A Human Caregiver Support System in Elderly Monitoring Facility

Transcription

1 2012 IEEE International Conference on Multimedia and Expo Workshops A Human Caregiver Support System in Elderly Monitoring Facility M. Anwar Hossain and Dewan Tanvir Ahmed College of Computer and Information Sciences (CCIS) King Saud University Riyadh, KSA {mahossain, dtahmed}@ksu.edu.sa Abstract The number of elderly population is increasing worldwide and often they need assistance in their daily activities. In many situations, these elders are placed in elderly care facilities in order to receive continuous assistance from the human caregivers. The caregivers usually keep a watchful eye on the elders and help them in their activities of daily living. However, study shows that the human caregivers often suffer from boredom for being engaged in monitoring the elderly, which also compromises the care and assistance needed for the vulnerable elderly. In order to address this issue, we propose a human caregiver support system that aims to comprehend elderly persons activities and decides what services to provide them in different situations and when to notify the human caregiver about any incident that happens in the care facility. Our preliminary experiment shows the potential of such system. Keywords-elderly support system; human caregiver; health and well-being; elderly monitoring facility; I. INTRODUCTION Worldwide elderly population is increasing, so is the need to support this group of population. Many elders suffer from physical and cognitive impairment, and remain in some elderly care facilities where they receive assistance from human caregivers. The caregivers continuously monitor these elderly and help them in their activities of daily living when needed. It has been reported that the continuous monitoring of elderly also puts burden on the human caregivers [1] and increase their cognitive overload. This not only hampers the timely assistance needed for the elderly, but also compromises the quality of assistance they receive. Therefore, it is important to develop effective tools and techniques for the human caregivers so that they can provide the much needed assistance to the elderly in monitoring their health and well-being. However, developing such tools and techniques is challenging since it involves understanding the current situation and activities of the elderly. Existing work address this issue from different corners: deliver information and entertainment services to the elder patients [2], [3]; provide robotic assistance to the elderly [4], [5]; offer reminding services for task completion [6] and so on. Although these works contribute significantly to elderly monitoring domain, they lack the focus on minimizing human caregivers burden and boredom. Unlike these works, we aim to develop a human caregiver support system, which will help them in effectively addressing the needs of the elderly. The cornerstone of our approach is to develop a virtual support system for the human caregiver to assist them for effectively monitoring the elderly. The system is able to capture the context of the environment and elderly activities, determine on-the-fly what services to provide to the elderly, decide whether to invoke those services automatically, and intervene human caregiver for additional support or involve external parties when necessary. Our contribution in this paper is two-fold. First, we introduce human caregiver support system in the context of elderly monitoring, which works by considering the surrounding context and activities of the elderly people. Second, we illustrate the different modes of interaction of the human caregiver support system for monitoring effective the elderly and reducing the cognitive load of the human caregiver. The remainder of this paper is organized as follows. We comment on existing literature in Section II; The proposed human caregiver support system is illustrated in Section III. This is followed by the implementation highlights in Section IV and experimental results in Section V. The paper is concluded in Section VI with a glimpse of future work directions. II. RELATED WORK We survey related research in this section. In particular, we investigate what tools and support services have been proposed for monitoring the elderly and whether those tools and services work in mediation with the human caregiver to reduce his/her cognitive load that is felt in monitoring the elderly. The CMU Pittsburgh NurseBot project [7] is one endeavor towards developing robotic assistance for the elderly. It focused on robotic platform that was used for testing several functionalities such as intelligent reminding functions, tele-presence applications, surveillance and assistance for physically impaired people. The robot in this project used to observe elderly and learn their patterns of movement to provide the required services. Several other works also prompted for developing robotic assistance. Tamura et al. [4] described the use of entertainment robot that responds to spoken commands from /12 $ IEEE DOI /ICMEW

