Integrated Smart Home Services and Smart Wearable Technology

for the Disabled and Elderly

Ayse Tuna

, Resul Das

and Gurkan Tuna

School of Foreign Languages, Trakya University, Edirne, Turkey

Department of Software Engineering, Firat University, Elazig, Turkey

Department of Computer Programming, Trakya University, Edirne, Turkey

Keywords: Smart Home, Innovative Services, Wearable Technology, Log Management, Analysis.

Abstract: Smart Home is indeed a broad concept which includes the techniques and systems applied to living spaces.

While its main goal is to reduce the consumption of energy, it provides many benefits including living in

comfort, security and increasing flexibility. Smart homes are achieved through networking, control and

automation technologies. Since smart homes offer more comfort and security and provide novel innovative

services, people with disabilities or the elderly can take the advantages and improve their life quality.

However, for such novel services, an analytical infrastructure which can manage overall data flow provided

by various sensors, understand anomalous behaviour, and make necessary decisions. In this study, for

efficient data handling and visualisation, an integrated smart service approach based on the use of a smart

vest is proposed. The smart vest plays a key role in the proposed system since it provides the main health

parameters of the monitored person to the smart home service and enables tracking of the monitored

person’s location. The proposed system offers many benefits to both people with disabilities and the elderly

and their families in terms of increased efficiency of health care service and comfort for the monitored

person. It can also reduce the cost of health care services by reducing the number of periodical visits.

1 INTRODUCTION

Smart homes are the houses of the future which

includes services connecting the physical world with

the digital world. In smart homes, digital devices are

connected to each other and the features which

provide a base facilitating communication, feedback,

and alerting are found. In smart homes, software

services and new generation technologies are

coupled together with existing devices supported by

sensors and actuators. It is a big mistake to think that

smart homes only provide remote management

advantages and the houses that generate their own

energy. Smart homes where we will spend a

significant part of our life will be able to provide

solutions improving the quality of our life through

correct and critical information gained from the

novel services of them. For novel services provided

by smart home applications, an analytical

infrastructure which can manage data flow provided

by sensors, understanding anomalous behaviour, and

making appropriate decisions must be set up. In

addition, the obtained data must be handled,

processed and visualized efficiently.

With the increase in the population of the

elderly, the importance of home services has started

to become more important (Istepanian et al, 2004).

Support services are very important especially in

diseases that lead to loss of cognitive functions, such

as Alzheimer. Here, fulfilling the daily activities is

the highest priority for the patients. In this regard,

gathering data from various sensors and applying

data mining techniques on it enables the detection of

the events at home (Moutacalli et al, 2013; Jovanov

et al, 2003). A similar study on this topic is

presented in (Son et al, 2013). The use of aiding

technologies for the care of diabetic patients can

prevent existing symptoms from getting aggravated

and help in improving them.

However, almost all smart home services rely on

localization information (Manley et al, 2007).

Wireless sensor networks, one of the most

commonly used network technologies for indoor

localization, are used for the fall detection and

prevention for the elderly and old patients (Wang et

al, 2014). Although it is difficult to implement,

emerging innovative smart home services are based

on image/video based activity recognition systems

(Jalal et al, 2012; Jalal et al, 2014).

173

Tuna A., Da¸s R. and Tuna G..

Integrated Smart Home Services and Smart Wearable Technology for the Disabled and Elderly.

DOI: 10.5220/0005552001730177

In Proceedings of 4th International Conference on Data Management Technologies and Applications (DATA-2015), pages 173-177

ISBN: 978-989-758-103-8

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

It is obvious that there is an integration need for

smart home services. Because services provided by

different technologies and tools alone are not

sufficient enough to improve human life. At the

same time, in both the existing and designed

systems, there is a need to carry out comprehensive

performance analysis and determine acceptable

service quality details.

Although there are many studies in the literature

such as smart home technologies (Son et al, 2011;

Han and Lim, 2010; Jiang et al, 2004), smart clothes

(Mitilineos et al, 2013; Vassiliadis et al, 2004;

Vassiliadis et al, 2011), data collection and control

using wireless sensor networks (Hulsmann and

Windt, 2007; Zhu et al, 2012; Francesco et al, 2011;

Incel et al, 2012), localization in wireless sensor

networks (Tuna et al, 2014; Buratti and Verdone,

2009; Mao et al, 2007; Han et al 2013) and log

management system (Daş et al, 2008; Daş and

Türkoğlu, 2009; Goel and Jha, 2013; Taghavi et al,

2012), the application and implementation of smart

home services for a common purpose is a relatively

new topic. Therefore, the services which include and

present novel approaches to address the existing

problems of the elderly and disabled are needed.

