Developing Ambient Intelligence Applications
for the Assisted Living of the Elderly
Edgardo Avilés-López, J. Antonio García-Macías, and Ismael Villanueva-Miranda
CICESE Research Center, Mexico
{avilesl, jagm, ivillanu}@cicese.mx
ABSTRACT
Ambient Assisted Living is a research area which has emerged to assist individuals in performing their everyday activities with the help of ambient intelligence technology. To ac-complish this, technological aspects such as context acquisi-tion, device integraacquisi-tion, networking, and social aspects such as accessibility, usability, privacy, and others, play an im-portant role. These aspects become crucial when assisting the elderly, because they present very specific and demand-ing requirements. In this paper, we present work in some application scenarios conducted within our research group, focused on supporting the elderly. The implementation of these scenarios is possible through the use of our proposed technological infrastructure consisting of two main parts: (1) a physical platform constituted by sensors, actuators, RFID tags, and other devices, and (2) a software platform pro-viding Web services with basic blocks such as voice recogni-tion, indoor localizarecogni-tion, and others, that can be composed to construct more sophisticated services.
Categories and Subject Descriptors
D.2.6 [Software Engineering]: Programming Environments; D.2.11 [Software Engineering]: Software Architectures; J.3 [Computer Applications]: Life and Medical Sciences
Keywords
ambient assisted living, sentient visors, ubiquitous comput-ing, smart home, mobility assistance, audio sensor networks, pervasive health, ubiquitous mashups
1.
INTRODUCTION
The ubiquitous computing vision [22] describes an aug-mented world full of intelligent devices that comprises a fine grained distributed network. Ambient intelligence (AmI) goes further by studying how to allow environments to be sensitive, adaptive, and responsive to the presence of people, in order to support them in their day to day experiences [2].
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We are interested in ambient assisted living (AAL) systems with a special focus on supporting the elderly support (also known as Home Care Systems [6]). The physical and men-tal declination that comes with the natural aging process is one of the reasons why ambient intelligence aims to come up with new alternatives and innovative tools to improve their lifestyle by helping them to maintain certain degree of independence.
The rest of the paper is organized as follows. In section 2 we talk about the motivation of our work and explain why the support for the elders is a good case study for ambi-ent intelligence. Next, in section 3 we discuss some of the related work. In section 4, we present our technological in-frastructure, which comprises both a physical and a software platform to build services on top of it. Then, in section 5, we discuss the cases of study developed with our contributions and talk about how they were used to successfully accom-plish the specific requirements of each case. In section 6, we talk about the lessons learned and finally, we conclude with some final remarks and outline the future work.
2.
MOTIVATION
Augmented spaces are particularly helpful for the elders, and, as average life expectancy increases worldwide with an aging population constantly growing [20], we can expect a greater demand for services and applications oriented to-ward assisting them. These demands include providing ser-vices for those who suffer from various illnesses (both mental and physical) such as: diabetes, arthritis, senile dementia, alzheimer, heart-related diseases among many others. The emergence of new types of mobile and embedded comput-ing devices, developments in wireless networkcomput-ing and com-munications, smart sensors, and others, gives us the tools and methods to come up with innovative services and appli-cations to better assist users, and therefore, improve their lifestyle. Examples of those can be seen in related areas such as tele-homecare [11], pervasive health (or ubi-health) [15], and mobile tele-monitorization [13].
We believe that support for the elderly through AAL in-frastructures is the most naturally appropriate. Not just because of its evident social impact, but because of the char-acteristics of its special requirements. We summarize this by analysing the characteristics shown by a traditional ambient intelligence system, according to Oppermann et al. [16]:
• Invisible. Since there is the need to track and auto-matically identify the users of an augmented space, they are required to wear a digital tag that can take the form of a watch, a purse, glasses, be embedded in
clothes, and others. In some nursing homes (like the one we applied our cases of study in), residents are re-quired to wear an identification purse, allowing us to replace it by a digitally augmented version.
• Mobile. As users move around their homes, or nursing rooms, they need to carry with them some of the com-putation or sensing needed to support their activities. This becomes crucial when dealing with an application that helps the user to take medications, or one in which factors as blood pressure, sugar levels, an others, must be constantly monitored and controlled.
• Spontaneous communication. In a scenario in which constant connectivity is required and the number of networked elements is high, an ad hoc network infras-tructure is needed, in order to guarantee connectivity in spite of node failures.
