Real-Time Requirements Analysis in Smart Healthcare Monitoring Systems: A Case Study of an ATmega328P-Based Health Monitoring Device
DOI:
https://doi.org/10.65405/fr7jkm87Keywords:
Smart Healthcare Monitoring, Real-Time Systems, ATmega328P, WCET Analysis, Rate Monotonic Scheduling, Earliest Deadline First, Response-Time Analysis, Embedded Systems, Healthcare IoT.Abstract
Smart healthcare monitoring systems have become increasingly important for continuous patient observation and timely medical intervention. However, many low-cost embedded healthcare devices are developed with limited consideration of real-time requirements, which may lead to delayed responses during emergency situations. This paper presents a real-time requirements analysis of an ATmega328P-based healthcare monitoring device capable of measuring body temperature, heart rate, blood oxygen saturation (SpO₂), and blood pressure while providing remote alert functionality through GSM communication.
The study begins with an analysis of the implemented prototype and its software architecture. The original system employs a sequential super-loop execution model containing multiple blocking delays that negatively affect responsiveness and emergency handling performance. To address these limitations, a hybrid real-time architecture is proposed that combines event-driven processing, interrupt-assisted emergency detection, and priority-based task scheduling.
Worst-Case Execution Time (WCET) estimation, response-time analysis, Rate Monotonic Scheduling (RMS), and Earliest Deadline First (EDF) scheduling techniques were applied to evaluate the timing behavior of the system. A Python-based simulation framework was developed to assess processor utilization, schedulability, response times, deadline satisfaction, and emergency alert latency.
The results show that the proposed task set achieves a processor utilization of 14.5% and remains schedulable under both RMS and EDF policies. Response-time analysis confirmed that all tasks meet their timing constraints. Furthermore, the proposed hybrid architecture reduced emergency alert latency from approximately 20.5 seconds in the original implementation to 160 milliseconds, representing a latency reduction of 99.22%.
The findings demonstrate that integrating real-time scheduling mechanisms and interrupt-assisted emergency management can significantly improve the responsiveness, predictability, and reliability of resource-constrained healthcare monitoring systems while maintaining compatibility with low-cost embedded platforms.
Downloads
References
[1] C. L. Liu and J. W. Layland, “Scheduling Algorithms for Multiprogramming in a Hard-Real-Time Environment,” Journal of the ACM, vol. 20, no. 1, pp. 46–61, 1973.
[2] J. W. S. Liu, Real-Time Systems. Upper Saddle River, NJ, USA: Prentice Hall, 2000.
[3] G. C. Buttazzo, Hard Real-Time Computing Systems: Predictable Scheduling Algorithms and Applications, 3rd ed. New York, NY, USA: Springer, 2011.
[4] M. Wolf, Computers as Components: Principles of Embedded Computing System Design, 4th ed. Burlington, MA, USA: Morgan Kaufmann, 2016.
[5] Microchip Technology Inc., ATmega328P Datasheet, Chandler, AZ, USA, 2018.
[6] N. M. Khalafullah, N. E. Omran, M. Alfordogh, R. Almorabit, and M. Karaim, “Portable Emergency Health Monitoring System Based on Arduino Nano (ATmega328P),” International Science and Technology Journal (ISTJ), Special Issue of the 2nd Libyan International Conference on Applied and Engineering Sciences (LICASE-2), pp. 3–12, Oct. 2024.
[7] A. Darwish and A. E. Hassanien, “Wearable and Implantable Wireless Sensor Network Solutions for Healthcare Monitoring,” Sensors, vol. 11, no. 6, pp. 5561–5595, 2011.
[8] M. S. Hossain and G. Muhammad, “Cloud-Assisted Industrial Internet of Things (IIoT) Enabled Framework for Health Monitoring,” Computer Networks, vol. 101, pp. 192–202, 2016.
[9] Maxim Integrated, MAX30100 Pulse Oximeter and Heart-Rate Sensor IC for Wearable Health, San Jose, CA, USA, 2014.
[10] Melexis Technologies NV, MLX90614 Infrared Thermometer Sensor Datasheet, Belgium, 2021.
[11] P. A. Shaltis, A. T. Reisner, and H. H. Asada, “Cuffless Blood Pressure Monitoring Using Hydrostatic Pressure Changes,” IEEE Transactions on Biomedical Engineering, vol. 55, no. 6, pp. 1775–1777, 2008. [12] T. Yilmaz, R. Foster, and Y. Hao, “Detecting Vital Signs with Wearable Wireless Sensor Systems,” Sensors, vol. 10, no. 12, pp. 10837–10862, 2010.
[13] P. D. P. Adi and A. Kitagawa, “Performance Evaluation of ZigBee-Based Wireless Sensor Networks,” International Journal of Advanced Computer Science and Applications, vol. 10, no. 6, 2019.
[14] Arduino, Arduino Uno Rev3 Technical Specifications, 2024.
[15] Arduino, Arduino Nano Technical Specifications, 2024.
[16] SIMCom Wireless Solutions, SIM900A GSM/GPRS Module Hardware Design Guide, 2023.
[17] H. Kopetz, Real-Time Systems: Design Principles for Distributed Embedded Applications, 2nd ed. New York, NY, USA: Springer, 2011.
[18] A. Burns and A. J. Wellings, Real-Time Systems and Programming Languages: Ada, Real-Time Java and C/Real-Time POSIX, 4th ed. Harlow, U.K.: Addison-Wesley, 2009.
[19] M. Joseph and P. Pandya, “Finding response times in a real-time system,” The Computer Journal, vol. 29, no. 5, pp. 390–395, 1986.
[20] K. Tindell, A. Burns, and A. J. Wellings, “An extendible approach for analyzing fixed priority hard real-time tasks,” Real-Time Systems, vol. 6, no. 2, pp. 133–151, Mar. 1994.
[21] A. K. Mok, Fundamental Design Problems of Distributed Systems for the Hard-Real-Time Environment. Cambridge, MA, USA: MIT Press, 1983.
[22] J. A. Stankovic, “Misconceptions about real-time computing: A serious problem for next-generation systems,” IEEE Computer, vol. 21, no. 10, pp. 10–19, Oct. 1988.
[23] G. C. Buttazzo, “Rate monotonic vs. EDF: Judgment day,” Real-Time Systems, vol. 29, no. 1, pp. 5–26, Jan. 2005.
[24] A. Burns, “Preemptive priority-based scheduling: An appropriate engineering approach,” in Advances in Real-Time Systems, S. H. Son, Ed. Englewood Cliffs, NJ, USA: Prentice Hall, 1994, pp. 225–248.
[25] Alnnale, T. (2026). Predictive Governance in Digital Enterprises: An LSTM-Enhanced Deep Learning Framework for Economic Optimization of IT Incident Management Using Enriched Process Logs. Al-Farooq Journal of Sciences, 2(3), 86-113.
[26] Krichene, E., Hmadi, M. S. A., & Al-Gajamiya, S. K. (2026). A Fair Comparative Framework for Time-Series Forecasting Using ARIMAX, XGBoost, and LSTM: Evidence from Libya. Al-Farooq Journal of Sciences, 2(3), 69-85.
