ENHANCED FALL AND TREMOR DETECTION IN HEALTHCARE USING DEEP LEARNING AND THRESHOLD BASED ANOMALY ALERTS

Abstract

Early detection of falls and abnormal tremors is critical in healthcare, particularly for elderly individuals and patients with neurological disorders such as Parkinson’s disease. This paper presents an enhanced system for real-time fall and tremor detection by integrating deep learning techniques with threshold-based anomaly alert mechanisms. The proposed framework utilizes wearable sensor data and/or video input to continuously monitor patient movements. A deep learning model—such as a Convolutional Neural Network (CNN) or Long Short-Term Memory (LSTM) network—is employed to accurately classify motion patterns and identify fall events and tremor activities. To improve reliability and reduce false positives, a threshold-based anomaly detection layer is incorporated, which evaluates parameters such as acceleration magnitude, frequency of motion, and posture changes. When these parameters exceed predefined thresholds, the system triggers immediate alerts to caregivers or healthcare providers. The hybrid approach ensures both high detection accuracy and real-time responsiveness