• Heterogeneous an hierarchicalAugmenting a space usu-ally involves the integration and coordination of sev-eral different kind of system nodes and devices. A hierarchical organization of them can help in building a richer encapsulated functionality. For example, a sensor node can provide raw sensorial data, a sensor network on the other hand, can constitute a sort of “macroscope” we can use to identify composite activi-ties, while hiding us the individual nodes coordination. • Context-aware. Three steps are involved: first, raw context data such as temperature, identity, localiza-tion, and others, is acquired; second, context is mod-eled, communicated through the system, and then in-terpreted; third, context-inferred data is used to per-form an action or cast a change in the system. This requirement becomes really important for the elder be-cause some systems usually rely on personal context such as blood pressure, sugar levels, and others. Con-text in such situations should be communicated in fast, reliable and opportune ways.
• Anticipatory. The system must take actions on behalf of the user displaying proactive behaviour. For exam-ple, if the elder user has suffered a fall, the system must alert the nursing home personnel.
• Natural communication with users. This requirement is specially important with the elderly. This kind of user presents interaction problems with traditional mech-anisms such as mouse, keyboard, text on screens, etc. A new approach must be taken when displaying data, selecting between options, and requesting input data. Possible approaches could be the use of voice com-mands and visual components. This is a topic of ac-tive research (for instance, see [12]). Also, additional research is needed to accomplish proper usability. • Natural interaction with users by means of devices they
are used to. Users of ambient intelligent systems should consider the use of everyday items as ways of interac-tion with the users.
• Adaptive. Systems must be designed to be able to re-solve abnormal or exceptional situations autonomously (if possible), and be prepared to support other scenar-ios the user could need to incorporate or change after
time. Also, as technology rapidly evolves, we must de-sign system architectures in such way that new devices and communication technologies can be exploited (as a research instance, see [10]).
Along with the above listed characteristics, there are other features:
• Elder users, and specially, elders hosted in a nursing home are used to stay in well known spaces, for in-stance, their bedrooms, a game area, a TV room, and so on. Augmentation of their spaces can, therefore, be carefully planned.
• Detractors of ambient assisted living usually mention privacy as a major concern on supporting scenarios. The elderly, in cases where they are being hosted in homes for their care taking, are constantly being mon-itored. Their activities are recorded (usually video taped), their localisation tracked, and so on. Also, as the augmented spaces are basically confined spaces, privacy can be relaxed to some extent and not take primary importance on research designs for the elders.
3.
RELATED WORK
AlarmNET is an AAL system designed to provide assis-tance through a smart healthcare environment. It specifi-cally targets assisted-living residents and others whom may benefit from continuous remote healthcare monitoring [21]. AlarmNET’s network manages an audit trail and continuous medical history while preserving resident’s autonomy, com-fort and privacy, thus improving security and quality of life. The AlarmNET system [24] is mainly based on a wireless sensor network that provides readings of temperature, light, pulse and blood oxygenation. It also features a circadian activity rhythm analysis program. Similar to AlarmNET, our platform offers a number of different services to support the elder, while we also offer a sensorial infrastructure, we provide other services and tools such as physical actuators, an augmented reality framework, and others, that allow the implementation of many different application scenarios.
CenceMe, is a personal sensing system that enables users to share their activities through the Web, particularly through the use of social networks [7]. The CenceMe system can iden-tify the status of a user in terms of his activity (sitting, walk-ing, etc.), disposition (happy, sad, etc.), habits (at work, at he gym, etc.), and surroundings (environmental conditions). While our infrastructure also includes a behavioral analysis service that can be used to infer if a user is seated, walk-ing, or if he has fallen, we are taking this further by offering a complete solution for ambient assisted living applications that use that information to enable further support.
EMI2lets [8] is a platform that facilitates the development and deployment of AmI scenarios. Users of the platform in-teract with smart objects in the environment through the use of mobile devices. The platform helps on discovering locally available services, deploying new software represen-tatives of everyday objects, and creating software controllers to be run on mobile devices. Our contributions also consider the interaction with augmented objects through the use of the sentient visor toolkit, which is not just focused on the interaction with the object controllers, but on providing a natural way for the user to interact with them. Or focus is also more general, to provide a platform for the description,
discovery, and integration of services that produce compo-sitions to facilitate the development of AmI applications, independently of aspects such as the targeted platform, the programming language, or the scenario where the final ap-plication is deployed.
