Authors : M G Srinivasa; Chandana N S; Kaviya S; Mohith M; Manoj Kumar B S

Volume/Issue : Volume 10 - 2025, Issue 5 - May

Google Scholar : https://tinyurl.com/44urjhf5

DOI : https://doi.org/10.38124/ijisrt/25may1826

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : In our project, we seek a smart and reliable Alzheimer Patient Monitoring System on the basis of IoT-based technology. The system can monitor the health and safety of Alzheimer's patients in real-time and alert caregivers in case of an emergency. We are using a vibration sensor to detect sudden falls or movements, which are common and dangerous for such patients are a safety. A moisture sensor has been added to check for hygiene-related issues such as incontinence, which will help in ensuring cleanliness and the comfort. The humidity sensor confirms the environmental conditions around the patient to maintain the patient in a healthy and safe environment. To track the movement of the patient and prevent them from being lost, we have included a GPS module that constantly monitors their location. All the data collected by the sensors is displayed on an LCD screen for local observ ation. The entire system is also connected to Adafruit IO, an internet-based IoT platform for remote real-time observation. Adafruit IO Caregivers receive real-time alerts and notifications on their device, enabling them to respond instantly in the event of any form of emergency. The project has been planned to improve patient care, improve independence, and reduce the workload on caregivers by making optimal use of smart technology

Keywords : Alzheimer's Monitoring, IoT, Vibration Sensor, Moisture Sensor, GPS Tracking, Humidity Sensor, Adafruit IO, Patient Safety, Remote Health Monitoring.

References :

1. R. P. Jadhav, M. D. Kengar, O. V. Narule, V. W. Koli, and S. B. Kumbhar, “A review on alzheimer’s disease (ad) and its herbal treatment of alzheimer’s disease,” Asian Journal of Research in Pharmaceutical Science, vol. 9, no. 2, pp. 112–122, 2019.
2. P. Maresova, S. Tomsone, P. Lameski, J. Madureira, A. Mendes, E. Zdravevski, I. Chorbev, V. Trajkovik, M. Ellen, and K. Rodile, “Tech nological solutions for older people with alzheimer’s disease,” Current Alzheimer Research, vol. 15, no. 10, pp. 975–983, 2018.
3. Pratiarso, A., Imanuddin, A. I., Yuliana, M., et al. (2017). Implementation of Kalman Filter method for tracking position of Alzheimer’s patients. In International conference on smart cities, automation & intelligent computing systems (ICON-SONICS) (pp. 135–140). IEEE. https://doi.org/10.1109/icon sonics.2017.8267836.
4. Wojtusiak, J., & Mogharab, N. R. (2019). Location prediction using GPS trackers: Can machine learning help locate the missing people with dementia? Internet of Things.https://doi.org/10.1016/j.iot.2019.01.002
5. Akkaş, M. A., Sokullu, R., & Çetin, H. E. (2020). Healthcare and patient monitoring using IoT. Internet of Things,11,100173. https://doi.org/10.1016/j.iot.2020.100173.https://doi.
6. H.E. Adardour et al.,"Outdoor Alzheimer’s Patients Tracking Using an IoT System and a Kalman Filter Estimator" Wireless Personal Communications https://doi.org/10.1007/s11277-020-07713-4.
7. Manu M J et al., "A Smart Wearable Device for Tracking and Fall Detection for Geriatric People, Alzheimer Patients and Children with Autism",2021 5th International Conference on Electrical, Electronics, Communication, Computer Technologies and Optimization Techniques (ICEECCOT) 10-11,December 2021.