Many cyclists use online-maps for planning their routes, however, only little information is known about the road surface of different cycling paths, farm or public roads. Cyclists prefer road surfaces fitting the type of bike they are using for a specific ride (e.g., time trial, road, MTB, cyclocross, gravel bike). Often riders upload their ride data including GPS, heart rate (HR) or power (P) on platforms like Strava or Garmin Connect. In this research we tried to evaluate whether it is possible to (1) evaluate the road surface quality using a 3D accelerometer mounted on the bicycle’s fork (f = 500 Hz) and whether (2) results of similar quality can be achieved using the accelerometer of a smartphone (f = 100 Hz) placed in the cyclist’s pocket. For data acquisition a cyclist rode on a cyclocross bicycle on three different road surfaces (cobblestones, gravel and tarmac) with three different speeds (10, 20 and 30 km/h) and three different tire pressures (3, 4 and 5 bar). Data of both measuring systems were analyzed using machine learning algorithms. Results showed that road surfaces could be predicted with more than 99% accuracy with the accelerometer and with more than 97% with the smartphone-data.
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