Computers in Human Behavior 33 (2014) 63–68

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Computers in Human Behavior

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Perspectives of human factors in designing elderly monitoring system

0747-5632/$ - see front matter � 2013 Elsevier Ltd. All rights reserved.http://dx.doi.org/10.1016/j.chb.2013.12.010

⇑ Tel.: +966 1 4696316.E-mail address: mahossain@ksu.edu.sa

Mohammad Anwar Hossain ⇑Software Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia

a r t i c l e i n f o a b s t r a c t

Article history:Available online 23 January 2014

Keywords:Human factorsElderly monitoring systemAssisted livingElderlyCaregiver

This paper studies the perspectives of human factors in the context of an elderly monitoring system. Sucha system is designed to continuously monitor older adults and support them with various services. It alsohelps the caregiver with on-the-spot updates based on elderly monitoring. However, how the elderly andhuman caregiver perceive and interact with such system as an integral part of it, can greatly influencetheir understanding of the purpose and usefulness of the system. In this paper, we highlight several corefunctions of an elderly monitoring system and show how human factors can influence the design of thesystem. Experimental results show improvements with respect to efficiency, effectiveness and usersatisfaction.

� 2013 Elsevier Ltd. All rights reserved.

1. Introduction

In the current demographics, older adults constitute a large pro-portion of world population and they often suffer from cognitiveand physical impairments. As a result, we witness a significantgrowth in elderly nursing facilities that assist older adults throughcaregiver intervention. While such caregiver-based assistance isnecessary, burden on the caregivers also remains a concern (Garlo,O’Leary, Van Ness, & Fried, 2010; LaVela, Johnson, Miskevics, &Weaver, 2012; McVicar, 2003) that degrades the quality of supportthe older adults receive. With the advancement of sensing andcommunication technology, there have been visible initiatives indesigning assistive healthcare systems such as an elderly monitor-ing system (Hossain & Ahmed, 2012; Tseng & Lu, 2013), which canhelp the caregiver to keep an watchful eye on the elderly and pro-vide several services in different situations, thus reducing someburden the caregivers face. Clearly such a system involves manystakeholders including elderly, caregivers, emergency personneland so on. Hence it is important that these systems are designedby considering human in the loop, which is addressed by thehuman factors principles (Salvendy, 2012).

The area of human factors focuses on interactions among people,system and their environment that contributes to improve the qual-ity of human well being and system performance (iea). With respectto healthcare context, the need to characterize these interactions hasbeen well recognized by many researchers (Carayon et al., 2006;Duffy, 2010; Hignett, Carayon, Buckle, & Catchpole, 2013; Or et al.,2009) in order to meet the emerging needs of the older people andsupport them in different settings. Fig. 1, adapted from (Carayon

et al., 2006), shows how users (i.e. elderly and caregiver) are integralpart of an elderly care system, especially when such a system is inter-active with different user groups. Furthermore, such interaction isnot simply using the system functionality in an explicit manner,rather it involves automatic and semi-automatic interaction withthe instrumented elderly care environment. Fig. 1 also indicates thatdue to the mismatches among the five elements of the system, i.e.technology and tools, tasks, persons, organization, and environment,many issues relevant to human factors arise (Or et al., 2009). Properapplication of human factors principles is the way forward toaddress these issues.

Human factors principles aim for effectiveness, efficiency, anduser satisfaction in the design of artifacts and devices. This isspecially important in the design of healthcare products that areuseful and usable by the elderly population. Although such impor-tance has been identified by many (Duffy, 2010; Hignett et al.,2013), there are still some gaps in the adoption of human factorsprinciples in healthcare systems (Gurses, Ozok, & Pronovost,2012). In particular, as the main goal of an elderly monitoring sys-tem is to facilitate the interaction among different stakeholdersalong with continuous monitoring of the elderly, we believe moreattention should be paid to the people who are intended to benefitfrom such a system.

This paper studies the perspectives of human factors in thecontext of designing an elderly monitoring system. Toward this,it first discusses the different human factors issues relevant tosome primary requirements of such system. It then presents anexample of an elderly monitoring system and briefly describesthe system components. Later it shows how the different humanfactors consideration influence the design of such a system. Fewexperiments have been performed that shows the effectivenessand usefulness of the system.

Fig. 1. Analytical framework for human factors in the healthcare facility.

