Advanced Geriatric Rehabilitation Monitoring with IoT and Logistic Regression Approach

Résumé fourni par la source

Improvements in geriatric rehabilitation monitoring are required due to the increasing number of people aged 65 and above worldwide. It provides a novel approach to rehabilitation for the elderly using Logistic Regression (LR) and the Internet of Things (IoT). Rehabilitation Measures Database (RMD) continually gathers patient data which includes information from a variety of IoT-enabled sensors. These sensors include motion detectors, heart rate monitors, and wearable devices. LR analyzes this data to forecast patient outcomes and determine critical variables impacting their recovery. This technique is designed to help healthcare practitioners quickly customize responses to individual requirements by providing personalized, real-time information. Using LR, the system can estimate the likelihood of positive rehabilitation outcomes from various inputs, including activity level, heart rate variability, and environmental variables. Timely and effective medical interventions are made possible by the study's demonstration of substantial increases in patient monitoring accuracy and the early identification of potential health concerns. The analysis and collection of data are ongoing, which allows for monitoring rehabilitation progress and adapting treatment plans as needed. The proposed system achieved 92.3% accuracy in predicting rehabilitation outcomes, decreased fall risk by 28%, and enhanced treatment adherence by 35%, illustrating the efficacy of IoT and LR in geriatric care.

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