4.
TECHNOLOGICAL INFRASTRUCTURE
Before presenting and discussing the different scenarios we have explored to support the elders, we have to talk about our technological infrastructure. We have designed and im-plemented a platform featuring basic services and tools to allow the easy composition of hardware and software func-tionality. In the following subsections we introduce and de-tail each of these services and tools.
4.1
Services
A service in our platform consists of a RESTful [9] Web service which exposes its functionality by describing what common interfaces it implements, and that notifies its avail-ability within the environment by using the discovery mech-anism provided by the platform. We have designed a number of common interfaces for those services, including context-acquisition, behaviour analysis, indoor localization, meta-data management, and others.
Once the services are registered in the platform, a devel-oper can create compositions by defining which services are involved and the application flow between them. The com-position files are sent to one of the execution engines avail-able in the environment, which are in charge of handling the requests and data involved on the execution of the described functionality. The developer can alternatively use our plat-form framework to directly interact with the discovery and description mechanisms to handle its own composition im-plementation.
Some services in our platform have to notify data as it happens, i.e., they follow an event-based communication. To support this, we have included the publish/subscribe model. For instance, if you want to be notified about new tag read-ings of an RFID service, you must first subscribe to it and specify the parameters of how and when you will be alerted. As the components of our platform are mostly RESTful Web services, a notification of data usually consists of a POST request to a target subscribed URI. The notification can be configured with aggregation operators.
We have implemented a working prototype of our platform that includes server, desktop, and mobile versions of the execution engine. To conduct our cases of study, we also implemented the next basic services:
Indoor Localization This service estimates the user’s po-sition based on the signal’s intensity of surrounding Wi-Fi access points. This approach has been widely used in other work that requires knowledge of user/object location in an indoor environment [14]. The service has to be trained before the location can be estimated. Training is done by measuring Wi-Fi signal strengths at different points of the area to be covered and asso-ciating the measured signal strength with a specific lo-cation. This process is also associated to the platform being used, additional training is required for each of the different platforms to be used. When an estimation request is issued, the service looks into the measure-ments stored during training and estimates a position
by using a custom heuristic based on the similitude of signal strengths. During experimental tests and using between 3 and 10 access points, we managed to get an average error of 4 meters, which is enough to sup-port the localization needed in our cases of study (as explained in Section 5).
Wireless Sensor Networks This service provides a high level abstraction to different wireless sensor platforms, allowing the easy acquisition of raw context. The type of context being acquired depends on the sensorial ca-pability of the nodes in the network. Our setup con-sisted of 8 MicaZ motes and 10 Telos B motes (both from Crossbow, Inc.) which provided us with envi-ronmental (light, temperature, humidity, noise) and user (audio capture, accelerometer) context. In some special cases we attached a sensor node to a user to monitor his behaviour by tracking the changes of the accelerometer data. The current implementation of this service is based on a previous work [3]. This ser-vice also offers asynchronous notifications following the publish/subscribe model. An entity should first sub-scribe itself by describing the desired sensor type, the sensing area of interest, the time window, and the ag-gregation operator to be applied to sensor readings be-tween that window (e.g., the hourly average tempera-ture of a room).
RFID Readers This service provides notifications of RFID tag readings. The intention of this service is to serve as a way for automatic identification of objects and users. We use this service in thetaking medications applica-tion scenario (see subsecapplica-tion 5.1) where the medicine bottles should be identified.
Video Feeds The current implementation of this service takes the form of a Web widget, a visual component that can be easily embedded in other Web pages or systems. However, the design of the service specifies the delivering of a video stream that can be used in conjunction with image processing techniques to offer richer behavioral analysis.
Voice Commands Voice commands are a very natural way of interaction where user effort is minimized. With this service, we enable voice commands on an environ-ment by provisioning it with a wireless sensor network where nodes are equipped with a constantly open mi-crophone. The reliability of the voice recognition net-work is extremely important, as the sensor nodes are highly prone to failures. We have deployed nodes re-dundantly, often overlapping coverage areas, in this way, if a node fails, another one can perform its func-tions without disrupting overall system functionality. The main functionality that this service exposes is the notification of a new command identified by the net-work. To detect a command, the sensor nodes first capture sound, and if it is within the human voice range then they forward it to a base station, where the sound is analyzed to determine if it is a command. The base station is the service provider as well (please refer to [17] for in-depth details).
