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Machine learning is used for the research and development of ITS services and the rider assistance for on-road motorcycle racing. Meanwhile, rider assistance systems for off-road motorcycles have yet to be developed, partly due to the complexity of the measurement conditions, as described in the previous paper. This research aims to create a reliable AI which is capable of classifying typical jump behaviors in off-road riding by machine learning to create a rider assistance system for off-road motorcycles. Motorcycle manufacturers and certain research institutes use motion sensors to collect data, but the data is obtained from a limited number of vehicles and riders. The creation of a rider assistance system requires a large amount of validation data. Furthermore, it is desirable to achieve the target with data that can be measured in mass-produced vehicles, which will make it possible to collect data even from general users.

Owing to the recent developments in sensors with reduced size and weight, it is now possible to install sensors on a body of a motorcycle to monitor its behavior during running. The analysis of maneuverability and stability has been performed based on the data resulted from measurements by these sensors. The tire forces and moments is an important measurement item in maneuverability and stability studies. However, the tire forces and moments is difficult to measure directly, therefore, it is a common practice to measure the force and the moment acting on the center of the wheel. The measuring device is called a wheel forces and moments sensor, and it is widely used for cars. The development of a wheel forces and moments sensor for motorcycles has difficulty particular to motorcycles. First, motorcycles run with their bodies largely banked, which restricts positioning the sensors.