In this study, the use of a smart vest for

monitoring the main health parameters of the elderly

and disabled people and tracking their locations is

proposed. In this way, physical condition related to

main parameters will be collected and stored in the

management system, and the person’s location will

be simultaneously tracked using the proposed

localization system. To the best of our knowledge,

in the literature there are no prior studies proposing

an integrated smart home service for the elderly and

people with disabilities. Therefore, in this paper, we

focus on the integration of innovative smart home

services for this goal. The rest of the paper is

organized as follows. The description of the

proposed approach is given in Section 2. Section 3

presents the data management system of the

proposed approach. Finally, Section 4 concludes the

paper.

2 INTEGRATED SMART HOME

SERVICE APPROACH

In this study, an integrated smart home service

which aims monitoring personal activities at home is

proposed. In this regard, a smart vest which enables

monitoring the main health parameters is realized

along with a wireless sensor network-based data

collection and localization system and a smart log

management and alert system. The integrated service

improves the quality of the lives of the elderly and

disabled and is not only important for the elderly and

disabled but also for their relatives. As shown in

Figure 1, different from the existing works in the

literature, the integrated service approach proposed

in this study combines the techniques which are

generally handled separately.

Figure 1: Proposed approach.

In the proposed approach, for monitoring the

main health parameters of the elderly and disabled

people and tracking their locations, a smart vest is

used. The smart vest collects various 3 parameters

related to the physical condition of the monitored

person and sends them to data management system

over a wireless sensor network (WSN), a kind of

wireless network consisting of hundreds to

thousands of low-power multi-functional sensor

nodes with sensing, computation and

communication capabilities (Akyildiz et al, 2002;

Alemdar and Ersoy, 2010; Kiokes et al, 2014). It

also enables the monitored person’s location to be

tracked simultaneously.

The smart vest to be used in the scope of the

experimental studies will be developed by off-the-

shelf circuit components, sensors and flexible

printed circuit boards. The antenna will be

embedded in the smart vest and it will not restrict

the person’s freedom of action. In the smart home,

the approach which determines the smart vest’s

location on a relative basis is going to be followed to

determine the localization of the person (who is

wearing the smart vest.) Based on the signals

received from the smart vest which also acts as an

antenna, the smart vest’s location will be solved

using the approach of classifying the fixed nodes

with known locations with similar attenuation

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factors to itself. In this method which is based on the

determination of suitable geometric points, both

attenuation factors and the smart vest’s location will

be estimated. The proposed localization method is

suitable for simple sensor nodes with limited

processing power and memory resources.

At the heart of the offered smart home services, a

smart home management system lies. The most

important component of the smart home

management system which will connect external

world by the use of web-based services is a novel

data management system. The novel data

management system will be able to provide the

monitoring of all events and visualize the events in

order to extract the patterns which will be used to

estimate possible problems in the future. For

instance, by monitoring an old person during 10

days, it will be able to extract activity patterns,

record all events occurred and trigger the alerts

when deviations from normal activities happen.

Thus, it will be possible to inform the person’s

relatives or institutions which provide health

services. The proposed data management system

will be able to trigger an alarm if a record is not

generated within a determined period and it will

deliver the real-time tags and alarms to specified

users by SMS, e-mail and smart phone notification

messages. Therefore, it is necessary to store the

generated logs by keeping the error risk at minimum

and without overlooking security measures.

3 INTELLIGENT DATA

MANAGEMENT SYSTEM

In the proposed approach, the data management

system shown in Figure 2 is responsible for storing

logs collected from sensors after cleaning them,

extracting patters and generating behaviour profiles

after analysis, understanding anomalous behaviour,

and triggering alerts by making appropriate

decisions. It can be seen as the core of the proposed

approach.

Figure 2: Data management system.

Raw data in logs are not suitable for being

processed by the proposed system. Therefore, before

being processed, it must be processed to eliminate

the records which are not usable or irrelevant to the

process. Data pruning process shown in Figure 3 is

responsible for this.

