Intelligent Healthcare Navigation: Personalized Path Planning with Patient Condition and Environmental Awareness

Pedestrian navigation services typically optimize travel time or distance, overlooking environmental burdens that can aggravate chronic health conditions. We present a Smart Path Planner that personalizes walking routes by embedding live air quality, noise, slope, and microclimate data directly into the cost function of an A∗search algorithm. The system assigns condition-specific weights, such as increased penalties for poor air quality in respiratory profiles or for steep gradients in mobility condition profiles, so that each edge in the street graph reflects both physical length and health impact. Route generation is followed by an explainable scoring phase that exposes the environmental samples underlying every decision. Experiments on six health condition profiles show environmental score improvements of 10-25% while path length rises by only 4-8%, with points of interest coverage essentially unchanged. These results demonstrate that clinically significant relief can be achieved at negligible distance cost, positioning the proposed planner as a practical, data-driven advance toward personalized, health-optimized pedestrian navigation.