Speech Synthesis Each instance of a provider for this ser-vice is directly associated with a speaker. On a request,
the message received is synthesized to an audible form and played on the service’s speaker. The synthesized speech is another very natural way of interaction with the users, as we don’t require them to look a display to acknowledge information.
Actuators This kind of services allows the developers to perform a physical reaction in the environment. This reaction can take the form of lights, motors, sounds, and other actuators. We have implemented services to trigger single servo-motors that can be used to open doors or windows. We designed these services in a generic way so the developers can easily adapt them to their own application scenarios.
Input Devices We have implemented services to allow sim-ple input, such as buttons, sliders, touch events, rota-tors, and others using Phidget hardware [1]. As stated before, one of the common characteristics of an AmI system is to provide natural interaction with the users by means they are used to. We designed the input de-vice serde-vices with a general focus, so they can be easily extended to include other types of devices, for exam-ple, infrared sensors, electric scales, and others. This type of services are event-based, any entity interested on receiving the event of a new user input must first subscribe.
Other Services There are other services implemented to allow specialized actions. One example of these ser-vices is the context-processor which receives sensor reading sent by a wireless sensor service and then, af-ter applying a certain logic (through scripts), gener-ates new events. We have used the context-processor service in the intelligent care monitoring application scenario. More services can easily be added to the platform. The necessary steps are: first, develop it on any programming language (as long as it exposes its functionality through the RESTful approach), sec-ond, describe which platform interfaces are being im-plemented by the service in a service description file, and third, to register it on the service discovery mech-anism (by using one of the many different provided tools).
4.2
Tools
Along with the design and implementation of the services in the platform, we have a couple of tools built on top of our infrastructure to better support the development of AAL ap-plications for the elderly. The most important one is a sen-tient visor toolkit that can be used to develop applications that allow the interaction of users with augmented objects in a very natural way.
4.2.1
Sentient Visors
User interaction on augmented assisted living environ-ments is intended to be the most natural possible, ideally not requiring the use of traditional means such as displays, keyboards, and so on. When designing interaction of elder users with augmented environments, we must think on new approaches that offer users of advanced age with alternatives that require no previous knowledge nor training to use. El-ders need a clear and simple human-interaction model that
Virtual Object Representation Physical Object Automatic Object Identification Context
Acquisition Reality ScenarioAugmented
Additional Services
Figure 1: The application scenario of a sentient vi-sor.
presents them filtered, condensed, and summarized informa-tion based on their personal profile and preferences. Follow-ing the idea introduced by the ambient devices concept [23] we have been working on what we call sentient visors [5]. A sentient visor is a device carried by the user that basically presents three characteristics: a screen, a back-facing cam-era (pointing to the back of the screen), and some sensing capabilities. A tablet computer or a modern smartphone can be currently used as sentient visors [25].
The scenario of use for a sentient visor is as follows (see Fig. 1). To start the interaction, the user points the camera (and the device) to the augmented object of interest or to an area in the environment according to what the application scenario involves. The device then automatically identifies what is being focused, captures whatever additional context is required by the application, and then sends a request for an augmented reality scenario to a service in our platform to be shown on the device. The augmented reality scenario could include some sort of basic interaction such as touching one of the objects on the screen. The device then sends as many additional requests to the data providers as the appli-cation flow needs. The final scenario is that the user sees an augmented version of the physical reality and in this way, he is able to interact with the objects he uses in his everyday life just by pointing the sentient device to it. We use this tool on thetaking medicationsscenario (see subsection 5.1).
4.2.2
Other Development Tools
We have developed other tools aimed to better assisting the developers of ambient intelligence systems by provid-ing higher abstraction layers on top of our platform. For instance, while the services are already designed to facili-tate the development, the Mashup Editor (see [4] for more details) allows the development of applications just by drag-ging graphical representations of the services to form com-positions to be run on the execution engines, which is also handled by the editor. Other tools provide Web widgets to present and manage information, they are also ready to be updated as new notifications arrive, automatically handling the low level communication.
5.
APPLICATION SCENARIOS
We now present some scenarios that we came across dur-ing our work with ambient-assisted livdur-ing technologies to
Figure 2: The taking medications application show-ing the two possible results.
promote safer and healthier lifestyles for elders. These sce-narios have also been very useful for highlighting the benefits of our proposed platform for developing AmI applications.
