Design of Application Framework for Vital Monitoring Mobile-Based System

Authors

DOI:

https://doi.org/10.26555/jiteki.v10i2.28416

Keywords:

Application Framework, Feature-Oriented Domain Analysis, Vital Sign Monitoring, Mobile Application, Hot Spots

Abstract

In the realm of modern healthcare, continuous monitoring can leverage the affordable wearable devices available on the market to manage costs. However, these devices face several limitations, such as restricted access for other parties, including nurses and doctors, and the need for redevelopment to integrate new devices for data accessibility. This study addresses these challenges by establish an application framework tailored for mobile-based systems, by ensuring accessibility by external parties. The research contribution is encompassing two key aspects: the potential implementation of Feature-Oriented Domain Analysis (FODA) in the domain of mobile-based vital sign monitoring, particularly in the absence of prior studies addressing the same context, and the identification reusable (frozen spots) and adaptable components (hot spots), providing guidance for the development of mobile-based vital sign monitoring. FODA is utilized during the analysis activity. Design patterns are then implemented when creating class diagrams in the design activity. This study finding reveal 7 primary features and 18 sub-features essential that must be incorporated into the application framework. The framework includes 5 hot spots and 7 frozen spots, with the implementation of Strategy and Filter design patterns. In conclusion, the developed application framework represents a significant advancement in vital sign monitoring, particularly within mobile-based systems. This study emphasizing limitations in analysis and design phases. In future research, the focus will shift to the construction and stabilization phases, all crucial for refining the framework. Implementing framework in actual applications can aid in developing vital sign monitoring systems and potentially improving healthcare outcomes.

References

J. C. H. Soto, I. Galdino, E. Caballero, V. Ferreira, D. Muchaluat-Saade, and C. Albuquerque, “A survey on vital signs monitoring based on Wi-Fi CSI data,” Comput. Commun., vol. 195, no. January, pp. 99–110, 2022, https://doi.org/10.1016/j.comcom.2022.08.004.

M. Z. Uddin, M. M. Hassan, A. Alsanad, and C. Savaglio, “A body sensor data fusion and deep recurrent neural network-based behavior recognition approach for robust healthcare,” Inf. Fusion, vol. 55, pp. 105–115, Mar. 2020, https://doi.org/10.1016/J.INFFUS.2019.08.004.

A. Haleem, M. Javaid, R. P. Singh, R. Suman, and S. Rab, “Biosensors applications in medical field: A brief review,” Sensors Int., vol. 2, no. May, p. 100100, 2021, https://doi.org/10.1016/j.sintl.2021.100100.

E. Ullah, M. M. Baig, H. GholamHosseini, and J. Lu, “Vital signs and early warning score monitoring: perceptions of clinical staff about current practices and introducing an electronic rapid response system,” Heliyon, vol. 8, no. 10, p. e11182, 2022, https://doi.org/10.1016/j.heliyon.2022.e11182.

C. Dall’Ora et al., “How long do nursing staff take to measure and record patients’ vital signs observations in hospital? A time-and-motion study,” Int. J. Nurs. Stud., vol. 118, p. 103921, Jun. 2021, https://doi.org/10.1016/j.ijnurstu.2021.103921.

T. Shaik et al., “FedStack: Personalized activity monitoring using stacked federated learning,” Knowledge-Based Syst., vol. 257, p. 109929, Dec. 2022, https://doi.org/10.1016/J.KNOSYS.2022.109929.

A. Haleem, M. Javaid, R. P. Singh, and R. Suman, “Telemedicine for healthcare: Capabilities, features, barriers, and applications,” Sensors Int., vol. 2, no. July, p. 100117, 2021, https://doi.org/10.1016/j.sintl.2021.100117.

A. Haleem, M. Javaid, R. Pratap Singh, and R. Suman, “Medical 4.0 technologies for healthcare: Features, capabilities, and applications,” Internet Things Cyber-Physical Syst., vol. 2, no. February, pp. 12–30, 2022, https://doi.org/10.1016/j.iotcps.2022.04.001.

Y. Xie et al., “Integration of Artificial Intelligence, Blockchain, and Wearable Technology for Chronic Disease Management: A New Paradigm in Smart Healthcare,” Curr. Med. Sci., vol. 41, no. 6, pp. 1123–1133, 2021, https://doi.org/10.1007/s11596-021-2485-0.

Z. xia Lu et al., “Application of AI and IoT in Clinical Medicine: Summary and Challenges,” Curr. Med. Sci., vol. 41, no. 6, pp. 1134–1150, 2021, https://doi.org/10.1007/s11596-021-2486-z.

