Fall Prediction in Elderly Through Vital Signs Monitoring A Fuzzy-Based Approach

Senior individuals are among the most frequent users of healthcare services, accounting for approximately 12% of the public sector, primary, and hospital care. Most elderly people who live alone and are not monitored are at risk of losing their lives because of a sudden fall caused by a slip, trip, or health issue and are not reported to an emergency department in time to receive immediate treatment. Thus, reliable and cost-effective e-health technologies are essential for solitary older adults. This research aims to predict potential falls in elderly individuals by detecting anomalies through continuous monitoring. The proposed prediction technique with the Fall Prediction Algorithm learns and performs tasks using Fuzzy rules. The acquired results are categorized based on the prediction risk levels. The proposed fall risk prediction model is evaluated using data collected from three different sources and the findings are compared to the Morse Falls Scale. According to the results obtained, the suggested prediction model has a total accuracy of 95.24%, sensitivity of 93.75%, and specificity of 100%. With these advancements in the proposed heterogeneous technology, elderly falls can be predicted earlier to save their lives.