Knowledge-based health service considering user convenience using hybrid Wi-Fi P2P

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• 作者：Kyungyong Chung ; Joo-Chang Kim ; Roy C. Park
• 关键词：u ; Healthcare ; Knowledge ; based health ; M2M ; Hybrid Wi ; Fi ; P2P ; Human UX/UI ; Healthcare ; Chronic disease
• 刊名：Information Technology and Management
• 出版年：2016
• 出版时间：March 2016
• 年：2016
• 卷：17
• 期：1
• 页码：67-80
• 全文大小：4,050 KB
• 参考文献：1.Michael R (2007) Applying emergent ubiquitous technologies in health: the need to respond to new challenges of opportunity, expectation, and responsibility. Int J Med Inform 76(3):349–352
  2.Lim JE, Choi OH, Na HS, Baik DK (2009) A Context-aware fitness guide system for exercise optimization in U-health. IEEE Trans Inf Technol Biomed 13(3):370–379CrossRef
  3.Kim JK, Kim J, Lee D, Chung KY (2014) Ontology driven interactive healthcare with wearable sensors. Multimed Tools Appl 71(2):827–841CrossRef
  4.Park R, Jung H, Shin DK, Cho YH, Lee KD (2014) Telemedicine health service using LTE-Advanced relay antenna. Pers Ubiquit Comput 18(6):1325–1335CrossRef
  5.Khan AM, Lee SW (2013) Need for a context-aware personalized health intervention system to ensure long-term behavior change to prevent obesity. In: Proceedings of the 2013 5th international workshop on software engineering in health care (SEHC), pp 71–74
  6.Valentin G, Howard AM (2013) Dealing with childhood obesity: passive versus active activity monitoring approaches for engaging individuals in exercise. In: Proceedings of ISSNIP biosignals and biorobotics conference, pp 1–5
  7.Jung EY, Kim JH, Chung KY, Park DK (2013) Home health gateway based healthcare services through U-health platform. Wirel Pers Commun 73(2):207–218CrossRef
  8.Kang SK, Chung KY, Ryu JK, Rim KW, Lee JH (2013) Bio-interactive healthcare service system using lifelog based context computing. Wirel Pers Commun 73(2):341–351CrossRef
  9.Han DS, Lee MK, Park SJ (2010) THE-MUSS: mobile u-health service system. Biomedicine 97(2):178–188
  10.Elgazzar K, Aboelfotoh M, Martin P, Hassanein HS (2012) Ubiquitous health monitoring using mobile web services. Proced Comput Sci 10:332–339CrossRef
  11.Kim JH, Chung K (2014) Ontology-based healthcare context information model to implement ubiquitous environment. Multimed Tools Appl 71(2):873–888CrossRef
  12.Cheng SH, Huang CY (2013) Coloring-based inter-WBAN scheduling for mobile wireless body area networks. IEEE Trans Parallel Distrib Syst 24(2):250–259CrossRef
  13.Ayyildiz C, Erman K, Ozgul ME, Erman AT, Gungor C (2012) TeleHealth: intelligent healthcare with M2M communication module. In: Proceedings of the 2th ACM workshop on mobile systems, applications, and services for healthcare 2012. doi:10.​1145/​2396276.​2396285
  14.Oh JY, Kim JK, Lee HS, Choi SS (2010) Phase rotation shift keying for low power and high performance WBAN in-body systems. In: Proceedings of the international conference on information and communication technology convergence, pp 28–32
  15.Shon T, Koo B, Choi H, Park Y (2009) Security architecture for IEEE 802.15.4-based wireless sensor network. In: Proceedings of the 4th international symposium on wireless pervasive computing, pp 1–5
  16.Higuera JE, Polo J (2011) IEEE 1451 standard in 6LoWPAN sensor networks using a compact physical-layer transducer electronic datasheet. IEEE Trans Instrum Meas 60(8):2751–2758CrossRef
  17.Ayoub ZT, Ouni S, Kamoun F (2011) Association criteria to optimize energy consumption and latency for IEEE 802.15.4/ZigBee wireless sensor networks. In: Proceedings of the 4th joint IFIP wireless and mobile networking conference (WMNC), pp 1–4
  18.Lee MJ, Zhang R, Zheng J, Ahn GS (2010) IEEE 802.15.5 WPAN mesh standard-low rate part: meshing the wireless sensor networks. IEEE J Sel Areas Commun 28(7):973–983CrossRef
  19.Xin Q, Manne F, Zhang Y, Wang X (2012) Almost optimal distributed M2M multicasting in wireless mesh networks. Theoret Comput Sci 439:69–82CrossRef