64 M.A. Hossain / Computers in Human Behavior 33 (2014) 63–68

2. Human factors perspectives in elderly monitoring system

An elderly monitoring system is designed to offer many func-tionalities that are carried out either by the elderly patients, thecaregiver or the system itself. Several researchers (Haux, Howe,Marschollek, Plischke, & Wolf, 2008; Koch et al., 2009; Or et al.,2009) have highlighted a variety of such functions. In the follow-ing, we describe three most relevant functions and several humanfactors issues associated to these functions.

� Emergency detection and alarm.� Information and media service access.� Interaction and communication.

2.1. Emergency detection and alarm

This function is performed by the instrumented environmentwhere the elderly lives. Usually such an environment is equippedwith different sensors and actuators devices (de Ruyter & Pelgrim,2007; Hossain & Ahmed, 2012; Tseng & Lu, 2013). The sensor-basedsystem continuously monitor the elderly to identify events of inter-est such as detecting falls. Several human factors are associated withthis function. For example, how and when the system should reportthe detection of an abnormal event is a concern. Depending on theseverity of the event, the system should generate alarm and informthe caregiver or other emergency personnel when required. Careshould also be given in selecting the appropriate delivery channelthrough which the emergency situation will be reported, such ascontrolling the environmental lighting condition, sending SMS orvibrating a wrist band the caregiver might wear.

In a technology-augmented environment, privacy oftenbecomes an important concern (Miskelly, 2001) due to the use ofpervasive technology for continuous monitoring. Hence, it is criti-cal that the emergency detection and alarm function of the systemadopts privacy-preserving techniques (Moncrieff, Venkatesh, &West, 2008) for improved user satisfaction without compromisingthe actual monitoring task.

2.2. Information and media service access

Accessing information and media services is one of the primaryfunctions the elderly and caregiver perform (Alpay et al., 2004;Haux et al., 2008; Or et al., 2009). The requested informationmay vary from health information to medication dosage to dietary

information to a wide range of media services for entertainment.Often older adults struggle to find the right information and haveconcerns over the use of technology (Wagner, Hassanein, & Head,2010). However, recent study shows positive attitudes of theelderly towards technology and technology-driven services(Mitzner et al., 2010).

Human factors central to information access lies on how theinformation is accessed by the elderly and caregiver. The elderlymay find it difficult to consume various information due to theway it is presented (e.g. color, font size, screen) or may be overbur-dened with excessive information. Several context-aware mecha-nisms (Bricon-Souf & Newman, 2007; Kleinberger, Becker, Ras,Holzinger, & Müller, 2007) can be used to help elderly get relevantinformation within the right context. User interface also plays amajor role on how the elderly perceive the information, especiallywhen elders suffer from various down syndrome or other diseases.Various usability heuristics (Nielsen, 2005) should be appliedwhen facilitating information access to the elderly.

2.3. Interaction and communication

It is fundamental to provide interaction and communicationfacility within the context of elderly monitoring system. Interac-tion in this domain is not merely simple human–computer interac-tion, rather it involves complex human-environment interaction.The premise of elderly monitoring system is an smart instru-mented environment consisting of myriad sensor and actuator de-vices. Hence, different types of interaction with the environmentcan occur, namely explicit interaction, automatic interaction andsemi-automatic interaction (Hossain & Ahmed, 2012). Throughexplicit interaction the elderly and caregiver may access systemfunctionality by using system-provided interfaces. The automaticinteraction is performed by the system itself, which can invokeseveral services for the user based on the identification of prede-fined context. In semi-automatic interaction, the system and usercan jointly perform a tasks that is needed by the user.

Communication functionality (Or et al., 2009) is another impor-tant aspect of elderly monitoring system as it aims to facilitatecommunication between multiple parties including elderly, care-giver, external service provider, emergency personnel and so on.Effective and efficient communication is vital for elderly care indifferent scenarios. For example, in case of a fall detection, thecaregiver should be notified without delay so as to take appropri-ate actions.

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Several human factors issues are relevant to designing interac-tion and communication functionality. Interaction should be easyand understandable so that the users are not overburdened withthe technology presence in the elderly care facility. The user shouldenjoy a perceived sense of control (Salvendy, 2012) within theinstrumented environment. Similarly, the communication meansand messages should be clear to the concerned users.

3. The prototype elderly monitoring system

This section briefly illustrates the design of our prototype el-derly monitoring system, which we named as virtual caregiver(Hossain & Ahmed, 2012). The primary goal of this system is tohelp monitoring the elderly for health and well-being as well asto act as an assistant of the caregiver with a view to reduce thephysical and cognitive load of elderly and the caregiver. Thesystem is designed by considering the human factors perspectivesdescribed in earlier section. Fig. 2 presents the deploymentdiagram of the prototype system.

