Tongyuhao Shen¹ and Tyler Boulom² , ¹Irvine Valley College, USA, ²Woodbury University, USA

Mountain biking carries significant injury risk, with up to 90% of cross-country riders sustaining at least one injury per season. A major cause is improper suspension setup, which is technically complex and difficult for everyday riders to optimize. This paper develops a cross-platform mobile application that combines a Raspberry Pi sensor unit with an AI advisor to deliver personalized suspension recommendations. The app, built in Flutter and Dart, streams live suspension data from the bike over Bluetooth Low Energy, sends rider profiles to OpenAI's GPT-4o-mini model via a REST API, and displays personalized trail recommendations using Google's Geocoding API. We tested the AI's accuracy by comparing 10 rider profiles against the official Fox 36 and RockShox setup charts, finding the AI produced recommendations within Fox's ±10 PSI tolerance on 70% of profiles. The app provides accessible, data-driven suspension tuning that helps riders set up their bikes safely and confidently.

Suspension, ArtificialIntelligence, Mountain Biking, Injury