Deep Belief Network-Based Activity of Daily Living Monitoring for Fall Risk Prediction in Elderly

Abstract

Despite advancements in healthcare and emerging technologies, falls among older adults remain a significant health issue. Recent research has increasingly focused on developing advanced monitoring methods, predicting, and preventing falls in this population. Achieving high performance in fall prediction requires a clear understanding of relevant features such as gait patterns, balance metrics, muscle strength, and environmental factors. Identifying these key indicators and incorporating data from wearable sensors, medical histories, and demographic information can significantly enhance predictive accuracy. This study proposes an intelligent fall prediction model that anticipates future falls in older adults by continuously monitoring their Activities of Daily Living (ADLs) and detecting abnormalities. The model uses a Deep Belief Network (DBN) that incorporates contrastive divergence for pre-training, backpropagation for fine-tuning, and the Adam Optimizer to minimize loss. Evaluation of the proposed model shows it achieved an accuracy of 91.67%, specificity of 100%, and sensitivity of 90.00% when compared to the Ground Truth (GT) and existing fall prediction approaches. These results suggest that advanced deep learning techniques can effectively assist in early fall risk prediction, potentially reducing the likelihood and severity of falls among older adults.