Design and Construction of a Mobile-Based IoT System for Fall Risk Monitoring in Stroke Rehabilitation Patients
Keywords:
IoT, Fall Risk, Stroke Patients, Mobile Monitoring, ESP32
Abstract
Stroke rehabilitation patients have a high risk of falling, requiring continuous supervision to ensure safety and support the recovery process. This research aims to design and build a mobile-based Internet of Things (IoT) system to monitor patients' fall risk in real-time. The system utilizes a wearable device integrated with an ESP32-C3 microcontroller, an Inertial Measurement Unit (IMU) MPU-6050 sensor, and a BMP280 pressure sensor to acquire patient movement and altitude data. This data is transmitted via Bluetooth Low Energy (BLE) to a mobile application to be analyzed using a fall detection algorithm. The analysis results are displayed on the application interface as a risk status, activity graphs, and alert notifications if a potential fall is detected. The resulting design demonstrates that this system is effective in assisting caregivers to comprehensively monitor the patient's condition. Through accurate and responsive monitoring, this system is expected to enhance patient safety during rehabilitation and provide useful historical data for medical personnel.
References
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O’Brien, M. K., Shin, S. Y., Khazanchi, R., Fanton, M., Lieber, R. L., Ghaffari, R., Rogers, J. A., & Jayaraman, A. (2022). Wearable Sensors Improve Prediction of Post-Stroke Walking Function Following Inpatient Rehabilitation. IEEE Journal of Translational Engineering in Health and Medicine, 10, 1–11. https://doi.org/10.1109/JTEHM.2022.3208585
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Song, Z., Ou, J., Wu, S., Shu, L., Fu, Q., & Xu, X. (2025). Wearable fall risk assessment by discriminating recessive weak foot individual. Journal of NeuroEngineering and Rehabilitation, 22(1), 64. https://doi.org/10.1186/s12984-025-01599-8
Toledo, V. M. de, Colombo, C. da S., Pizetta, I. H. B., Carmo, L. P. T. do, & Guimarães, R. S. (2025). Wearable IoT Devices: Feasibility and Effectiveness in Implementing Local Decision-Making for Elderly Fall Detection. Anais Do XXI Simpósio Brasileiro de Sistemas de Informação (SBSI 2025), 389–398. https://doi.org/10.5753/sbsi.2025.246513
Wang, L., Wang, L., Wang, Z., Gao, F., Wu, J., & Tang, H. (2024). Clinical Effect Analysis of Wearable Sensor Technology-Based Gait Function Analysis in Post-Transcranial Magnetic Stimulation Stroke Patients. Sensors, 24(10), 3051. https://doi.org/10.3390/s24103051
Zhang, J., Li, Z., Liu, Y., Li, J., Qiu, H., Li, M., Hou, G., & Zhou, Z. (2024). An Effective Deep Learning Framework for Fall Detection: Model Development and Study Design. Journal of Medical Internet Research, 26, e56750. https://doi.org/10.2196/56750
Abdollahi, M., Rashedi, E., Kuber, P. M., Jahangiri, S., Kazempour, B., Dombovy, M., & Azadeh-Fard, N. (2024). Post-Stroke Functional Changes: In-Depth Analysis of Clinical Tests and Motor-Cognitive Dual-Tasking Using Wearable Sensors. Bioengineering, 11(4), 349. https://doi.org/10.3390/bioengineering11040349
Ahamed, B., Sellamuthu, S., Karri, P. N., Srinivas, I. V., Mohammed Zabeeulla, A. N., & Ashok Kumar, M. (2023). Design of an energy-efficient IOT device-assisted wearable sensor platform for healthcare data management. Measurement: Sensors, 30, 100928. https://doi.org/10.1016/j.measen.2023.100928
Baiense, J., Zdravevski, E., Coelho, P., Pires, I. M., & Velez, F. J. (2025). Driving Healthcare Monitoring with IoT and Wearable Devices: A Systematic Review. ACM Computing Surveys, 57(11), 1–38. https://doi.org/10.1145/3731595
Bonanno, M., Ielo, A., De Pasquale, P., Celesti, A., De Nunzio, A. M., Quartarone, A., & Calabrò, R. S. (2025). Use of Wearable Sensors to Assess Fall Risk in Neurological Disorders: Systematic Review. JMIR MHealth and UHealth, 13, e67265. https://doi.org/10.2196/67265
