this goal, we propose to use the persona concept to help us build a user model based on the personas' aptitudes. The practice of user modelling emphasizes ...
User Modelling in Ambient Intelligence for Elderly and Disabled People Roberto Casas1, Rubén Blasco Marín 1, Alexia Robinet2, Armando Roy Delgado2, Armando Roy Yarza1, John McGinn2, Richard Picking 2 and Vic Grout 2 1
2
Grupo Tecnodiscap, Universidad de Zaragoza, Maria de Luna 1 50018 Zaragoza, Spain {rcasas | rblasco | armanroy}@unizar.es
Centre for Applied Internet Research (CAIR), University of Wales, NEWI, Plas Coch Campus, Mold Road Wrexham, LL11 2AW, Wales, UK {a.robinet | a.delgado | j.mcginn | r.picking | v.grout}@newi.ac.uk
Abstract Ambient Intelligence (AmI) characterizes a vision where humans are surrounded by computers. Combining ongoing technological developments (e.g. pervasive computing, wearable devices, sensor networks etc.) with user-centred design methods greatly increases the acceptance of the intelligent system and makes it more capable of providing a better quality of life in a non-intrusive way. Elderly people, with or without disabilities, could clearly benefit from this concept. Thanks to smart environments, they can experience considerable enhancements, giving them an opportunity to live more independently and for longer in their home rather than in a health-care centre. However, to implement such a system, it is essential to know for whom we are designing. Indeed, the system needs to know the users’ capabilities and behaviour in order to adapt itself and improve the interaction. Creating a user model based on the principal characteristics of the end-users will contribute positively in the development of the system’s intelligence. In this paper, we present an intelligent system with a monitoring infrastructure that will help mainly elderly users with impairments to overcome their handicap. The purpose of such a system is to create a safe and intuitive environment that will facilitate the achievement of household tasks in order to preserve independence of elderly residents for a while longer. Pursuing this goal, we propose to use the persona concept to help us build a user model based on the personas’ aptitudes. The practice of user modelling emphasizes the importance of user-centred techniques in any AmI system development and highlights the potential impacts of AmI for certain targeted groups - in this case, the elderly and people with disabilities. Keywords User models, Ambient Intelligence, elderly people, assistive interfaces, impairments.
1. Introduction With the major increase of the elderly population, particularly in Europe, there is a need for smart homes. In fact, it seems that keeping the elderly out of institutional care is the main concern in order to ease the demand made upon health services [1]. In the context of the ageing population, Ambient Intelligence (AmI) offers solutions such as health monitoring, location tracking, security etc. that could appeal to many people if only cost was not a barrier. Financially, living in a smart home is preferable as living in a nursing home is quite expensive. Smart home environments can perceive long-term changes that may cause health concerns [2]. Such a system, embedded in smart homes, could alert carers and family of any significant changes in the resident’s behaviour, diet, daily tasks or health. Fall detectors, smart pill dispensers, RFID tags on food packaging, medical equipment to test heart rate and blood pressure, GPS trackers, sensors, and so on, create a safer environment in which to live for people with sensory, cognitive or physical challenges. This convergence of technologies enables elderly people to stay at home and receive human care in a much quicker and easier manner. The application of the information society to home environments is characterized by the integration of networked computational devices into the physical context. This trend, associated with ubiquitous computing, is evolving toward systems with intelligent and context-sensitive behaviour a vision of future technological development in AmI [3]. AmI is characterized by ubiquitous computing, omnipresent communications and intelligent user interfaces. AmI systems have to be adaptive, personalized, unobtrusive and anticipatory [4,5].
As a system development trend, universal access to AmI environments brings about the accessibility and usability by user with different characteristics and needs [4]. It is widely accepted that AmI and ubiquitous computing can cope with the elderly and people with disabilities’ problems in their everyday life [6, 7, 8]. For home environment systems, a particular feature is to support daily routines, such as preparing food or operating household appliances. A typical schema of an AmI application is shown in Figure 1.
