An Analysis of the Walking Environmental Factors Affecting the Stress of Pedestrians for Route Recommendation

Abstract

Walking is an easy exercise for maintaining health, although there are several environmental factors, such as road congestion or slopes, which are stressful for pedestrians, especially elders. Therefore, a pedestrian navigation system is required to recommend a less stressful route for a user by estimating such environmental factors. Towards realizing such a system, we firstly collect vital sign, spatiotemporal and environmental data from pedestrians, and secondly analyze the relation between them, and then propose models for estimating the personal stress of a pedestrian. As our vital sign data, we calculate the stress of each pedestrian from the R-R interval data obtained by a heart rate sensor. Our spatiotemporal data includes the walking speed of pedestrians and road grade calculated from GPS sensor data. We measure the degree of road congestion, as our environmental data, using a laser range finder. Our analysis reveals that the road congestion is an important factor affecting stress in walking. The proposed models based on neural networks employ the road congestion in addition to the vital sign and spatiotemporal data. Experimental results indicate that the road congestion degree is effective and the stress of pedestrians estimated by our models is similar to the real stress.