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Towards a Service-Oriented Architecture for a Mobile Assistive System with Real-time Environmental S

清华大学 辅仁网/2017-07-07
Towards a Service-Oriented Architecture for a Mobile Assistive System with Real-time Environmental Sensing
Darpan Triboan,Liming Chen,Feng Chen,Zumin Wang*
∙ Darpan Triboan, Liming Chen, and Feng Chen are with the Context, Intelligence, and Interaction Research Group (CIIRG), De Montfort University, Leicester, LE1 9BH, UK. E-mail: darpan.triboan@my365; liming.chen@dmu.ac.uk, fengchen@dmu.ac.uk.
∙ Zumin Wang is with the Department of Information Engineering, Dalian University, Dalian 116622, China.

摘要:

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AbstractWith the growing aging population, age-related diseases have increased considerably over the years. In response to these, Ambient Assistive Living (AAL) systems are being developed and are continually evolving to enrich and support independent living. While most researchers investigate robust Activity Recognition (AR) techniques, this paper focuses on some of the architectural challenges of the AAL systems. This work proposes a system architecture that fuses varying software design patterns and integrates readily available hardware devices to create Wireless Sensor Networks (WSNs) for real-time applications. The system architecture brings together the Service-Oriented Architecture (SOA), semantic web technologies, and other methods to address some of the shortcomings of the preceding system implementations using off-the-shelf and open source components. In order to validate the proposed architecture, a prototype is developed and tested positively to recognize basic user activities in real time. The system provides a base that can be further extended in many areas of AAL systems, including composite AR.

KeywordsActivities of Daily Living (ADL)Service-Oriented Architecture (SOA)semantic webontology modelingWeb Ontology Language (OWL)Activity Recognition (AR)Smart Homes (SH)Wireless Sensor Networks (WSNs)
Corresponding Authors:Zumin Wang
Issue Date: 13 December 2016
Cite this article:
Darpan Triboan,Liming Chen,Feng Chen, et al. Towards a Service-Oriented Architecture for a Mobile Assistive System with Real-time Environmental Sensing[J]. Tsinghua Science and Technology, 2016, 21(6): 581-597.
URL:
http://tst.tsinghuajournals.com/EN/10.1109/TST.2016.7787002OR http://tst.tsinghuajournals.com/EN/Y2016/V21/I6/581


Fig. 1System architecture overview: Initial implementation of the SMART system (2009).
Fig. 2System architecture overview: Service-oriented implementation of the SMART system (2012).
System detailsSystem version
SMARTProposed
(2009)(2012)(2016)
PurposeActivity recognition using Smart HomesReengineered based on the initial versionReengineered with SOA implementation.
Implementation typeStandalone web applicationSOA; SOAP-based; browser-based interfaceSOA; REST-based; SSE and mobile application
Language (s)C#, ASP.NET, dotNet/GWT basedJava, AJAX, JavaScript, HTML/CSS, SQLJava and SRARQL
Main dependenciesSemantic Web (SemWeb), AJAX, Silverlight, Euler, and PelletPELLET (reasoning tool), Apache Jena, Mule ESB, Glassfish, JAX-WS, H2 RDBMS, AJAXApache Jena, Fuseki Server, JAX-RS 1.1, Jersey 2, Jersey SSE, XBee Java lib, Tyrus Web sockets, Apache Tomcat Server and Android Studio.
InterfaceBrowser-basedBrowser-basedMobile-based (Android application)
PortabilitySingle computerOne-to-manyOne-to-many
LicensingProprietaryOpen-sourceOpen-source
Table 1Comparison between predecessors and the proposed system.
Fig. 3The proposed mobile SMART system using SOA and semantic web technologies.
Fig. 4Software: Breakdown of the “Sensor Utils” package.
Fig. 5Hardware: Connectivity diagram of sensing devices.
Fig. 6SSE mechanism for real-time message flow of sensing and inferencing results between client and web service.
Fig. 7Pseudocode for executing a SPARQL query on the server endpoint using Jena API.
Fig. 8Layered object properties for bucket-based structure data.
Fig. 9Bucket-based approach for data structuring using object properties.
Fig. 10Managing user preferences and ADL simulation mode interface.
Fig. 11ADL simulation result of two possible preferences with their missing sensors to complete the activity.
Fig. 12User preference management interface in action.
Fig. 13Illustrating the inferencing steps taken using SPARQL query language.
Fig. 14Patient’s main menu and UI of managing medicines doses.
Activity number (#)UAPSensor objects sequenceTotal number of sensors
1MakeIndian TeaKitchenDoor1, KitchenCupboard1, TeaBagJar, IndianTeaSpiceJar, SugarJar, Kettle1, KitchenWaterTap1, Fridge1, MilkBottle1,11
2MakeCappuccino CoffeeKitchenDoor1, KitchenCupboard1, CappuccinoBagJar, SugarJar, Kettle1, KitchenWaterTap1, Fridge1, MilkBottle1, EatingSpoon1, Mug110
3MakeStawberry JuiceKitchenDoor1, KitchenCupboard1, JuicerMixerCup1, SugarJar, KitchenCupboard2, ChoppingBoard1, Knife1, Fridge1, StawberryPacket1, MilkBottle1, KitchenWaterTap1, GlassCup1, JuicerMixer1,13
4MakingChips AndBeansKitchenDoor1, FridgeFreezer1, ChipsBag1, KitehenCupboard2, OvenTray1, HeinzBakedBeansCan1, KitchenWaterTap1, MicrowaveBowl1, OvenDoor1, MicrowaveDoor1, CeramicPlate111
5MakePastaKitchenDoor1, KitchenCupboard1, PastaBag1, PastaPot1, KitchenWaterTap1, WoodCookingSpoon, PastaSauce, SaltBottle18
6TakingMedicineKitchenCupboard1, MedicineContainer1, GlassContainer1, KitchenWaterTar14
Table 2User activity preferences with the associated total number of sensor objects.
Scenario typesExact no. of sensorsExtra sensors activationFaulty/missing
TP1√××
TP2×√×
TP3××√
Table 3AR test scenario types.
Activity number (#)Examples of tests specifications
1TP1: #1,
TP2: #1, add KitchenCupboard2 and GlasCup1.
TP3: #1, swap TeaBagJar and OvenDoor1.
2TP1: #2,
TP2: #2, add KitchenCupboard2 and GlasCup1.
TP3: #2, replace Mug1 with GlassCup1.
Table 4Two examples of AR test cases.
Activity number (#)Test typeExp1 (ms)Exp2 (ms)Exp2 (ms)Avg. (ms)Avg. per activity number (ms)
　TP13890398851274335.00　
1TP25175417648024717.674472.33
　TP34172414547764364.33　
　TP14013395344394135.00　
2TP24131413547254330.334287.67
　TP34275428846304397.67　
　TP13926392343534067.33　
3TP24303431645714396.674410.56
　TP35310422547684767.67　
　TP14116417544524247.67　
4TP26330447446955166.334636.33
　TP34410446146144495.00　
　TP14150426544094274.67　
5TP24446441459194926.334584.11
　TP34497453346244551.33　
　TP14166480142714412.67　
6TP24532455645634550.334473.56
　TP34415446044984457.67　
　　　　　　4477.43
Table 5Results showing average activity inferencing duration from the last activities recorded.

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