exsy.12464116372Alamri, A., Cha, J., & El Saddik, A. (2010). AR-REHAB: An Augmented Reality Framework for Poststroke-Patient Rehabilitation. IEEE Transactions on Instrumentation and Measurement, 59(10), 2554-2563. doi:10.1109/tim.2010.2057750Antoniou, G., & van Harmelen, F. (2004). Web Ontology Language: OWL. Handbook on Ontologies, 67-92. doi:10.1007/978-3-540-24750-0_4Bobillo F.(2008).Managing vagueness in ontologies. Universidad de Granada.Bobillo F. (2015).The fuzzyDL system. Retrieved July 10 2018 fromhttp://www.umbertostraccia.it/cs/software/fuzzyDL/fuzzyDL.htmlBobillo, F., Delgado, M., & Gómez-Romero, J. (2012). DeLorean: A reasoner for fuzzy OWL 2. Expert Systems with Applications, 39(1), 258-272. doi:10.1016/j.eswa.2011.07.016Bobillo, F., & Straccia, U. (2016). The fuzzy ontology reasoner fuzzyDL. Knowledge-Based Systems, 95, 12-34. doi:10.1016/j.knosys.2015.11.017Boucenna, S., Narzisi, A., Tilmont, E., Muratori, F., Pioggia, G., Cohen, D., & Chetouani, M. (2014). Interactive Technologies for Autistic Children: A Review. Cognitive Computation, 6(4), 722-740. doi:10.1007/s12559-014-9276-xCarter J. E. L.(2002).The Heath‐Carter anthropometric somatotype—Instruction manual. San Diego:State University.Chiu, Y.-H., Chen, T.-W., Chen, Y. J., Su, C.-I., Hwang, K.-S., & Ho, W.-H. (2018). Fuzzy logic-based mobile computing system for hand rehabilitation after neurological injury. Technology and Health Care, 26(1), 17-27. doi:10.3233/thc-171403Fernández-Caballero, A., González, P., & Navarro, E. (2017). Gerontechnologies - Current achievements and future trends. Expert Systems, 34(2), e12203. doi:10.1111/exsy.12203Giles, R. (1976). Łukasiewicz logic and fuzzy set theory. International Journal of Man-Machine Studies, 8(3), 313-327. doi:10.1016/s0020-7373(76)80003-xHsieh, Y.-W., Hsueh, I.-P., Chou, Y.-T., Sheu, C.-F., Hsieh, C.-L., & Kwakkel, G. (2007). Development and Validation of a Short Form of the Fugl-Meyer Motor Scale in Patients With Stroke. Stroke, 38(11), 3052-3054. doi:10.1161/strokeaha.107.490730Karime, A., Eid, M., Alja’am, J. M., Saddik, A. E., & Gueaieb, W. (2014). A Fuzzy-Based Adaptive Rehabilitation Framework for Home-Based Wrist Training. IEEE Transactions on Instrumentation and Measurement, 63(1), 135-144. doi:10.1109/tim.2013.2277536Krynicki, K., Jaen, J., & Navarro, E. (2016). An ACO-based personalized learning technique in support of people with acquired brain injury. Applied Soft Computing, 47, 316-331. doi:10.1016/j.asoc.2016.04.039Leap Motion INC. (2018).Leap Motion. Retrieved July 10 2018 fromhttps://www.leapmotion.com/Lukasiewicz, T., & Straccia, U. (2008). Managing uncertainty and vagueness in description logics for the Semantic Web. Journal of Web Semantics, 6(4), 291-308. doi:10.1016/j.websem.2008.04.001Metz, D. . (2000). Mobility of older people and their quality of life. Transport Policy, 7(2), 149-152. doi:10.1016/s0967-070x(00)00004-4Nassabi M. H. Den Akker H. &Vollenbroek‐Hutten M. (2014).An ontology‐based recommender system to promote physical activity for pre‐frail elderly 181–184.Navarro, E., González, P., López-Jaquero, V., Montero, F., Molina, J. P., & Romero-Ayuso, D. (2018). Adaptive, Multisensorial, Physiological and Social: The Next Generation of Telerehabilitation Systems. Frontiers in Neuroinformatics, 12. doi:10.3389/fninf.2018.00043OpenNI Pioneering Members. (2018).OpenNI. Retrieved July 10 2018 fromhttp://openni.ru/about/index.htmlOrbbec 3D. (2018).Orbbec Astra Pro. Retrieved July 10 2018 fromhttps://orbbec3d.com/product‐astra‐pro/Rodríguez, A. C., Roda, C., Montero, F., González, P., & Navarro, E. (2015). An Interactive Fuzzy Inference System for Teletherapy of Older People. Cognitive Computation, 8(2), 318-335. doi:10.1007/s12559-015-9356-6Shaughnessy, M., Resnick, B. M., & Macko, R. F. (2006). Testing a Model of Post-Stroke Exercise Behavior. Rehabilitation Nursing, 31(1), 15-21. doi:10.1002/j.2048-7940.2006.tb00005.xSu, C.