J. Aguilar-Toran, J. Punter-Villagrasa, X. Munoz, and P. Miribel-Catala, “Home hospitalization system for the remotely and continuous monitoring of chronic patients,” in IECON Proceedings (Industrial Electronics Conference), 2022, pp. 1–6. https://doi.org/10.1109/IECON49645.2022.9968342.

S. Ran et al., “Homecare-Oriented ECG Diagnosis With Large-Scale Deep Neural Network for Continuous Monitoring on Embedded Devices,” IEEE Trans. Instrum. Meas., vol. 71, pp. 1–13, 2022, https://doi.org/10.1109/TIM.2022.3147328.

M. Menniti, G. Oliva, F. Lagana, M. G. Bianco, A. S. Fiorillo, and S. A. Pullano, “Portable Non-Invasive Ventilator for Homecare and Patients Monitoring System,” in 2023 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2023 - Conference Proceedings, 2023, pp. 1–5. https://doi.org/10.1109/MeMeA57477.2023.10171872.

P. Solic, R. Colella, T. Perkovic, C. G. Leo, S. Sabina, and L. Catarinucci, “Exploring the Potential of Bluetooth Low Energy for Wireless Sensing and On-Board Computation in Remote Health Monitoring,” 2023 8th Int. Conf. Smart Sustain. Technol. Split. 2023, pp. 1–3, 2023, https://doi.org/10.23919/SpliTech58164.2023.10193108.

F. J. Aranda, F. J. Alvarez, F. Parralejo, E. Sansano-Sansano, and R. Montoliu, “A novel method for in-home Gait Speed estimation in Health Monitoring Using Bluetooth Low Energy,” Proc. IEEE Int. Conf. Ind. Technol., vol. 2021-March, pp. 671–676, 2021, https://doi.org/10.1109/ICIT46573.2021.9453578.

S. S. Nawrin, K. Ichiji, S. Yamaki, N. Sugita, and M. Yoshizawa, “A study on indoor physical activity monitoring using Bluetooth signal strength,” LifeTech 2021 - 2021 IEEE 3rd Glob. Conf. Life Sci. Technol., pp. 489–493, 2021, https://doi.org/10.1109/LifeTech52111.2021.9391957.

Z. Lv and Y. Li, “Wearable Sensors for Vital Signs Measurement: A Survey,” J. Sens. Actuator Networks, vol. 11, no. 1, 2022, https://doi.org/10.3390/jsan11010019.

N. A. Malik, P. Sant, T. Ajmal, and M. Ur-Rehman, “Implantable Antennas for Bio-Medical Applications,” IEEE J. Electromagn. RF Microwaves Med. Biol., vol. 5, no. 1, pp. 84–96, 2021, https://doi.org/10.1109/JERM.2020.3026588.

C. Areia et al., “The impact of wearable continuous vital sign monitoring on deterioration detection and clinical outcomes in hospitalised patients: a systematic review and meta-analysis,” Crit. Care, vol. 25, no. 1, pp. 1–17, 2021, https://doi.org/10.1186/s13054-021-03766-4.

N. El-Rashidy, S. El-Sappagh, S. M. Riazul Islam, H. M. El-Bakry, and S. Abdelrazek, “Mobile health in remote patient monitoring for chronic diseases: Principles, trends, and challenges,” Diagnostics, vol. 11, no. 4, pp. 1–32, 2021, https://doi.org/10.3390/diagnostics11040607.

K. Ragavan, R. Ramalakshmi, V. SrirengaNachiyar, G. G. Priya, and K. Jeyageetha, “Smart Health Monitoring System in Intensive Care Unit using Bluetooth Low Energy and Message Queuing Telemetry Transport Protocol,” in 2023 5th International Conference on Smart Systems and Inventive Technology (ICSSIT), 2023, pp. 284–291. https://doi.org/10.1109/ICSSIT55814.2023.10061050.

M. Donati, A. Celli, A. Ruiu, S. Saponara, and L. Fanucci, “A Telemedicine Service System Exploiting BT/BLE Wireless Sensors for Remote Management of Chronic Patients,” Technologies, vol. 7, no. 1, 2019, https://doi.org/10.3390/technologies7010013.

M. M. Ali, S. Haxha, M. M. Alam, C. Nwibor, and M. Sakel, “Design of Internet of Things (IoT) and Android Based Low Cost Health Monitoring Embedded System Wearable Sensor for Measuring SpO2, Heart Rate and Body Temperature Simultaneously,” Wirel. Pers. Commun., vol. 111, no. 4, pp. 2449–2463, Apr. 2020, https://doi.org/10.1007/s11277-019-06995-7.

