Cristina Roda1, Arturo Rodríguez1, Víctor López-Jaquero2, Pascual González2, and Elena Navarro2
1 Albacete Research Institute of Informatics (I3A), Albacete, Spain {cristinarodasanchez,art.c.rodriguez}@gmail.es
2 Computing Systems Department, University of Castilla-La Mancha, Albacete, Spain {victor,pgonzalez,enavarro}@dsi.uclm.es
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 moti- vations 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 architec- ture (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 effec- tive way to develop such systems since agents are reactive, proactive and exhib- it 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 us- ing a fixed set of exercises.
Keywords: ambient intelligence, intelligent system, multi-agent system, healthcare AmI system, architecture, motor impairment problems, home care, telerehabilitation.
1 Introduction
Ambient Intelligence (AmI) is a very active topic of research that is gaining more and more attention because of its characteristics, transparency for the user and intelli- gence. As Ducatel et al. [1] state, AmI promotes the development of innovative and intelligent user interfaces “embedded in an environment that is capable of recognizing and responding to the presence of different individuals in a seamless, unobtrusive and often invisible way”. This means that AmI systems become transparent as people do not perceive their complexity neither their presence, and are intelligent to react in a
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proactive and sensitive way [2] at the same time. These two characteristics have had a great impact because it has allowed technology to be used by people who, otherwise, would have been probably computer illiterate.
Older people is one of the collectives that can take advantage of the use of AmI systems. They have been traditionally reluctant to the use of technology. For instance, a household survey [3] about ICT use carried out among 1,001 people from England and Wales in 2003 showed that the use of computer was a minority activity amongst older people, because they considered it had low relevance to their daily-life. Howev- er, AmI has become a meaningful advance in this sense, as it represents “the future vision of intelligent computing where environments support the people inhabiting them”[4], that is, AmI systems focus on older adults’ real needs so that they satisfy one of their main motivations to adapt technological innovations: perceived benefits [5]. And, perhaps, everything related to healthcare is perceived by them as both valu- able and beneficial.
The quality and cost of healthcare and wellbeing services is a critical issue, lately exacerbated by an increasing aging population. For this reason, the development of healthcare AmI systems are becoming a necessity that must be shortly addressed.
There are already a wide diversity of these systems that are being used in different contexts. For instance, the development of the smart homes have allowed [6] to move away the care from hospitals to older people’ home, that is, to bring the health and social care to the patient instead of bringing the patient to the health system. They have been also developed for very different purposes from training for cognitive re- habilitation [7] till physical rehabilitation [8]. This paper focuses on an AmI system for this last kind purpose in order to treat older people’ motor impairment problems by using specific devices to control the patient’s movements. One of the main differ- ences 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. In this paper, we present the Multi-Agent [9] architecture of this system. The rest of the paper is structured as follows. After this introduction, Section 2 presents the related work. Then, in Section 3, the architecture of the rehabil- itation system is detailed. Finally, the conclusions and future work are described in Section 4.
2 Related Work
As stated by Cook et al. [10] AmI technologies are expected to be sensitive, respon- sive, adaptive, transparent, ubiquitous, and intelligent. Context-aware computing field mainly provides support to the first three features, while the area of ubiquitous computing is the one that facilitates transparency and ubiquity. However, intelligence is the most critical feature as it makes AmI systems more sensitive, responsive, adap- tive, transparent and ubiquitous. The main reason is that intelligence helps in under- standing user environments and, consequently, in providing adaptive assistance [11].
This explains why AmI entails contributions from different AI areas [12], such as machine learning [13], neural networks [11] and, specially, Multi-Agent System
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(MAS) [9]. MAS are particularly good at modeling real-world and social systems, where problems are solved in a concurrent and cooperative way without the need of reaching optimal solutions. This is why the natural relationship between AmI and MAS is being widely exploited [14]. 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 [15]. Agents react to humans based on information obtained by sensors and their knowledge about human behaviors within agent-based AmI applications [16].
One of the approaches that combines AmI and MAS is the one proposed by Corchado et al. [17]. They propose an intelligent environment (GerAmi), which inte- grates MAS and other technologies, such as mobile devices, in order to facilitate the management of geriatric residences. In this sense, they assign each nurse and doctor an autonomous healthcare agent that includes relevant information about patient loca- tions, historical data and several alarms to make a plan, so each professional can fol- low his agent’s plan, and modify it to accommodate delays or alarms. In such a case, his associated agent makes a new planning in real time with the new information.
As stated by Isern et al. [18] there are a wide range of works that apply MAS in healthcare. Some of the fields of application of these agent-based approaches are decision support systems, which are aimed to assist the professional in the execution of healthcare processes, such as treatments or diagnostics, or remote care, where they are aimed to remotely monitor the status of patients allowing pervasive care. In the latter case, there are multiple proposals focusing on (remote) home care, e.g., to monitor and assist older people at home, identifying potential dangerous situations (AmIHomCare [19]), or to provide support in the daily activities of an older person (ROBOCARE domestic environment [20]). Another similar approach is IAServ (In- telligent Aging-in-place Home care Web Services platform) [21], which produces a personalized healthcare plan to meet the desire of patients of still living in his own house. This is done by first submitting the patient’s profile to IAServ by the healthcare professional, and then this profile is converted into an ontology specifica- tion to facilitate the generation of the personalized care plan for the patient, done by an inference engine.