The system portrayed in this figure provides several functional-ities. The SituationAnalyzer and SensingDataProcessor with thehelp of sensor and actuator components perform emergency detec-tion and alarm services. Information and media service accessfunctionality is performed by the ServiceInvocationManager com-ponent, while the interaction and communication functionality issupported by the VirtualCaregiver component. Several other rele-vant functions are also supported by this system and a detaileddescription of those are out of the scope of this paper, but can befound in (Hossain & Ahmed, 2012). In a nutshell, the system iden-tifies the current context of the user, determines whether toprovide services automatically or not, intervene human caregiveror external service provider when necessary, and responds to theexplicit command made by the elderly and caregiver.

4. Evaluation

This section elaborates how the experiment was conductedwith the prototype system (System-A), which has the functionalitydescribed earlier and the design incorporated several humanfactors principles. For this evaluation, we also developed another

Fig. 2. The deployment diagram of the p

variants of the system prototype (System-B) that adopts traditionaldevelopment principles without giving much attention to humanfactors and that only has navigation capability to access informa-tion through explicit interaction.

4.1. Evaluation procedure

For our experiment, we invited ten male elderly subjects withina age group of 60–70 years. Also four human caregivers, who usu-ally takes care of their older parents and relatives at home, partic-ipated in the evaluation. The objective of this experiment is toevaluate the following:

� Efficiency of the elderly and caregiver in accomplishing a taskwhen using the two variants of the system.� Perceived satisfaction of the elderly and caregiver in the two

systems.� Level of engagement of the caregiver using the two systems.� Whether the caregiver’s burden is minimized.

In light of the above goals, we explained the test participants whatactions they have to perform and what they might expect during theevaluation process. We also gave a walkthrough to make themselvesfamiliar with the different functionalities the systems can perform.Our experiment followed a within-group design. This means the sametest participants were used to test both the prototype systems. Weused a set of questionnaire to evaluate the results from theexperiment. In the following we report our evaluation result.

4.2. Evaluation results

Fig. 3 shows the evaluation score that represents efficiency ofthe elderly in terms of obtaining timely assistance, receiving ex-pected services in time and ease of interaction with the two systemprototypes. The score in the figure is based on the average value ofthese factors. It is evident that the system with full functionalitythat considered human factors obtained higher score. The sameis observed in terms of caregiver efficiency in performing the as-signed tasks. This is presented in Fig. 4.

rototype elderly monitoring system.

Fig. 3. Results showing the efficiency of the elderly using the two systems.

Fig. 4. Results showing the efficiency of the caregiver using the two systems.

Fig. 5. Results showing perceived satisfaction of the elderly using the two systems.

Fig. 6. Results showing perceived satisfaction of the caregiver using the two systems.

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With respect to perceived satisfaction, Figs. 5 and 6 shows theresults obtained from elderly and caregiver, respectively.

In order to obtain the level of engagement of the caregiver withthe two systems, we collected data over a period of 10 h in differ-ent days. We observe that with System-A, the caregiver had toengage in minimal number of events, whereas with System-B thecaregiver were overburdened with the number of reported events.It conveys that caregiver’s engagement is minimized with thefunctioning prototype system and the burden he/she faces is alsoalleviated. This is presented in Fig. 7.

In this evaluation, the scores were obtained on a scale from 1 to7. Overall, the results show clear advantages of the elderly moni-toring system for both the elderly and caregiver.

5. Related work

Human factors consideration has become an integral aspect ofmany system design and development activities, which is equallycritical in the case of designing elderly monitoring systems. As a re-sult, we witness continuous research on human factor issues in thecontext of home care and health information systems. In this sec-tion, we study some of these existing research and briefly com-ment on them. Our study mainly highlights two categories ofresearch, one generally focus on home care or elderly monitoringsystems and supports, while the other focus on the associationsof human factors in those systems.

5.1. Research on elderly monitoring systems

Support for elderly has been suggested with the development ofrobot-based artificial companions, such as in (Kriglstein & Wallner,2005; Montemerlo, Pineau, Roy, Thrun, & Verma, 2002; Tamuraet al., 2004). Tamura et al. (2004) promoted voice command basedrobot use to provide services for the people with demantia. Monte-merlo et al. (2002) focused on developing robotic platform forphysically impaired people to enable effective reminding func-tions, surveillance, tele-presence, and social interactions. The pro-posed robot not only helped to observe elderly and track them, butalso inferred behaviors of the elderly by learning their movementpatterns. In a similar motive, an artificial companion named Homiewas proposed (Kriglstein & Wallner, 2005) as an entertainmentand health assistant for vision impaired people. Homie had thecapability to read out text messages, record notes and appoint-ments, receive text messages from doctors regarding medicalinformation and consultation dates, and to be used as a remotecontrol for the television based on speech command.

