Enhancing Heart Rate Detection Accuracy Through the Kalman Filter Algorithm

The imperative need for precise heart rate detection, particularly in the early diagnosis of heart conditions, amidst various risk factors like unhealthy lifestyle choices, stress, air pollution, and nutritional imbalances. It advocates for the utilization of the Kalman Filter Algorithm to refine the accuracy of heart rate monitoring systems. By bolstering the stability and precision of these systems, the algorithm aims to surmount challenges inherent in current methods, which often grapple with instability and inaccuracies. Through meticulous comparative analysis, the study showcases a notable reduction in error rates. In heart rate detection through the heart rate detection system, the MAPE percentage of errors found was smaller when using the Kalman Filter during heavy activity, which was 3.51%, compared to 6% without the Kalman Filter. Similarly, in experiments without heavy activity, the error rate was 2.69% with the Kalman Filter, compared to 8.95% without it. This represents an error reduction of approximately 4.37%. These findings underscore the potential of the proposed methodology to significantly propel health monitoring technologies forward, thereby facilitating more dependable and efficient management of heart diseases. Consequently, this research makes substantial strides towards enhancing overall health and elevating the quality of life for individuals at risk of heart disorders, ultimately fostering a healthier society.