Vision-Based Fall Detection System: a Machine Learning Approach for Real-Time Alert and Intervention

Falls are a significant health concern, particularly in older adults and individuals with limited mobility. Early detection is crucial to prevent severe injuries by facilitating prompt medical intervention. This paper proposes a vision-based fall detection and alert system that employs machine learning to identify falls in real time. A camera continuously monitors movements, and computer vision techniques are utilized to extract key motion features. Subsequently, the system employs a trained machine learning model to classify the detected actions as either falls or regular activities. To enhance response time, the system incorporates an automated alert mechanism that immediately notifies caregivers or emergency contacts. In instances where the recipient’s phone is in silent mode, the system initiates an emergency call accompanied by a loud siren to ensure prompt attention. Unlike wearable-sensor-based methods, this approach is nonintrusive and cost-effective. The experimental results indicate that the system accurately distinguishes falls from normal activities, rendering it a promising solution for healthcare and assisted living applications.