2 Figure 1. High-level task and interaction flow of the human caregiver support system the people with severe dementia. The authors convincingly demonstrated the robot-based approach as an effective rehabilitation tool for this group of people. Similarly, robots were used by the authors [5] as a communication activator based on a recreational game call Nandoku. They stressed the need for improving communication, especially group communication for the cure of elderly suffering from depressing and dementia. An agent-based architectural framework has been proposed in [8], which aimed to facilitate the development of flexible elderly care services. Other researchers has established CareLab [9] at the Philips High Tech Campus in Eindhoven, which is an one bedroom hi-tech apartment as a senior-care facility. It is equipped with myriad sensors and devices to study different context situations in which the elderly will use several applications related to their health and well-being. The work reported in [2] describes AVANTI project, which facilitates the adaptation of web content and user interface for individuals including elderly. Many works have focused on elderly care in the home environment. E. Hollywood et al. [10] created an artificial companion called Program Alleviating Loneliness (PAL) for the home-bound elderly. The system is engaging and entertaining to be elderly at the same time. The OLDES project [11], as realized in Italy and Czech Republic, provides entertainment services to the elderly through different channels and animator-based special interest forums. Another work with a broader perspective has been reported as icare [3], which addresses the social and behavioral aspects of aging services. The system supports access to ambient services, unbound information reachability and seamless social connection through the icare home portal. A recent work [12] addressed the development of inhome assistance application for the elderly. This work aims at sensing, predicting, reasoning, and acting in response to the elderly activities at home. The authors developed a C- AmI system architecture by synthesizing various sensors, activity recognition, case-based reasoning (CBR) and elderly in-home assistance customized knowledge. The above works focused on different tools and systems that help the elderly in their daily lives. However, mediation between the tools and the human caregiver was hardly present. Unlike those, our approach is to develop an elderly monitoring system as a support service for the human caregiver so that they can effectively monitor and assist those elderly. III. PROPOSED HUMAN CAREGIVER SUPPORT SYSTEM The goal of the proposed human caregiver support system is to assist the caregiver to monitor the elderly person from health and well-being perspective. We developed this system as a means of providing services to the elderly based on their current activities and context. Thus, such a system can reduce both cognitive and physical load of a human caregiver. Figure 1 shows a high-level interaction flow of the system. This figure shows that the system becomes aware of the situation based on the sensing data. The system then decides whether to invoke relevant services, interact with human caregiver or call for external assistance in critical situations. In the following section, we describe these steps. A. Sensing The sensing module collects data from multiple sensors placed in the environment, processes the data and identifies different context parameters such as who is around, what is happening, the location and the like. In the overall system context, this module independently works to process the context parameters. Other modules obtain these context parameters when required. Therefore, the operation of other modules is not affected even if there is an underlying change in the sensing architecture and devices. 436

3 Table I ACTIVITY STATE TRANSITION AND THE CORRESPONDING ACTIONS Normal Socializing Lying Normal Socializing Lying visitor entered fall down invoke service intervene caregiver, inform external members visitor left invoke service standup visitor enter invoke service invoke service B. Situation analysis From Figure 1, it is evident that the situation analysis task is based on the outcome of the sensing tasks. The main objective of the situation analysis task is to determine the current context of the environment and the activities the elderly people are engaged in. Depending on the situation of the the elderly monitoring environment, the human caregiver support system will carry out relevant interaction to support care-giving. The following scenarios illustrate some cases when such interaction may occur: Elderly person falls down (drop suddenly or collapse) and remains down on floor for more than a specified period of time with minimal body movement. Elderly person falls asleep but some environmental and entertainment devices (e.g. lights, TV, DVD) are running. Somebody enters the room occupied by the elderly person but the person is unknown to the elder or the elderly facility. C. Human caregiver support system Based on the current situation of elderly activities, the human caregiver support system determines what level of assistance is required by the elderly or the human caregiver. In Table I, we provide a snapshot of the elderly activities, and list the different types of support the system can provide. In a similar fashion, we can determine the required level of assistance in different situations. In this table, the left-most column and the top-most row shows sample activity names. The content of each cell describes a transition between two activities from left column to top row. For example, a state change from sleeping to normal state occurs when the elderly person wakes up and that will require the support system to invoke services relevant to that situation. In the following, we describe human caregiver support system in terms of several important aspects, which are 1) rule definition for activity transition, 2) interaction modes, and 3) service invocation. 1) Rule definition: We model the transition between two activity states and the corresponding actions to be performed by the support system using the Event Condition Action (ECA) rules. The ECA is a short-cut to represent the structure of active rules especially for event-driven architectures and active database systems. ECA languages are indeed an intuitive and powerful model of programming that deals with reactive systems. In recent days, ECA rule engines use several variations on rule structure. Augusto et al. [13] and Liu et al. [14] show the use of ECA rules and some variations for applications in Smart Homes and assisted living for elderly people. The basic construct of ECA is reactive rules, which have the following form: On <event expression> If <condition> Do <action> This means that On detecting certain events (i.e. elderly activity), If certain conditions are verified (i.e. true), then specific actions should be executed (i.e. Do). Based on such ECA rule construct, we can model the cases defined in Section III-B, for example the following: On elderly person in bed for a long period of time If unexpectedly in bed during this period of time Do intervene the human caregiver. 2) Interaction modes: According to the situation, the proposed support system acts based on three different modes that are explained below. Automatic service invocation: In this mode, the system interacts with service invocation manager to implicitly invoke relevant services for the elderly. The service invocation manager identifies potential conflicts between the already running services and the newly prescribed services. It then schedules the execution of the suggested services. Human caregiver intervention: According to the change of the activity states, the system might intervene human caregiver in critical cases. For example, an elderly might fall on the floor and remain there for a specified period of time, which might require human caregiver s attention. The human caregiver in response can explicitly activate any services; give command to the system to perform certain actions or ask for external assistance if needed. External service request: In some critical situations, like the human caregiver, the system might as well call for external assistance. For example, calling for emergency assistance in case of a fire. 437