Other approaches are related to home care, even though they are focused on assist- ing professionals. For example, K4Care platform [22] provides an environment for all actors involved in the provision of home care services so they can remotely access all the knowledge they require, keep track of their current and pending activities, or re- quest the necessary services for their patients.
Most of the approaches mentioned before [17][19][20][21] are specifically oriented to older people as they represent a susceptible population group to be assisted regard- ing healthcare and home care. So much so that there are a wide variety of Ambient- Assisted Living (AAL) tools for older adults based on the AmI paradigm, as Rashidi and Mihailidis stated in [23]. These authors distinguish several AAL application are- as, but we are especially interested in one in particular: Therapy, namely in telerehabilitation systems as it is the main object of our research.
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Regarding to rehabilitation area, we can find some works that propose intelligent robotic systems to assist the physical rehabilitation process of the patient, e.g., for knee rehabilitation [8] or for lower limb rehabilitation [7] that, unlike the first one, it uses a MAS to detect bioelectric and physical signals through a sensor network locat- ed in the patient’s body in order to determine his movement intention and assist him in doing such a movement. Another different proposal, called OntoRis [24], offers an ontology-based rehabilitation service that the patient can use to acquire comprehen- sive information about his prescribed rehabilitation treatment, or it can simply serve as an interactive learning platform for people interested in this particular medical field. Finally, Abreu et al. [25] focus their attention on cognitive rehabilitation, name- ly on using 3D games for neuropsychiatric disorders rehabilitation. These authors propose a MAS for automatic control while the patient is playing a 3D game in order to reduce human intervention needed to manage the execution of software processes.
In this sense, our proposal has some similarities with respect to the one presented by Abreu et al. as we also propose a MAS that is able to control the performance of all the tasks that a patient is doing during a rehabilitation therapy. The main difference here is that our approach is centered on physical rehabilitation, instead of cognitive rehabilita- tion. However, unlike the works mentioned before [8][7] about physical rehabilitation where an intelligent controller is used to manage the robot behavior, our proposal is fo- cused on the performance of the tasks prescribed in a particular rehabilitation therapy.
Moreover, our proposal is centered on physical rehabilitation for older people as we have noticed that this collective has specific difficulties while doing rehabilitation exer- cises mainly because of physical (and/or psychical) problems associated with aging.
Furthermore, our system takes the advantage of using a MAS combined with AmI, as some of the works mentioned before. Therefore, the use of the AmI paradigm makes sense here when talking about older people given that AmI provides transpar- ent and intelligent mechanisms to interact with any type of software. The benefits of using AmI for older people are clearly stated before, allowing an older adult to inter- act in a transparent, simple and easy way with our rehabilitation system. This kind of intelligent system may avoid possible conflicts that arise when older people interact with software systems in a classical way as most of them are not familiarized with the use of technology at all.
Thus, this paper presents an extension and enrichment of the system proposed in a previous work [26] in order to reach the development of a complete system able to create, perform and monitor therapies for physical rehabilitation of older people dis- eases. Our proposal provides a tool that can be used by experts in the field of rehabili- tation to define a set of customized therapies and the rules that determine the behavior of the system at runtime. In this way, activities can be adapted to older people while performing a particular therapy. The creation of therapies is driven by a metamodel that defines the Domain Specific Language. Namely, in rehabilitation domain, a Therapy is composed by Activities, an Activity is composed by Tasks, and a Task is a set of Steps which can be Gestures or Postures that the older person has to perform.
Relationships between elements of the same hierarchical level can be established in order to define a sequence of Therapies, Activities or Tasks, respectively. Therefore, the therapy model can be described as a composed diagram state.
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Fig. 1. Snapshot of the user interface to define a therapy, using Microsoft PixelSense
The therapist can easily instantiate the metamodel by using a user interface devel- oped for Microsoft PixelSense (see Fig 1) and specify the movements that the older person has to perform by using Microsoft Kinect. The resulting model can be inter- preted by the system which automatically generates therapies that are performed by older people with motor impairment problems. These therapies are also supervised and monitored by the system using MS Kinect and other kind of sensors that provide information about physical conditions of the older person in a transparent way. Fur- thermore, in rehabilitation context, an older person only has to move according to the specified therapy without direct interaction with any devices, taking advantage of the AmI paradigm. Moreover, the therapist defines a set of Fuzzy Inference Systems (FISs) that allows our system to adapt the therapies and to decide the performance order of the tasks, activities and therapies at runtime. In order to support these fea- tures, a MAS has been integrated into our system, which will be described in the next section.