Figure 3: Data pruning process.

Data sent through a set of selected port numbers

is captured and logged on along with date

information. In the data management system shown

in Figure 2, using the services of Listening Server,

Listening Tool is responsible for listening for a set of

selected ports. Cryptolog Database is responsible for

recording and the daily reporting of the logs

generated by Listening Tool. Admin Panel stores

the day sends the recorded log data after being

checked to Stamp Server for stamping. In addition,

Stamp Tool stamps the log data coming from

Listening Server and stores them on Time Stamp

Database. Admin Panel users are enabled to check

whether the log data stored on Cryptolog Database

have been changed after having been stamped by

Time Stamp Database.

4 CONCLUSIONS

Currently, there are Smart Home systems which can

control precisely the heating, the lighting and the

electrical appliances and be remotely controlled.

However, they can also perform other specific tasks

if innovative services are integrated. If this is done

and essential electronic components, products,

software systems, services and methods are

integrated, the smart home can assist people with

disabilities or the elderly in their daily lives, and

greatly enhance their life quality since they generally

stay in their own home longer and at the same time

wish to remain autonomous and independent. In this

study, an integrated system involving a smart vest

and a set of smart service components is proposed.

In the proposed system, the smart vest provides the

main health parameters of the monitored person to

the integrated system and enables tracking the

monitored person’s location. Since the data gathered

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from different sensors can be correlated to produce

an overall picture of the monitored person's health,

the proposed system offers several advantages to

health care providers.

REFERENCES

Istepanian, R. S. H., Jovanov, E. and Zhang, Y. T. 2004.

Guest Editorial Introduction to the Special Section on

M-Health: Beyond Seamless Mobility and Global

Wireless Health-Care Connectivity. IEEE

Transactions on Information Technology in

Biomedicine, 8(4), 405-414.

Moutacalli, M. T., Marmen, V., Bouzouane, A. and

Bouchard, B. 2013. Activity Pattern Mining using

Temporal Relationships in a Smart Home. IEEE

Symposium on Computational Intelligence in

Healthcare and e-health (CICARE), 83-87.

Jovanov, E., Lords, A., Raskovic, D., Cox, P., Adhami, R.

and Andrasik, F. 2003. Stress monitoring using a

distributed wireless intelligent sensor system. IEEE

Engineering in Medicine and Biology Magazine,

22(3), 49-55.

Son, Y.-S., Jo, J., Park, J.-H. and Pulkkinen, T. 2013.

Diabetic Patient Care using Home User Activity

Recognition. ICTC 2013, 191-196.

Manley, E. D. and Deogun, J. S. 2007. Location Learning

for Smart Homes. 21st International Conference on

Advanced Information Networking and Applications

Workshops (AINAW'07).

Wang, J., Zhang, Z., Li, B., Lee, S. and Sherratt, R. S.

2014. An Enhanced Fall Detection System for Elderly

Person Monitoring. IEEE Transactions on Consumer

Electronics, 60(1), 23-29.

Jalal, A., Uddin, M. Z. and Kim, T.-S. 2012. Depth Video-

based Human Activity Recognition System Using

Translation and Scaling Invariant Features for Life

Logging at Smart Home. IEEE Transactions on

Consumer Electronics, 58(3), 863-871.

Jalal, A. and Kamal, S. 2014. Real-Time Life Logging via

a Depth Silhouette-based Human Activity Recognition

System for Smart Home Services. Proc. 11th IEEE

International Conference on Advanced Video and

Signal Based Surveillance (AVSS), 74-80.

Akyildiz, I. F., Su, W., Sankarasubramaniam, Y. and

Cayirci, E. 2002. Wireless sensor networks: a survey.

Computer Networks, 38, 393-422.

Alemdar, H. and Ersoy, C. 2010. Wireless sensor

networks for healthcare: A survey, Computer

Networks, 54(10), 2688-2710.

Mitilineos, S. A., Ioannidis, Z. C., Vassiliadis, S. G.,

Potirakis, S. M., Stathopoulos, N. A., Savaidis, S. P.

and Vossou, C. G. 2013. Textile antennas challenges

and applications: A spiral antenna design for textile

development. Proc.1st Intern. Conf. on Technics,

Technologies and Education ICTTE 2013.