  20.Park Y, Min D (2013) Design and implementation of M2M-HLA adaptor for integration of ETSI M2M platform and IEEE HLA-based simulation system In: Proceedings of the 5th international conference on computational intelligence, modelling and simulation, pp 315–320
  21.Hung SH, Chen CH, Tu CH (2012) Performance evaluation of machine-to-machine (M2M) systems with virtual machines. In: Proceedings of the 15th international symposium on wireless personal multimedia communications (WPMC), pp 24–27
  22.Benner M, Schope L (2011) Using continua health alliance standards—implementation and experiences of IEEE 11073. In: Proceedings of the IEEE 12th international conference on mobile data management, pp 40–45
  23.Piniewski B, Muskens J, Estevez L, Carroll R, Cnossen R (2010) Empowering healthcare patients with smart technology. Computer 43(7):27–34CrossRef
  24.Wartena F, Muskens J, Schmitt L, Petkovic M (2010) Continua: the reference architecture of a personal telehealth ecosystem. In: Proceedings of the 12th IEEE international conference on e-health networking applications and services (Healthcom), pp 1–6
  25.Jo S, Lee TK, Bang EW, Kim CJ, Im HJ, Kwon YJ, Jo YB, Baek DM, Joo YS (2010) Factors associated with unmet needs for medical care among island inhabitants in Korea. Korean Soc Agric Med Commun Health 35(2):151–164CrossRef
  26.Kbar G, Mansoor W, Naim A (2010) Voice over IP mobile telephony using WIFI P2P. In: Proceedings of the 6th international conference on wireless and mobile communications (ICWMC), pp 268–273
  27.Rajasekhar S, Khalil I, Tari Z (2004) A scalable and robust QoS architecture for Wifi p2p networks. In: Proceedings of the first international conference on distributed computing and internet technology, pp 65–74
  28.Park R, Jung H, Shin DK, Kim GJ, Yoon KH (2015) M2M-based smart health service for human UI/UX using motion recognition. Clust Comput 18(1):221–232CrossRef
  29.Lee SH, Chung KY, Lim JS (2014) Detection of ventricular fibrillation using hilbert transforms, phase-space reconstruction, and time-domain analysis. Pers Ubiquit Comput 18(6):1315–1324CrossRef
  30.Chung KY (2014) Recent trends on convergence and ubiquitous computing. Pers Ubiquit Comput 18(6):1291–1293CrossRef
  31.Jung H, Chung K (2015) Knowledge based dietary nutrition recommendation for obesity management. Inf Technol Manag. doi:10.​1007/​s10799-015-0218-4
  32.Park RC, Jung H, Chung K, Yoon KH (2015) Picocell based telemedicine health service for human UX/UI. Multimed Tools Appl 74(7):2519–2534CrossRef
  33.Chung K, Boutaba R, Hariri S (2014) Recent trends in digital convergence information system. Wirel Pers Commun 79(4):2409–2413CrossRef
  34.Jung H, Yang JG, Woo JI, Lee BM, Ouyang J, Chung K, Lee YH (2015) Evolutionary rule decision using similarity based associative chronic disease patients. Clust Comput 18(1):279–291CrossRef
  35.Jung H, Chung K (2015) Sequential pattern profiling based bio-detection for smart health service. Clust Comput 18(1):209–219CrossRef
  36.Medical Device (2012) Mobile health (mHealth) technology, revolution of medical service and management, GilKorea
  37.Ibrahima N, Ousmane S, Claude L, Hamadou SH (2014) Enhanced HIP-based micro-mobility and macro-mobility management by proactive signaling scheme. In: Proceedings of the IEEE international conference on parallel and distributed system, pp 780–786
  38.Lindberg DV, More H (2014) Blind categorical deconvolution in two-level hidden Markov models. IEEE Trans Geosci Remote Sens 52(11):7435–7447CrossRef
  39.Rho MJ, Kim HS, Chung K, Choi IY (2015) Factors influencing the acceptance of telemedicine for diabetes management. Clust Comput 18(1):321–331CrossRef
  40.Kim SH, Chung K (2015) Emergency situation monitoring service using context motion tracking of chronic disease patients. Clust Comput 18(2):747–759CrossRef
  41.Boutaba R, Chung KY, Gen M (2014) Recent trends in interactive multimedia computing for industry. Clust Comput 17(3):723–726
  