Besides robotic assistance, research has focused on developingsensor-enabled assisted living environment. The Philips’ CareLab(de Ruyter & Pelgrim, 2007) is a one-bedroom technology-aug-mented apartment to use as a elderly-care facility. This facility isequipped with various sensors and actuators devices to study theelderly in different context situations while they use several appli-cations related to their health and well-being. Khoo, Cheok,Nguyen, and Pan (1988) proposed the Age Invaders (AI) game to al-low elderly to play with the young in a spatially free and relaxingphysical space, which also facilitated parents to participate remo-tely in the game environment. Another work, the iCare portal(Chang, Yuan, & Li, 2009) emphasizes on the social and behavioral

Fig. 7. Results showing the engagement of human caregiver using the two system.

M.A. Hossain / Computers in Human Behavior 33 (2014) 63–68 67

issues of aging services and supports access to ambient servicesand media for the elderly. Recently, Hossain and Ahmed (2012)proposed a virtual caregiver system to facilitate the monitoringof elderly as an assistant to human caregiver. This system providesseveral interaction opportunities based on the occurrence of eventsin the elderly care facility in order to alleviate the cognitive load ofthe elderly and caregiver.

5.2. Research on human factors in home care system

There are several researches in this category. Or et al. (2009)discusses the human factors and ergonomics (HFE) concerns forpatient care at home related to information access, communica-tion, and patient self monitoring. Based on the current concerns,the authors proposes HFE guidelines for designing and developingfuture health information technology for home care. Authors in(Gurses et al., 2012) have seen HFE as a way to improve patientsafety and accordingly made five recommendations to integrateit into patient safety efforts taken by health care community. Thestudy in (Duffy, 2010) reports the advancement of how HFE canimprove quality, efficiency, effectiveness and safety in patient care.This study in particular covers topics relevant to healthcare andservices, patient safety, modeling and analytical approaches, hu-man computer interaction and organizational aspects toward im-proved healthcare.

Hignett et al. (2013) provide a state of science commentary byillustrating four examples in healthcare domain including occupa-tional ergonomics, design for patient safety, surgical safety andorganizational and socio-technical systems to review and analyzeHFE issues. The authors have also identified future opportunitiesto embed HFE in healthcare systems and improve its quality aswell as to reduce growing healthcare costs.

As an overview, the works reported above corresponding to HFEprovide general guidelines and recommendations to improvehealthcare systems and to promote patient safety, quality, efficiencyand effectiveness with the integration of HFE. These guidelines andrecommendations are important for designing and developinghealthcare systems for aging population. Contrary to the reportedresearch, the proposed work study the perspectives of human factorswith respect to several core functions of an elderly monitoringsystem.

6. Conclusions

In this paper, we studied several perspectives of human factorsand highlighted its influence in designing core functionalities of an

elderly monitoring system. Brief illustration of a prototype systemis provided. Experiments conducted with the prototype resulted inpositive feedback from the elderly and human caregiver. We alsoshowed that the caregiver’s burden is minimized based on efficienthandling of events in different contexts. Our future work willconcentrate on real-life implementation of the elderly monitoringsystem and observe the behavior of elderly and caregiver over alonger period of time.

Acknowledgment

The authors extend their appreciation to the Deanship of Scien-tific Research at King Saud University for funding this workthrough the research group Project No. RGP-VPP-049.

References

Alpay, L. L., Toussaint, P. J., Ezendam, N. P., Rövekamp, T. A., Graafmans, W. C., &Westendorp, R. G. (2004). Easing internet access of health information forelderly users. Health Informatics Journal, 10, 185–194.

Bricon-Souf, N., & Newman, C. R. (2007). Context awareness in health care:A review. International Journal of Medical Informatics, 76, 2–12.

Carayon, P., Hundt, A. S., Karsh, B., Gurses, A., Alvarado, C., Smith, M., et al. (2006).Work system design for patient safety: The SEIPS model. Quality and Safety inHealth Care, 15, i50–i58.

Chang, W. L., Yuan, S. T., & Li, E. Y. (2009). iCare home portal: An extended model ofquality aging e-services. Communications of the ACM, 52, 118–124.

de Ruyter, B., & Pelgrim, E. (2007). Ambient assisted-living research in carelab.Interactions, 14, 30–33.