Choi, J. H., Choi, E. S., & Park, D. (2023). In-hospital fall prediction using machine learning algorithms and the Morse fall scale in patients with acute stroke: a nested case-control study. BMC Medical Informatics and Decision Making, 23(1), 246. https://doi.org/10.1186/s12911-023-02330-0
Darcy, B., Rashford, L., Huizenga, D., Reed, K. B., & Bamberg, S. J. M. (2025). Balance Improvement and Fall Risk Reduction in Stroke Survivors After Treatment With a Wearable Home-Use Gait Device: Single-Arm Longitudinal Study With 1-Year Follow-Up. JMIR Formative Research, 9, e67297–e67297. https://doi.org/10.2196/67297
Demers, M., Cain, A., Bishop, L., Gunby, T., Rowe, J. B., Zondervan, D. K., & Winstein, C. J. (2023). Understanding stroke survivors’ preferences regarding wearable sensor feedback on functional movement: a mixed-methods study. Journal of NeuroEngineering and Rehabilitation, 20(1), 146. https://doi.org/10.1186/s12984-023-01271-z
Fula, V., & Moreno, P. (2024). Wrist-Based Fall Detection: Towards Generalization across Datasets. Sensors, 24(5), 1679. https://doi.org/10.3390/s24051679
Lobo, P., Morais, P., Murray, P., & Vilaça, J. L. (2024). Trends and Innovations in Wearable Technology for Motor Rehabilitation, Prediction, and Monitoring: A Comprehensive Review. Sensors, 24(24), 7973. https://doi.org/10.3390/s24247973
Ma, L., Wang, H., & Li, J. (2022). A Wearable Fall Detection and Alert System for the Elderly Based on an Unsupervised Learning Algorithm. IEEE Sensors Journal, 22(5), 4512–4521. https://doi.org/10.1109/JSEN.2022.3145876
O’Brien, M. K., Shin, S. Y., Khazanchi, R., Fanton, M., Lieber, R. L., Ghaffari, R., Rogers, J. A., & Jayaraman, A. (2022). Wearable Sensors Improve Prediction of Post-Stroke Walking Function Following Inpatient Rehabilitation. IEEE Journal of Translational Engineering in Health and Medicine, 10, 1–11. https://doi.org/10.1109/JTEHM.2022.3208585
Rech, K., da Cunha, M. J., Salazar, A. P., Almeida, R. da R., Pedrini Schuch, C., & Balbinot, G. (2025). Enhancing safety monitoring in post-stroke rehabilitation through wearable technologies. Clinical Rehabilitation, 39(3), 388–398. https://doi.org/10.1177/02692155241309083
Singh, G. (2025). Wearable IoT (w-IoT) artificial intelligence (AI) solution for sustainable smart-healthcare. International Journal of Information Management Data Insights, 5(1), 100291. https://doi.org/10.1016/j.jjimei.2024.100291
Song, Z., Ou, J., Wu, S., Shu, L., Fu, Q., & Xu, X. (2025). Wearable fall risk assessment by discriminating recessive weak foot individual. Journal of NeuroEngineering and Rehabilitation, 22(1), 64. https://doi.org/10.1186/s12984-025-01599-8
Toledo, V. M. de, Colombo, C. da S., Pizetta, I. H. B., Carmo, L. P. T. do, & Guimarães, R. S. (2025). Wearable IoT Devices: Feasibility and Effectiveness in Implementing Local Decision-Making for Elderly Fall Detection. Anais Do XXI Simpósio Brasileiro de Sistemas de Informação (SBSI 2025), 389–398. https://doi.org/10.5753/sbsi.2025.246513
Wang, L., Wang, L., Wang, Z., Gao, F., Wu, J., & Tang, H. (2024). Clinical Effect Analysis of Wearable Sensor Technology-Based Gait Function Analysis in Post-Transcranial Magnetic Stimulation Stroke Patients. Sensors, 24(10), 3051. https://doi.org/10.3390/s24103051
Zhang, J., Li, Z., Liu, Y., Li, J., Qiu, H., Li, M., Hou, G., & Zhou, Z. (2024). An Effective Deep Learning Framework for Fall Detection: Model Development and Study Design. Journal of Medical Internet Research, 26, e56750. https://doi.org/10.2196/56750
Published
2025-12-03
How to Cite
Husna, A., Sandi, A., & Ashari, I. (2025, December 3). Design and Construction of a Mobile-Based IoT System for Fall Risk Monitoring in Stroke Rehabilitation Patients. Seminar Nasional Penelitian Dan Pengabdian Kepada Masyarakat 2025, 4(1), 156-164. https://doi.org/https://doi.org/10.35960/snppkm.v4i1.1408
Section
Penelitian
This work is licensed under a Creative Commons Attribution 4.0 International License.
