Remote Monitoring Technologies for Mental Health in Rwanda
DOI:
https://doi.org/10.70619/vol5iss4pp1-12Keywords:
Remote Monitoring Technologies, Mental Health, Internet of Things, Electrocardiogram, electroencephalogram, Radio Frequency IdentificationAbstract
Nowadays people are more concerned about their health as diseases are increasing day by day and more. Hence this is very important to daily monitor health to minimize death caused by complications and diseases. The integration of the Internet of Things (IoT) in healthcare presents transformative opportunities for managing neuropsychiatric diseases. In Rwanda, where mental health care faces challenges such as limited accessibility and resource constraints, IoT-driven solutions offer innovative pathways to improve remote monitoring, diagnosis, and treatment. The study introduces smart wearable integration with IoT, where customized devices such as wristbands, smart rings, and patches with EEG, ECG, and stress level sensors enable continuous patient health tracking and mood detection. Additionally, IoT-based geofencing for emergency alerts is incorporated to track patient location using RFID and GPS, ensuring immediate intervention if a patient wanders outside a designated safe zone. These innovations enhance real-time monitoring, improve treatment adherence, and strengthen mental healthcare accessibility. In an Internet of Things setting, it suggests a smart health remote monitor system that can track a patient's current location and basic health indicators in real-time. Sensors such as a heartbeat sensor, body temperature sensor, ECG, EEG, and RFID with GPS sensors connected to a microcontroller to control hardware modules and a GSM module, as well as a communication network that connects to the servers, will be utilized in this system to collect data from the patient's body. The medical staff will get the patients' condition through a portal, where they may process and evaluate the patient's current state. The family members will receive the information instantly. The study seeks to bridge healthcare accessibility gaps, improve patient outcomes, and contribute to the advancement of digital mental healthcare systems in Rwanda.
References
J. O. Olmedo-Aguirre, J. Reyes-Campos, G. Alor-Hernández, I. Machorro-Cano, L. Rodríguez-Mazahua, and J. L. Sánchez-Cervantes, “Remote Healthcare for Elderly People Using Wearables: A Review,” Biosensors, vol. 12, no. 2. 2022. doi: 10.3390/bios12020073.
P. Gope et al., “Body Area Sensor Networks Challenges and Opportunities - University of Virginia,” IEEE Sens. J., vol. 13, no. 5, 2016.
S. Majumder, T. Mondal, and M. J. Deen, “Wearable sensors for remote health monitoring,” Sensors (Switzerland), vol. 17, no. 1. 2017. doi: 10.3390/s17010130.
S. Kumar et al., “A wristwatch-based wireless sensor platform for IoT health monitoring applications,” Sensors (Switzerland), vol. 20, no. 6, 2020, doi: 10.3390/s20061675.
M. W. Alam, T. Sultana, and M. S. Alam, “A heartbeat and temperature measuring system for remote health monitoring using wireless body area network,” Int. J. Bio-Science Bio-Technology, vol. 8, no. 1, 2016, doi: 10.14257/ijbsbt.2016.8.1.16.
A. Lanata, G. Valenza, M. Nardelli, C. Gentili, and E. P. Scilingo, “Complexity index from a personalized wearable monitoring system for assessing remission in mental health,” IEEE J. Biomed. Heal. Informatics, vol. 19, no. 1, 2015, doi: 10.1109/JBHI.2014.2360711.
R. K. Pathinarupothi, M. V. Ramesh, and E. Rangan, “Multi-layer architectures for remote health monitoring,” in 2016 IEEE 18th International Conference on e-Health Networking, Applications and Services, Healthcom 2016, 2016. doi: 10.1109/HealthCom.2016.7749443.
S. K. Prabhakar and H. Rajaguru, “Adaboost classifier with dimensionality reduction techniques for epilepsy classification from EEG,” in IFMBE Proceedings, 2018. doi: 10.1007/978-981-10-7419-6_31.
S. Al-Janabi, I. Al-Shourbaji, M. Shojafar, and S. Shamshirband, “Survey of main challenges (security and privacy) in wireless body area networks for healthcare applications,” Egyptian Informatics Journal, vol. 18, no. 2. 2017. doi: 10.1016/j.eij.2016.11.001.
T. Aledavood et al., “Data collection for mental health studies through digital platforms: Requirements and design of a prototype,” JMIR Res. Protoc., vol. 6, no. 6, 2017, doi: 10.2196/resprot.6919.
C. Silva, M. Saraee, and M. Saraee, “Predictive Modelling in Mental Health: A Data Science Approach,” in 2019 IEEE Conference on Sustainable Utilization and Development in Engineering and Technologies, CSUDET 2019, 2019. doi: 10.1109/CSUDET47057.2019.9214626.
A. Rawat and S. Gochhait, “Iot Enabled Mental Health Diagnostic System Leveraging Cognitive Behavioural Science,” in 2022 International Conference on Decision Aid Sciences and Applications, DASA 2022, 2022. doi: 10.1109/DASA54658.2022.9765032.
Y. Zhong et al., “Development of an Implantable Wireless and Batteryless Bladder Pressure Monitor System for Lower Urinary Tract Dysfunction,” IEEE J. Transl. Eng. Heal. Med., vol. 8, 2020, doi: 10.1109/JTEHM.2019.2943170.
U. K. Prodhan, M. Z. Rahman, I. Jahan, A. Abid, and M. Bellah, “Development of a portable telemedicine tool for remote diagnosis of telemedicine application,” in Proceeding - IEEE International Conference on Computing, Communication, and Automation, ICCCA 2017, 2017. doi: 10.1109/CCAA.2017.8229817.
B. Martínez-Pérez, I. De La Torre-Díez, and M. López-Coronado, “Mobile health applications for the most prevalent conditions by the World Health Organization: Review and analysis,” Journal of Medical Internet Research, vol. 15, no. 6. 2013. doi: 10.2196/jmir.2600.
S. M. R. Islam, D. Kwak, M. H. Kabir, M. Hossain, and K. S. Kwak, “The Internet of things for health care: A comprehensive survey,” IEEE Access, vol. 3, 2015, doi: 10.1109/ACCESS.2015.2437951.
A. Brogi et al., “Survey High-Performance Modelling and Simulation for Selected Results of the COST Action IC1406 cHiPSet,” Futur. Gener. Comput. Syst., vol. 29, no. 1, 2018.
F. Onorati et al., “Prospective Study of a Multimodal Convulsive Seizure Detection Wearable System on Pediatric and Adult Patients in the Epilepsy Monitoring Unit,” Front. Neurol., vol. 12, 2021, doi: 10.3389/fneur.2021.724904.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Marc Uwiragiye, Dr. Djuma Sumbiri
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