Duffy, V. G. (2010). Advances in human factors and ergonomics in healthcare. CRCPress.

Garlo, K., O’Leary, J. R., Van Ness, P. H., & Fried, T. R. (2010). Burden in caregivers ofolder adults with advanced illness. Journal of the American Geriatrics Society, 58,2315–2322.

Gurses, A. P., Ozok, A. A., & Pronovost, P. J. (2012). Time to accelerate integration ofhuman factors and ergonomics in patient safety. BMJ Quality and Safety, 21,347–351.

Haux, R., Howe, J., Marschollek, M., Plischke, M., & Wolf, K. H. (2008). Health-enabling technologies for pervasive health care: On services and ICTarchitecture paradigms. Informatics for Health and Social Care, 33, 77–89.

Hignett, S., Carayon, P., Buckle, P., & Catchpole, K. (2013). State of science: Humanfactors and ergonomics in healthcare. Ergonomics, 1–13.

Hossain, M., & Ahmed, D. (2012). Virtual caregiver: An ambient-aware elderlymonitoring system. IEEE Transactions on Information Technology in Biomedicine,16, 1024–1031.

International Ergonomics Association. <Http://www.iea.cc/> Accessed 25.09.13.Khoo, E. T., Cheok, A. D., Nguyen, T. H., & Pan, Z. (1988). Age invaders: Social

and physical inter-generational mixed reality family entertainment. VirtualReal, 12, 3–16.

Kleinberger, T., Becker, M., Ras, E., Holzinger, A., & Müller, P. (2007). Ambientintelligence in assisted living: Enable elderly people to handle future interfaces.In Universal access in human–computer interaction. Ambient interaction (pp. 103–112). Springer.

68 M.A. Hossain / Computers in Human Behavior 33 (2014) 63–68

Koch, S., Marschollek, M., Wolf, K., Plischke, M., Haux, R., Eckardt, J., et al. (2009). Onhealth-enabling and ambient-assistive technologies. Methods of Information inMedicine, 48, 29–37.

Kriglstein, S., & Wallner, G. (2005). HOMIE: An artificial companion for elderlypeople. In CHI 2005 extended abstracts on human factors in computing systems.

LaVela, S. L., Johnson, B. W., Miskevics, S., & Weaver, F. M. (2012). Impact of amulticomponent support services program on informal caregivers of adultsaging with disabilities. Journal of Gerontological Social Work, 55, 160–174.

McVicar, A. (2003). Workplace stress in nursing: A literature review. Journal ofAdvanced Nursing, 44, 633–642.

Miskelly, F. G. (2001). Assistive technology in elderly care. Age and Ageing, 30,455–458.

Mitzner, T. L., Boron, J. B., Fausset, C. B., Adams, A. E., Charness, N., Czaja, S. J., et al.(2010). Older adults talk technology: Technology usage and attitudes.Computers in Human Behavior, 26, 1710–1721.

Moncrieff, S., Venkatesh, S., & West, G. (2008). Dynamic privacy assessment in a smarthouse environment using multimodal sensing. In ACM transactions on multimediacomputing, communications, and applications (TOMCCAP) (Vol. 5, pp. 10).

Montemerlo, M., Pineau, J., Roy, N., Thrun, S., & Verma, V. (2002). Experiences with amobile robotic elderly guide for the elderly. In National conference on artificialintelligence, AAAI.

Nielsen, J. (2005). Ten usability heuristics. <http://www.nngroup.com/articles/ten-usability-heuristics/> Accessed 05.10.13.

Or, C. K., Valdez, R. S., Casper, G. R., Carayon, P., Burke, L. J., Brennan, P. F., et al.(2009). Human factors and ergonomics in home care: Current concerns andfuture considerations for health information technology. Work: A Journal ofPrevention, Assessment and Rehabilitation, 33, 201–209.

Salvendy, G. (2012). Handbook of human factors and ergonomics (4th ed.). New York:Wiley.

Tamura, T., Yonemitsu, S., Itoh, A., Oikawa, D., Kawakami, A., Higashi, Y., et al.(2004). Is an entertainment robot useful in the care of elderly people withsevere dementia? The Journals of Gerontology. Series A, Biological Sciences andMedical Sciences, 59, 83–85.

Tseng, P. C., & Lu, L. C. (2013). The elderly care system. Journal of Computers, 24.Wagner, N., Hassanein, K., & Head, M. (2010). Computer use by older adults: A

multi-disciplinary review. Computers in Human Behavior, 26, 870–882.