5.1
Taking Medications
Mr. Smith is a senior of advanced age who needs to take many different medications on a daily basis. Some-times he forgets to take the medications, and maybe worse, he takes the same dosis of pills twice or thrice because he just wants to be sure he took it. This situation can lead to adverse, sometimes very serious consequences and it is known to cause many problems worldwide [18]. Mr. Smith is physically impaired, so it is impossible for him to visit his physician except on certain special occasions. Without frequent visits, the medical examinations are conducted via phone calls and sometimes his physician changes the doses by altering the number of pills to be taken or by setting a different intaking schedule, which makes the problem even worse because Mr. Smith must remember the proper doses of each of the many bottles of pills he takes during the day. To support this scenario, we first identified which objects in Mr. Smith’s daily life we could augment to allow him to use the system the way he is used to. We decided to use the bottles of pills. We attached an RFID tag to each of the bottles to be able to individually identify them. Later, we analyzed which services were already available in our plat-form so we could use them. They were the RFID reader (for bottle identification) and the speech synthesis services (to alert the user). Then, we identified what custom services were needed to complete the functionality for this scenario. Two services were specially implemented: a service that pro-vides a Web page to input the doses specifications to be used by the physician, and a service that keeps record of the doses and that sends the alerts to the user when the schedule of a doses is met. Finally, to wrap all the services and imple-ment the final application, we used the sentient visor tool to create an augmented reality scenario that shows if the doses from a bottle should be taken or if the user should wait until the next doses.
The new scenario with the support of our contributions is as follows: Mr. Smith takes many different medications
Figure 3: Desktop version of the intelligent video monitoring application.
during the day. When he wants to be sure he is on schedule with the doses, he takes his mobile device (e.g., smartphone) and points it to each of the bottles of pills. In the screen, a green or red glow appears around the bottle indicating whether he needs to take or to wait for a doses; if he needs to take it, the number of pills also appears on the screen, if he must wait, an animated countdown appears telling him how many minutes he should wait until the next doses (see Fig. 2). When he must take the doses, a button appears, to be pushed after he takes it. When Mr. Smith hasn’t used the smartphone and the schedule is not met, the alert service notifies the voice synthesis service to send an user notification telling him the number of pills he must imme-diately take. Sometimes, Mr. Smith’s physician logs in into the doses specification Web page to change the schedule or to check the progress of his patient, but this is all transpar-ent to Mr. Smith, as the system takes care of updates and other details in the background.
5.2
Intelligent Care Monitoring
Mrs. Jones is a caregiver at a nursing facility. As part of her activities, she must regularly check the residents to look if they require assistance. To avoid her to walk around the many different rooms, they installed a closed circuit camera system for her. Now, when she is in the office she can keep an eye on the residents by watching a monitor that shows all the camera feeds in the building. Sometimes, one of the residents falls, if she is not careful with the inspection, the elder could spent a long time until she realized of the accident. Given the fact that during the aging process there is a considerable decline in motor capabilities, older adults become more prone to falls. While Mrs. Jones is not the only caregiver, they have many other activities, and after some time, the camera feeds are no longer being used for monitoring, but for having a record of what happens inside the building.
To implement this scenario, we provided each of the resi-dents with wearable sensor nodes, in this way, we are aware of their location by using the perceived Wi-Fi signal strengths and infer their behaviour by analyzing the readings of an embedded accelerometer. We get the locations of the users thanks to the indoor localization service which we had to
Wi-Fi Access Points
Wireless Sensor Network
User Controller and Localization Badge Door Controller
Figure 4: Infrastructure implemented for the voice-enabled assistance scenario.