J. Na et al., “Development of mHealth Literacy and Digital Health Equity Assessment Scale to Improve Health Equity,” in 2022 19th International Conference on Ubiquitous Robots (UR), 2022, pp. 165–169. https://doi.org/10.1109/UR55393.2022.9826269.

N. Azizah et al., A Vital Sign Monitoring System Exploiting BT/BLE on Low-cost Commercial Smartwatch for Home Care Patients. 2023. https://doi.org/10.1109/ISITIA59021.2023.10221157.

S. S. Kamble, A. Gunasekaran, and S. A. Gawankar, “Achieving sustainable performance in a data-driven agriculture supply chain: A review for research and applications,” Int. J. Prod. Econ., vol. 219, pp. 179–194, 2020, https://doi.org/10.1016/j.ijpe.2019.05.022.

R. Ransing et al., “Mental Health Interventions during the COVID-19 Pandemic: A Conceptual Framework by Early Career Psychiatrists,” Asian J. Psychiatr., vol. 51, no. April, 2020, https://doi.org/10.1016/j.ajp.2020.102085.

L. I. Gonzalez-perez and M. S. Ramirez-montoya, “Components of Education 4.0 in 21st Century Skills Frameworks: Systematic Review,” Sustainability, vol. 14, no. 3, p. 1493, Jan. 2022, https://doi.org/10.3390/su14031493.

O. E. Olorunshola and F. N. Ogwueleka, “Review of System Development Life Cycle (SDLC) Models for Effective Application Delivery,” in Information and Communication Technology for Competitive Strategies ({ICTCS} 2020), Springer Singapore, 2021, pp. 281–289. https://doi.org/10.1007/978-981-16-0739-4_28.

S. Shafiq, A. Mashkoor, C. Mayr-Dorn, and A. Egyed, “A Literature Review of Using Machine Learning in Software Development Life Cycle Stages,” IEEE Access, vol. 9, pp. 140896–140920, 2021, https://doi.org/10.1109/ACCESS.2021.3119746.

H. Al-Matouq, S. Mahmood, M. Alshayeb, and M. Niazi, “A Maturity Model for Secure Software Design: A Multivocal Study,” IEEE Access, vol. 8, pp. 215758–215776, 2020, https://doi.org/10.1109/ACCESS.2020.3040220.

J. Dabrowski, E. Letier, A. Perini, and A. Susi, “Analysing app reviews for software engineering: a systematic literature review,” Empir. Softw. Eng., vol. 27, no. 2, 2022, https://doi.org/10.1007/s10664-021-10065-7.

G. W. Wicaksono, P. B. Nawisworo, E. D. Wahyuni, and Y. M. Cholily, “Canvas Learning Management System Feature Analysis Using Feature-Oriented Domain Analysis (FODA),” IOP Conf. Ser. Mater. Sci. Eng., vol. 1077, no. 1, p. 012041, Feb. 2021, https://doi.org/10.1088/1757-899x/1077/1/012041.

Z. Yahya et al., “Design of Application Framework for Vital Sign Monitoring and Remote Doctor Consultation,” in ACM International Conference Proceeding Series, in SIET ’23. New York, NY, USA: Association for Computing Machinery, 2023, pp. 473–480. https://doi.org/10.1145/3626641.3626942.

G. W. Wicaksono, G. A. Juliani, E. D. Wahyuni, Y. M. Cholily, H. W. Asrini, and Budiono, “Analysis of Learning Management System Features based on Indonesian Higher Education National Standards using the Feature-Oriented Domain Analysis,” 2020 8th Int. Conf. Inf. Commun. Technol. ICoICT 2020, 2020, https://doi.org/10.1109/ICoICT49345.2020.9166459.

S. Sepulveda and A. Cravero, “Reasoning Algorithms on Feature Modeling—A Systematic Mapping Study,” Appl. Sci., vol. 12, no. 11, 2022, https://doi.org/10.3390/app12115563.

C. Camacho, L. Llana, A. Nunez, and M. Bravetti, “Probabilistic software product lines,” J. Log. Algebr. Methods Program., vol. 107, pp. 54–78, 2019, https://doi.org/10.1016/j.jlamp.2019.05.007.

H. Shatnawi and H. C. Cunningham, “Automated Analysis and Construction of Feature Models in a Relational Database Using Web Forms,” in Proceedings of the 2020 ACM Southeast Conference, New York, NY, USA: ACM, Apr. 2020, pp. 233–238. https://doi.org/10.1145/3374135.3385312.