Figure 1: Example of an AmI architecture Once the contextual information is collected, there is a data fusion process whose output will be evaluated. This processed information will be used as input to the decision process, applying action rules previously set. As a result of the last process, a variety of actions will be performed. For example, “smart” appliances can learn a user’s habits, keep track of planned activities and assist in their completion [7].
2. User modelling An AmI system has to be adaptive (its behaviour can change in response to a person’s actions and environment) and personalized (its behaviour can be tailored to the user’s needs). According to these features, AmI systems for the elderly and people with disabilities have to adapt, not only to the user’s actions and environment, but also to their behaviour and frame of mind. A contextsensitive AmI system should reconfigure dynamically to accommodate the needs of users, taking into account a wide range of users and context or behaviour situations. This user-centred functioning of AmI systems has to be supported by an adequate user model. The intelligence and interface of the system have to be aware of the user abilities and limitations to interact with the person properly. The user model must include information about the person’s cognitive level, sensorial and physical disabilities. The immediate option could be achieved through a very meticulous model, fully parameterizing the person; however, this could be difficult to use in practice. 2.1. Personas The “Personas” concept was originally introduced by [9] in his publication “The inmates are running the asylum”. In this book, his definition of personas is quoted as follows: “Personas are not real people, but they represent them throughout the design process. They are hypothetical archetypes of actual users.” [9] There are two different types of personas: primary personas, which represent the main target group and secondary personas, which can use the primary personas’ interfaces but which have specific additional requirements [10, 11]. Even though personas are fictional characters, they need to be created with rigor and precision; they tell stories about potential
users in ways that allow designers to understand them and what they really want. Characteristics such as name, age, picture, profession or any other relevant information are given to each persona in order to make them look more realistic or “alive”. The most accurate way of creating personas, also called “cast of characters”, is to go through a phase of observation of real users within the environment in which the system will exist and eventually interview them with the intention of finding a common set of motivations, behaviours and goals among the end-users. However, this method is expensive and time-consuming. A low-cost approach is to create them based on Norman’s assumption personas [12] where designers use their own experience to identify the different user groups. [11] explain that these assumption-based personas help designers to be aware of legitimate information that can have an effect on the system’s design. We have defined ten data-driven personas, based on European statistics (taken from [13] and [14], which both provide key Figures on Europe). Age, education, work, family situation, impairments, technology background and so on were randomly assigned to each personas based on these EU statistics. Altogether, we have built the ten personas (of different ages, from different countries and considering European indicators) presented as follows:
Hanna, 67 years old, Sweden Francesca, 63 years old, Italy Emilie, 83 years old, Belgium Mikkel, 73 years old, Denmark Katharina, 65 years old, Germany
James, 69 years old, United-Kingdom Nikos, 62 years old, Greece Joanna, 76 years old, Poland Manolo, 60 years old, Spain Juliette, 70 years old, France
Figure 2 is an example of a persona created with the purpose of defining a common user profile.
Figure 2: A sample of a Persona In summary, personas are a valuable tool, particularly when used in scenarios where designers test and evaluate the system features for usability and effectiveness. Working with personas is one of the best ways to provide the developers with valuable insights and an efficient way of keeping the stakeholders in mind throughout the system design with the aim of making and simplifying design decisions. They “allow us to see the scope and nature of the design problem. They make it clear exactly what the user’s goals are, so we can see what the product must do…” [9]
2.2. User modelling method for adaptable systems User modelling is a field of many years of experience [15, 16, 17, 18]. Over more than twenty years, researchers have developed different techniques [19, 20, 21] to apply user modelling for both generic and specific purposes in user-centred system developments. In the case of smart homes, the user’s acceptance has become one of the key factors to determine the success of the system. If the home system aims to be universally usable, it will have to accommodate a diverse set of users [22] and adjust to fulfil their needs in case they change. With the aim of helping practitioners to improve their user modelling techniques, some researches have established rules to follow, as for example, the set of user modelling guidelines for adaptive interfaces created by [23]. Thanks to the latest advancements in wireless sensor networks, context-awareness has become an affordable reality for many different applications. The ability to sense the behaviour of the user at home gives the ability to react to changes that clash with the default values set up initially; for example if the system detects an increase in the user level of expertise it can adjust the interface to optimize it with more advanced features and if the user does not respond as expected it can lower it back to the previous level. When the system is capable of adapting in time, depending on the user needs, applying a user modelling approach becomes more valuable; it is recommendable not to fall for a design based on static user stereotypes, which could appear useful in the first instance but may fail if applied for a long period of time. As it is difficult to develop an interface that will satisfy every single user, we decided to apply a user modelling technique, based on personas, with the intention of creating an accurate, parameterized user profile that could be adjusted to resolve the User Interface (UI) features of what could be the most appropriate for a specific user at any time.