train before using it. We took readings in 2,500 positions, in each location we took 10 signal strength readings. We equipped the nursing home with 5 Wi-Fi extra access points to enhance the location estimation. Each of the nodes inter-acts directly with the indoor localization service to estimate their individual position and then reports it to a centralized service. The behaviour analysis is provided by accelerome-ter readings, each sensor reports segments of data each 10 seconds to a centralized service, data is analyzed there to conclude if the user is walking, seated, or if he has fallen. The implementation of the centralized service in both cases is a specialization of the context-processor service provided by our platform. We use the video feeds of each camera to provide a way for the caregivers to confirm that the alerts are real events and avoid false positives. Finally we imple-mented an application, with desktop and mobile versions, that uses the functionality provided by the centralized loca-tion and behavior analysis services to support this scenario. The new scenario is as follows: Mrs. Jones must check the residents regularly. When she is in the office, in her desktop computer she can see a map of the building showing the activity and location of each resident. She can click on one of the resident icons and then the system shows the video feed of the nearest camera that has that location on sight. When one of the residents suffers a fall, the desktop system shows her a visual and audible alert highlighting the location and the video feed so she can be sure the alert is not a false positive and then go directly to attend the resident (see Fig. 3). When she and other caregivers are not in the office, they receive a fall alert containing the location and an image captured from the nearest camera. Residents can also use a button in the sensor node to trigger an alert.
5.3
Voice-Enabled Assistance
Mr. Brown is a patient who uses an electric-powered wheelchair to move himself around the building. Very often, he requires assistance to open doors or when he needs to go to the bathroom. When nobody is around he usually finds himself waiting until a caregiver or another patient assists him.
To support this scenario, we are using the indoor local-ization, voice commands, input device, and actuators ser-vices. We designed a custom input device (and service) that presents two buttons for the use of the elder. The
applica-tion (a service composiapplica-tion) is running in the environment, when the user pushes the “command” button, the applica-tion identifies him with its locaapplica-tion and waits for the voice command service to detect a command, when it receives the request for an action, it acts accordingly. When the user presses the “help” button, the system sends a notification to a caregiver in the form of a message containing the location of the patient (see Fig. 4).
The voice-powered scenario is as follows: Mr. Brown is an elder who presents physical disadvantages that makes him use a wheelchair. When he needs to open a door, he presses the “command” button and says “open”, then the door in front of him opens to let him enter. Once inside, he presses the button again and says “close”, then the door closes be-hind him. When Mr. Brown requires assistance from a caregiver, he presses the “help” button, then a caregiver re-ceives his request containing his location so he can be found rapidly.
6.
LESSONS LEARNED
Working with assisted living systems for supporting the elderly is a very interesting and challenging task; while the research offers a good degree of complexity, the results have an important and quick social impact. When designing the application scenarios, we found many issues that required very well planed privacy management, for instance, the in-telligent care monitoring scenario, where the activities of the residents of a nursing house are always being tracked. We think that, as long as the application is managed lo-cally, privacy of users in such cases is not so crucial. On the other hand, there are scenarios which are more oriented towards healthcare, where the processing and transmission of confidential patient data requires important security and privacy considerations [19]. We faced another problem when dealing with the precision of the indoor localization service. We needed a mechanism to get localization at the lowest cost possible (in terms of computation, infrastructure, and price) which led us to base it on Wi-Fi signal strength readings. Wi-Fi based techniques manage to get estimations within an approximate error of 20 meters, we needed a better one and considered a 5 meters error window. The precision we finally got was satisfactorily enough but this resulted par-ticularly difficult on the voice-enabled assistance scenario, where the user activates the doors in front of him. Luckily, doors in the nursing room in which we conducted the ex-perimentation were spread in far longer distances than our error window.
7.
CONCLUSIONS AND FUTURE WORK
In this article, we approached the use of ambient intelli-gence systems in the context of ambient assisted living for supporting the elder. This is a topic that has been pursued recently and it has gained greater importance because of the worldwide increase in average life expectancy [20]. We also presented our hardware and software infrastructure to sup-port this kind of applications and discussed the implemen-tation of a number of scenarios conducted in our research group. We believe our proposals will contribute in improving the lifestyle of the elderly by allowing the rapid construction of prototypes for usability, and application-driven research. As stated before, the elder users have quite specific re-quirements, and their relationship with augmented objects
and modern interface technologies have yet to be carefully analyzed. We plan to conduct an extensive evaluation of our contributions from two approaches. First, to evaluate the construction of ambient assisted living applications for the elderly with the use of our platform from the perspective of the developers of such applications. Second, to evaluate the usability and acceptance of technology of the applications from the perspective of the elderly user. We have success-fully tested the implemented applications in our laboratory, the next step is to conduct the usability studies by using our prototypes in the nursing home in which we conducted our analysis.
8.
ACKNOWLEDGMENTS
The work presented in this paper was funded with grants from CONACyT, the Mexican Council for Science and Tech-nology.
9.
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