Vassiliadis, S., Provatidis, Ch., Prekas, K. and Rangoussi,

M. 2004. Electrically Conductive Spun Yarns. Proc.

Xth International Izmir Textile and Apparel

Symposium, 37-49.

Vassiliadis, S., Potirakis, S., Rangoussi, M. and Provatidis,

C. 2011. Design of a wearable digital system for the

gait monitoring. Proc. 1st SMARTEX-Egypt.

Tuna, G., Gungor, V. C. and Potirakis, S. M. 2014.

Wireless Sensor Network-Based Communication for

Cooperative Simultaneous Localization and Mapping.

Computers and Electrical Engineering. DOI:

10.1016/j.compeleceng.2014.03.003.

Kiokes, G., Vossou, C., Chatzistamatis, P., Potirakis, S.

M., Vassiliadis, S., Prekas, K., Tuna, G. and Gulez, K.

2014. Performance evaluation of a communication

protocol for vital signs sensors used for the monitoring

of athletes. International Journal of Distributed

Sensor Networks, 2014 (2014), Article ID 453182.

DOI: 10.1155/2014/453182.

Hulsmann, M. and Windt, K. (Eds.) 2007. Understanding

autonomous cooperation and control in logistics,

Springer-Verlag.

Buratti, C. and Verdone, R. 2009. Performance analysis of

IEEE 802.15.4 non beacon-enabled mode. IEEE

Transactions on Vehicular Technology, 58(7), 3480-

3493.

Zhu, C., Zheng, C. and Han, G. 2012. A survey on

coverage and connectivity issues in wireless sensor

Networks. Journal of Network and Computer

Applications, 35(2012), 619-632.

Francesco, M. D., Anastasi, G., Conti, M., Das, S. K. and

Neri, V. 2011. Reliability and energy-efficiency in

IEEE 802.15.4/ZigBee sensor networks: an adaptive

and cross-layer approach. IEEE Journal on Selected

Areas in Communications, 29(8), 1508-1524.

Mao, G., Fidan, B. and Anderson. B. D. O. 2007. Wireless

sensor network localization techniques. Computer

Networks, 51(10), 2529-2553.

Han, G., Xu, H., Duong, T. Q., Jiang, J. and Hara, T.

2013. Localization algorithms of Wireless Sensor

Networks: a survey. Telecommunication Systems,

52(4), 2419-2436.

Incel, O., Ghosh, A., Krishnamachari, B. and

Chintalapudi, K. 2012. Fast Data Collection in Tree-

Based Wireless Sensor Networks. IEEE Transactions

on Mobile Computing, 11(1), 86-99.

Son, J.-Y., Park, J.-H., Moon, K.-D. and Lee, Y.-H. 2011.

Resource-aware smart home management system by

constructing resource relation graph. IEEE

Transactions on Consumer Electronics, 57(3), 1112-

1119.

Han, D.-M. and Lim, J.-H. 2010. Smart home energy

management system using IEEE 802.15.4 and zigbee.

IEEE Transactions on Consumer Electronics, 56(3),

1403-1410.

Jiang, L., Liu, D.-y. and Yang, B. 2004. Smart home

research. Proceedings of 2004 International

Conference on Machine Learning and Cybernetics, 2,

659-663.

Daş, R., Türkoğlu, İ. and Poyraz, M. 2008. Bir Web

Sitesine Ait Kullanıcı Erişim Kayıtlarının Web

Kullan

ım Madenciliği Yöntemiyle Analizi: Fırat

DATA2015-4thInternationalConferenceonDataManagementTechnologiesandApplications

176

Üniversitesi Örneği (in Turkish). e-Journal of New

World Sciences Academy, 3(2), 310-320.

Daş, R. and Türkoğlu, İ. 2009. Creating Meaningful Data

From Web Logs For Improving The Impressiveness of

a Website By Using Path Analysis Method. Expert

Systems with Applications, 36(3), 6635-6644.

Goel, N. and Jha, C. K. 2013. Analyzing Users Behavior

from Web Access Logs using Automated Log

Analyzer Tool. International Journal of Computer

Applications, 62(2), 29-33.

Taghavi, M., Patel, A., Schmidt, N., Wills, C. and Tew, Y.

2012. An analysis of web proxy logs with query

distribution pattern approach for search engines.

Computer Standards & Interfaces, 34(1), 162-170.

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