Running is a popular sport that offers health benefits but also poses a high risk of overuse injuries, often leading to a temporary or permanent cessation of running. These injuries are often caused by repetitive mechanical loading, emphasizing the need to quantify training loads to understand injury development. While such quantification is feasible in controlled settings like gait labs, capturing accurate data in natural outdoor conditions is challenging. This requires a practical sensor setup that is feasible for daily use and robust modelling approaches to relate sensor-derived data to mechanical load. We address these challenges by quantifying biomechanical loads during outdoor running using a minimal sensor setup. A key achievement is a model that uses inertial sensors to estimate vertical ground reaction forces (GRFs) used to estimate load. Moreover, we show that runners’ perceived exertion is often misaligned with sensor-derived load measurements, highlighting the importance of combining subjective and objective metrics for a holistic view of training loads. The discrepancy also underscores the need to quantify structure-specific loads, such as forces on the lower legs, to better understand their potential role in mechanical fatigue and injury risk. We present a neural network model to estimate 3D GRFs needed to quantify these structure-specific loads accurately. These results advance biomechanics with novel data methods for quantifying training loads in outdoor environments with a feasible setup for many runners, enabling large future studies to better understand injury mechanisms. The study highlights the difficulty of data analysis when moving from a controlled situation in the laboratory to a large study outside the laboratory.