Figure 3: The user modelling method Building a robust user profile, which all the possible end-users can fit into, including all the relevant user’s characteristics, will help to find the most feasible solution from both the point of view of the
system’s intelligence and the Human-Machine Interface (HMI). The use modelling process consists of three steps: 1. Sampling: originally we used the European population, specifically the elderly and impaired as the overall audience. Following the public European statistics [13] from the EC we grouped the audience into different groups having similar characteristics (e.g. people of certain age, studies, acquisitive power, disabilities, etc). 2. Analysing: After identifying the relevant characteristics among all the data gathered. Based on probabilistic values, we apply randomly the relevant characteristics from each group into ten Personas. 3. Modelling: Finally we used the created Personas to define a practical user’s profile to be used by the AI of the system – to create user models and modify them if necessary and the HMI – to determine which features the UI will have on each user’s model. Figure 3 summarizes the method used to generate the user profile. 2.3. User profile proposed The ten personas created represent a wide range of potential users for this research project. Each of them has a handicap, which could be physical, cognitive or sensorial. Also, as the user’s limitations have an impact on the HMI, the system must adapt to the user’s impairments and attempt to define how the HMIs should display notification messages in the most comprehensible way for the user. Consequently, by taking these personas into account, we have defined a user profile (Figure 4) useful for the design of the system’s intelligence and the HMI. It is also flexible enough to deal with any types of users and will influence the decision process.
Figure 4: User profile This user profile aims to consider cognitive and sensorial disabilities of the person. Inside user level we set four different grades: not possible (0) indicates that the user is not able to use the system - of course, this could be a temporary situation. Easy (1), standard (2) and expert (3), indicates the user’s level of understanding of the system. This understanding includes different issues: e.g. knowing all the system’s possibilities and features, having memory losses, technological skills, etc. Interface indicates how the user will interact with the system. We distinguish input (humanmachine) and output (machinehuman). As input we have voice control (most
natural way of communicating helpful for those people with reduced cognitive capacities or low technological skills), haptic controls (touch screens, keyboards, remote controllers, etc) and sensoric interfaces (inertial, pupil tracking, biosensor based systems, etc.) Output from the machine will normally be graphics, text and sound. Besides the user preferences, this could also have implicit information about the user’s cognitive level; if properly designed, graphical interfaces could be very intuitive. Inside the audio category, important features are volume and pitch, key issues that can help people with visual and aural disabilities to perceive the HMI. Display includes common adjustment controls in many screens: contrast, brightness and colour settings. These characteristics, besides adapting to the ambient light and user preferences, together with magnification, might help people with visual impairments to interact with the display. 2.4. User profile for the Personas Translation of the characteristics of the personas into the values in the user profile has been done considering the features and capabilities the persona relevant to the system. If we consider again the case of Hanna, the user profile could be as follows: Hanna, 67 years old, Sweden Interface: User level: Expert - Input (haptic) - Output (graphics and sound,) Audio: Display: - Volume (0-1) - Contrast (high) - Pitch (normal) - Magnification (high) - Brightness (high) - Colour (2) We were able to apply this user model to all ten personas, which makes us believe that the model proposed can now be use in the architecture of the system being developed.