• 作者单位：Kyungyong Chung (1)
  Joo-Chang Kim (2)
  Roy C. Park (3)
  
  1. School of Computer Information Engineering, Sangji University, 83, Sangjidae-gil, Wonju-si, Gangwon-do, 220-702, Korea
  2. Intelligent System Lab., School of Computer Information Engineering, Sangji University, 83, Sangjidae-gil, Wonju-si, Gangwon-do, 220-702, Korea
  3. Division of Computer Engineering, Dongseo University, 47 Jurye-ro, Sasang-gu, Busan, 617-716, Korea
  
• 刊物主题：Business Information Systems; Data Structures, Cryptology and Information Theory; Operations Research/Decision Theory; Computer Communication Networks; Business/Management Science, general;
• 出版者：Springer US
• ISSN：1573-7667

文摘

Recently, with changing paradigms in health, the focus of healthcare is shifting from treatment after contracting disease to prevention and early diagnosis of disease. Accordingly, the healthcare paradigm is changing from diagnosis and treatment to preventive management, emphasizing prevention of chronic diseases, such as obesity. In particular, obesity in children and adolescents has become a global issue. Lifestyle and health management using BT–IT convergence is needed to improve and manage the health of children and adolescents, and convenience and accessibility must be improved. For that, use of a machine-to-machine (M2M) u-health cluster that allows wireless network connection is increasing, along with wireless networks for measuring biometrics. Expanded to communications between people and objects as well as between objects, M2M refers to the next-generation convergence infra-architecture that offers intelligent services through various media. Because various wireless devices form a cluster when building a service platform using M2M, when the number of users with various M2M devices increases, data traffic increases and causes network overload, deteriorating system performance. To solve this problem, services are increasingly being built by combining a conventional network and Wi-Fi technology. However, in an M2M network, there is a limitation due to low transfer speed, because the network processes biometrics and data through different sensor nodes, and wireless communications based on the system is composed of different wireless sensor nodes. Thus, in this paper, we proposed a knowledge-based health service considering user convenience using a hybrid wireless fidelity (Wi-Fi) peer-to-peer (P2P) architecture. For knowledge-based health services in conventional M2M-based smart health services, hybrid Wi-Fi P2P and wireless devices must be linked. Because there are different ways to link hybrid Wi-Fi P2P devices, depending on the network environment, in this study, a dynamic configuration mechanism is applied to Wi-Fi P2P linkage of wireless devices in an M2M environment. The proposed service provides a high-quality health service (whenever patients use the knowledge-based health service) by building a network using a dispersed cross-layer optimization algorithm that optimizes variables of the transmission control protocol/internet protocol stack in order to improve the energy efficiency of the u-health sensor network and system reliability.