-J., Chiang, C.-Y., & Huang, J.-Y. (2014). Kinect-enabled home-based rehabilitation system using Dynamic Time Warping and fuzzy logic. Applied Soft Computing, 22, 652-666. doi:10.1016/j.asoc.2014.04.020Velozo, C. A., & Woodbury, M. L. (2011). Translating measurement findings into rehabilitation practice: An example using Fugl-Meyer Assessment-Upper Extremity with patients following stroke. The Journal of Rehabilitation Research and Development, 48(10), 1211. doi:10.1682/jrrd.2010.10.0203W3C. (2012).OWL 2 web ontology language. Retrieved July 10 2018 from https://www.w3.org/TR/owl2‐overview/Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353. doi:10.1016/s0019-9958(65)90241-xZhang, Z., Fang, Q., & Gu, X. (2014). Fuzzy inference system based automatic Brunnstrom stage classification for upper-extremity rehabilitation. Expert Systems with Applications, 41(4), 1973-1980. doi:10.1016/j.eswa.2013.08.09

    Evidence-based careflow management systems: the case of post-stroke rehabilitation

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    AbstractThe activities of a care providers’ team need to be coordinated within a process properly designed on the basis of available best practice medical knowledge. It requires a rethinking of the management of care processes within health care organizations. The current workflow technology seems to offer the most convenient solution to build such cooperative systems. However, some of its present weaknesses still require an intense research effort to find solutions allowing its exploitation in real medical practice. This paper presents an approach to design and build evidence-based careflow management systems, which can be viewed as components of a knowledge management infrastructure each health care organization should be provided with to increase its performance in delivering high quality care by efficiently exploiting the available knowledge resources. The post-stroke rehabilitation process has been taken as a challenging care problem to assess our methodology for designing and developing careflow management systems. Then a system was co-developed with a team of rehabilitation professionals who will be committed to use it in their daily work. The system’s main goal is to deliver a full array of rehabilitation services provided by an interdisciplinary team. They are related to identify which patients are most likely to benefit from rehabilitation, manage a rehabilitation treatment plan, and monitor progress both during rehabilitation and after return to a community residence. A model of the rehabilitation process was derived from an international guideline and adapted to the local organization of work. It involves different organizational units, such as wards, rehabilitation units, clinical laboratories, and imaging services. Several organizational agents work within them and play one or more roles. Each role is defined by the goals’ set that she/he must fulfill. Special effort has been given to the design and development of a knowledge-based system for managing exceptions, which may occur in daily medical work as any deviation from the normal flow of activities. It allows either avoiding or recovering automatically from expected exceptions. When they are not expected, organizational agents, with enough power to do that, are allowed to modify the scheduled flow of activities for an individual patient under the only constraint of justifying their decision. After an intensive testing in a research laboratory, the system is now in the process of being transferred in a real working setting with the full support of its future users
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