3. Use case: EasyLine + project Elderly people suffer some disabilities that get worst with years. These disabilities make it more difficult to perform tasks in a normal independent life. It is a fact that the main disabilities prevent them carrying out domestic tasks and that about a quarter of all household accidents occur in the kitchen where the appliances are key elements. The principal objective of the EasyLine+ project is to develop an AmI kitchen with advanced white goods prototypes near to market. This would increase the autonomy of the elderly and people with disabilities in their everyday activities, allowing them to live an independent life for a longer period of time. The system, being aware of the context and the user, enhances the intelligence of the white goods. With or without user cooperation, it will facilitate the use of the appliances, adapting the systems to the disabilities of the users. The system is also a learning system meaning it can detect the user’s behavioural patterns and identify any unusual changes or loss of abilities and try to compensate for them. Figure 4 shows a block diagram of the system.
Figure 5: Block diagram of EasyLine+ system In this system, user-awareness is of key importance. It has to know the user capacities in order to help him/her adequately by adapting both cognitive and HMI levels. For example, if the cognitive level is below the average, the interface will provide little information simultaneously, may use voice and graphics, eliminate advanced options, etc. If the person is totally blind the HMI will use voice or Braille communication. Context awareness is also necessary. It includes data from sensors (cupboards’ doors, presence, etc.), status of appliances and RFID information from food in the kitchen, clothes in the washing machine, etc. With all this information, the intelligent system will determine the commands sent to the white goods and establish a bidirectional communication with the HMI.
4. Conclusions The statistic predictions in Europe indicate a real global ageing of the population. This phenomenon will undoubtedly affect the market tendencies and will have some repercussions in the information society. Thanks to the appearance of many affine technologies in the home environment, it is easier to take steps towards the creation of more friendly and useful domotic systems, which will consequently increase the quality of life. Using a combination of the newest AmI technologies together in the creation of user-centred design systems will be the key for many applications oriented to a home environment. This paper has argued the importance of user-modelling involvement in the development of an AmI system. Further testing on the results will attempt to demonstrate if the solution offers an opportunity for more vulnerable people (such as elderly people or persons with disabilities) to live independently at home for a longer period of time. The implemented system will monitor the environment and user’s behaviour by analysing the data gathered through the sensor network and the user inputs in a non-intrusive way; subsequently the system will make decisions so as to eventually send notifications (some requiring an action to be taken) and, if necessary, update the user’s profile to enhance the interaction. Nevertheless, there are some important issues, not discussed fully in this paper but necessary to mention. There are some significant risks of social exclusion or other ethical concerns related to smart environments. Therefore, AmI systems in housing must support the user’s socialization, provide security and stimulate user’s physical and mental activities [25]. As a result, innovative
products need to satisfy certain requirements (e.g. affordability, user-friendliness, standards, robustness, interoperability etc.) in order to be accepted by a larger audience [26]. Another significant issue is the difficulties that software-based systems have in helping users with mobility impairments. Further improvements to solve this may include the deployment of actuators, commuters and voice synthesis control.
5. Acknowledgements This work has been supported by the EU FP6 ICT funded research project (no. 045515), “Low Cost Advanced White Goods for a Longer Independent. Life for Elderly People”
6. References 1.
2. 3. 4. 5. 6.
7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21.
22. 23. 24. 25. 26.