M. Hamdaqa, L. A. P. Met, and I. Qasse, “iContractML 2.0: A domain-specific language for modeling and deploying smart contracts onto multiple blockchain platforms,” Inf. Softw. Technol., vol. 144, 2022, https://doi.org/10.1016/j.infsof.2021.106762.

K. Ko, “A Study on Feature Modeling and Data Transmission Methods of Travel Program Data Service: Focusing on the Domestic Satellite Broadcasting Environment,” J. Digit. Contents Soc., vol. 24, no. 7, pp. 1435–1444, 2023, https://doi.org/10.9728/dcs.2023.24.7.1435.

M. Valja, R. Lagerstrom, U. Franke, and G. Ericsson, A Framework for Automatic IT Architecture Modeling: Applying Truth Discovery, vol. 2019, no. 20. 2019. https://doi.org/10.7250/csimq.2019-20.02.

F. Oquendo, “Coping with Uncertainty in Systems-of-Systems Architecture Modeling on the IoT with SosADL,” in 2019 14th Annual Conference System of Systems Engineering (SoSE), 2019, pp. 131–136. https://doi.org/10.1109/SYSOSE.2019.8753842.

G. Rasool, Y. Hussain, T. Umer, J. Rasheed, S. F. Yeo, and F. Sahin, “Design Patterns for Mobile Games Based on Structural Similarity,” Appl. Sci., vol. 13, no. 2, Jan. 2023, https://doi.org/10.3390/app13021198.

C. Krupitzer, T. Temizer, T. Prantl, and C. Raibulet, “An overview of design patterns for self-adaptive systems in the context of the internet of things,” IEEE Access, vol. 8, no. i, pp. 187384–187399, 2020, https://doi.org/10.1109/ACCESS.2020.3031189.

X. Tërnava, J. Mortara, P. Collet, and D. Le Berre, “Identification and visualization of variability implementations in object-oriented variability-rich systems: a symmetry-based approach,” Autom. Softw. Eng., vol. 29, no. 1, 2022, https://doi.org/10.1007/s10515-022-00329-x.

J. Mortara, X. Ternava, P. Collet, and A. M. Pinna-Dery, “Extending the identification of object-oriented variability implementations using usage relationships,” in ACM International Conference Proceeding Series, in SPLC ’21, vol. Part F171625-B. New York, NY, USA: Association for Computing Machinery, 2021, pp. 91–98. https://doi.org/10.1145/3461002.3473943.

X. Ternava, J. Mortara, and P. Collet, “Identifying and visualizing variability in object-oriented variability-rich systems,” in ACM International Conference Proceeding Series, in SPLC ’19, vol. A. New York, NY, USA: Association for Computing Machinery, 2019, pp. 231–243. https://doi.org/10.1145/3336294.3336311.

M. P. Gagnon, “Context Matters in Evidence Implementation Globally Comment on ‘Stakeholder Perspectives of Attributes and Features of Context Relevant to Knowledge Translation in Health Settings: A Multi-Country Analysis,’” Int. J. Heal. Policy Manag., vol. 11, no. 8, pp. 1580–1583, 2022, https://doi.org/10.34172/ijhpm.2021.179.

T. Sharma, P. Singh, and D. Spinellis, “An empirical investigation on the relationship between design and architecture smells,” Empir. Softw. Eng., vol. 25, no. 5, pp. 4020–4068, 2020, https://doi.org/10.1007/s10664-020-09847-2.

A. Zarras, “The Strategy Configuration Problem and How to Solve It,” in 26th European Conference on Pattern Languages of Programs, in EuroPLoP’21. New York, NY, USA: Association for Computing Machinery, 2022. https://doi.org/10.1145/3489449.3489980.

G. Silva, V. Andrade, R. Re, and R. Meneses, “A Quasi-Experiment to Investigating the Impact of the Strategy Design Pattern on Maintainability,” in Proceedings of the XXXV Brazilian Symposium on Software Engineering, in SBES ’21. New York, NY, USA: Association for Computing Machinery, 2021, pp. 105–114. https://doi.org/10.1145/3474624.3474636.

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2024-06-23

How to Cite

Rizky Ananda, M., Faisal, M. R., Herteno, R., Nugroho, R. A., & Abadi, F. (2024). Design of Application Framework for Vital Monitoring Mobile-Based System. Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika, 10(2), 252–264. https://doi.org/10.26555/jiteki.v10i2.28416

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