The progressive aging of the population in developed countries is becoming a problem for healthcare systems, which must invest ever higher sums in caring for their older citizens. One of the most important issues in this area involves the physical and cognitive problems associated with growing old. In order to reduce the effect of these problems, gerontechnology has emerged as one of the most promising alternatives, especially in the field of the telerehabilitation systems developed to date. However, most of these systems do not offer therapists the facilities to design therapies adapted to individual patients. This paper proposes a novel system that supplies this need and enables therapists to create bespoke motor therapies as state diagrams and manage them efficiently in a collaborative setting. The proposed system is equipped with a fuzzy-based decision-making component that therapists can use to control transitioning between states according to variables such as fatigue and performance. Therefore, the system makes it feasible to provide older patients with the treatment they need in their own homes while its effectiveness is controlled by a Fuzzy Inference System.
Abstract. Ambient Intelligence (AmI) is a very active topic of research that is gaining more and more attention because of its characteristics, transparency and intelligence. Older people is one of the collectives that can take advantage of the use of AmI systems because, thanks to these characteristics, AmI systems can focus on older adults' real needs so that they satisfy one of their main motivations to adapt technological innovations: perceived benefits. And, perhaps, everything related to healthcare and home care is perceived by them as both valuable and beneficial. In this paper, it is presented the Multi-Agent architecture (MAS) of a healthcare AmI system to treat older people' motor impairment problems by using specific devices to control the patient's movements. In this way, the natural relationship between AmI and MAS is being widely exploited. AmI proposes the development of context-aware systems that integrate different devices to recognize the context and act accordingly. Agents provide an effective way to develop such systems since agents are reactive, proactive and exhibit an intelligent and autonomous behavior. One of the main differences of our system is that it provides therapist with support to design new therapies, to adapt them to each specific person and to control their execution instead of using a fixed set of exercises.
Tele-rehabilitation is one of the main domains where Information and Communication Technologies (ICT) have been proven useful to move healthcare from care centers to patients’ home. Moreover, patients, especially those carrying out a physical therapy, cannot use a traditional Window, Icon, Menu, Pointer (WIMP) system, but they need to interact in a natural way, that is, there is a need to move from WIMP systems to Post-WIMP ones. Moreover, tele-rehabilitation systems should be developed following the context-aware approach, so that they are able to adapt to the patients’ context to provide them with usable and effective therapies. In this work a model-based approach is presented to assist stakeholders in the development of context-aware Post-WIMP tele-rehabilitation systems. It entails three different models: (i) a task model for designing the rehabilitation tasks; (ii) a context model to facilitate the adaptation of these tasks to the context; and (iii) a bio-inspired presentation model to specify thoroughly how such tasks should be performed by the patients. Our proposal overcomes one of the limitations of the model-based approach for the development of context-aware systems supporting the specification of non-functional requirements. Finally, a case study is used to illustrate how this proposal can be put into practice to design a real world rehabilitation task.
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