Roy Delgado, A., Robinet, A., McGinn, J., Grout, V., Picking, R.: Assistive Human-Machine Interfaces for Smart Homes. In: 3rd Collaborative Research Symposium on Security, E-Learning, Internet and Networking (SEIN), pp. 147--157. Information Security & Network Research Group, Plymouth (2007) Cook, D. J.: Providing for Older Adults Using Smart Environment Technologies. IEEE USA Today’s Engineer Online 5(7), (2007) IST Advisory Group: Scenarios for Ambient Intelligence in 2010. Final Report, EC. Brussels (2001 ). Emiliani, P.L., Stephanidis, C.: Universal Access to ambient Intelligence Environments: Opportunities and Challenges for People with Disabilities. IBM Systems Journal 44 (3), pp. 605--620 (2005) Belami Project, http://www.belami-project.org/definition Jorge, J.A.: Adaptive Tools for the Elderly: New Devices to Cope with Age-Induced Cognitive Disabilities. In: 2001 EC/NFS Workshop on Universal Accessibility of Ubiquitous Computing: Providing for the Elderly, pp. 66-70. ACM 2001, Alcácer do Sal (2001) Gonçalves, D.: Ubiquitous Computing and AI towards an Inclusive Society. In: 2001 EC/NFS Workshop on Universal Accessibility of Ubiquitous Computing: Providing for the Elderly, pp. 37-40. ACM 2001, Alcácer do Sal (2001) Abascal, J.: Ambient Intelligence for people with disabilities and elderly people. In: SIGCHI Workshop on Ambient Intelligence for Scientific Discovery (AISD). ACM 2004, Vienna (2004) Cooper, A.: The Inmates are Running the Asylum: Why High Tech Products Drive us Crazy and How to Restore the Sanity. Sams Publishing, USA (2006) Saffer, D.: Designing for Interaction: Creating Smart Applications and Clever Devices. New Riders, Berkeley (2007) Pruit, J., Adlin, T.: The Persona Lifecycle: Keeping People in Mind Throughout Product Design. Morgan Kaufmann, San Francisco (2006) Norman, D.: Ad-Hoc Personas & Empathetic Focus (2004) Eurostat, http://epp.eurostat.ec.europa.eu/ EIAA, http://www.eiaa.net/ Sleeman, D., Brown, J.S.: Intelligent Tutoring Systems, Academic Press, New York (1982) Petrelli, D., De Angeli, A., Convertino, G.: A User Centered Approach to User Modeling. In: 7th International Conference on User Modeling (UM99), pp. 255--264. Springer Wien, New York (1999) Fink, J., Kobsa, A., Nill, A.: Adaptable and Adaptive Information Access for All Users: Including the Disabled and the Elderly. In: 6th International Conference on User Modeling (UM97), pp. 171-173. Springer Wien, New York (1997) Carroll, J.M., Rosson, M.B.: The Paradox of the Active User. In: Carroll, J.M. (eds.) Interfacing Thought: Cognitive Aspects of Human-Computer Interaction. pp. 80--111. MIT Press/Bradford Books, Cambridge (1987) Barnard, P.: Interacting Cognitive Subsystems: A Psycholinguistic Approach to Short Term Memory. In: Ellis, A. (eds.) Progress in the Psychology of Language. pp. 197--258. Lawrence Erlbaum Associates, Hove (1985 ) Johnson, C.W.: Decision Theory and Safety-Critical Interfaces. In: Nordby, K., Helmersen, P.H., Gilmore, D., Arensen, S. (eds.) Interact ’95. pp. 127--132. Chapman Hall, London (1995) Kieras, D., Wood, S.D., Meyer, D.E.: Predictive Engineering Models based on the EPIC Architecture for Multimodal High Performance Human-Computer Interaction Tasks. ACM Transactions on Human-Computer Interaction (TOCHI) 4(3), 230-275 (1997) Shneiderman, B.: Universal Usability: Pushing Human-Computer Interaction Research to Empower Every Citizen. Communications of the ACM 43, 84--91 (2000) Kules, B.: User Modeling for Adaptive and Adaptable Software Systems. In: ACM Conference on Universal Usability. Arlington (2000) Eurostat, http://epp.eurostat.ec.europa.eu/ op den Akker, R.: How Useful is an Intelligent Computer? In: 2004 Conference on Human Factors in Computing Systems (CHI 2004). ACM 2004, Vienna (2004) Friedewald, M., Da Costa, O., Alahuhta, P., Heinonen, S.: Perspectives of Ambient Intelligence in the Home Environment. Telematics and Informatics 22(3), 221--238 (2005)
