Falls are a significant threat to the geriatric population, and prompt intervention is crucial to ensure their safety. Existing fall detection methods often lack accuracy, especially in real-world settings with variable human motion and environmental factors. This paper proposes a novel approach to improve real-time fall detection for geriatric risk assessment. Our system utilizes an ADXL345 accelerometer to capture tri-axial acceleration data