4 Figure 2. The core modules of the proposed support system 3) Service invocation: The Service Invocation module receives implicit command from the system when there is a need to invoke services relevant to a situation. The services could vary from switching light ON/OFF to adjusting light levels, selecting media, locking/unlocking doors, managing alarm ON/OFF etc. that have an effect on the elderly monitoring environment. D. Algorithm This algorithm for human caregiver support system takes context with an attribute set as input and provides elderly services as output. There are several steps in this process, which are listed below. Determine the list of possible services based on the current context that can be provided in the environment. Identify the list of currently running services that is needed to avoid any conflict for running the newly suggested services. Determine the modes of interactions for providing the elderly support services based on current context and elderly activity. The proposed system defines three modes of interaction, which suggests whether to utilize automatic Service Invocation, to intervene human caregiver and/or to call for external assistance. Identify the services that would conflict with the newly suggested services and make an schedule to execute the relevant services. Intervene the human caregiver if there is a need for human caregiver s attention. Call for external assistance based on the selected mode of interaction. E. Architecture The architecture of the proposed system is given in Figure 2. It is developed using a service-oriented architecture (SOA), where each of the modules acts as a service. A service can be a producer, a consumer or both. For example, the Sensor Data Processor in this figure is the producer of context-related information, whereas the Situation Analyzer is the consumer of such information. This figure also shows that the elderly monitoring environment is equipped with different sensors and actuator devices. The sensors are used to sense the environment activities whereas the actuator is used to activate different services in the environment. IV. IMPLEMENTATION The proposed framework is implemented on the.net development framework. Our implementation adopts web service-based approach in order to provide an interoperable technology platform. Also, we used web service discovery (WS-Discovery) [15] and web service eventing (WS-Eventing) [16] mechanism to make the developed services discoverable on the fly and to send notification of events based on a publish-subscribe model. Our implemented framework is deployed in an instrumented smart environment, where we placed several sensors such as IP cameras and X10 motion sensors, actuators, X10 lamp and appliance modules, phidget s temperature sensor, wireless weight scale, RFID tags, RFID reader, and VLC media players. V. EXPERIMENT For the experiment, we considered a two-room instrumented apartment where we strategically placed different sensor and actuator devices. We invited five elderly test participants as volunteers and two human caregivers who takes care of their older parents at home. A. Demonstrating situation analysis We used camera picture and motion sensor data to identify some situations when caregiving is needed. The sensing data processor first processes the sensor data that it receives and pass its observation to the situation analyzer. In our experiment, we used the opencv s face detection and recognition 438

5 Figure 3. Results showing the satisfaction level of the elderly addition to this, we also tested with known visitors whose profiles were registered in the system. In that case, the system instructed the service invocation manager to provide the identified services, for example switching on TV with some selected news sources. Similarly other situation and interaction modes can be demonstrated. In the case of a real life deployment, one should implement a fully functional automated activity detection mechanism and integrate it with the proposed system. C. Evaluating satisfaction and acceptance In order to measure the satisfaction of elderly people, we asked questions to the elderly to know whether they are satisfied by implicit interaction assistance provided by the system. We took a similar approach to know the level of acceptance of the human caregiver for the caregiving assistance he received from the system. Figure 3 and Figure 4 show the results of the responses provided by the elderly and the human caregiver, respectively. The degree of freedom of the responses was based on a scale between 1 to 7. The results show clear advantages of the system as accepted by both the elderly and the caregiver. Figure 4. Results showing the acceptance rate of the human caregiver ( algorithm to identify the visitor coming in the environment. Also the X10 motion sensor that we used provides RFbased motion sensing from which we could understand the movement level in the environment. Combining these two we understood the socialization activity the elderly is engaged. The system also identified whether the elderly is watching TV, which was determined based on RFID; and whether the elderly was sleeping, which was determined through motion sensing activity. B. Demonstrating service invocation Based on the situation analysis data, we demonstrate the interaction of the caregiver support system. In one test scenario, we had an intruder coming in the room the elderly lives, who was not registered in the database. As soon as the system became aware of this event, it sent SMS to the human caregiver. The human caregiver immediately came over to see what happened. We repeated this test to see whether the system performs the prescribed actions. In VI. CONCLUSIONS This paper proposes a human caregiver support system that provides assistance to the elderly as well as supports human caregiver to keep a watchful eye on the elderly. The system is aware of the monitoring environment and it dynamically determines whether to provide implicit services, intervene human caregiver and/or call for external assistance depending of the activity state change of the elderly. Our experimental results show that the elderly participants were highly satisfied with the system s assistance. The same was noticed from the human caregiver s positive feedback. However, more works need to be done. For example developing robust and distributed sensing data processing for understanding elderly activities and experimenting in real eldercare facility. REFERENCES [1] H. Brodaty et al., Towards harmonisation of caregiver outcome measures, Brain Aging, vol. 2, pp. 3 12, [2] J. Finka, A. Kobsaa, and A. Nill, Adaptable and adaptive information provision for all users, including disabled and elderly people, New Review of Hypermedia and Multimedia, vol. 4, pp , [3] W. Chang, S. Yuan, and E. Y. Li, icare home portal: an extended model of quality aging e-services, Communications of the ACM, vol. 52, pp , [4] T. Tamura et al., Is an entertainment robot useful in the care of elderly people with severe dementia?, The journals of gerontology. Series A, Biological sciences and medical sciences, vol. 59, pp ,

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