Handwriting speed is an important indicator of motor and cognitive difficulties. While various technologies, often combined with electronic tablets, have enabled the assessment of handwriting indicators, these systems rely on the relative position between the pen and tablet and do not directly capture hand movement, which limits their ability to accurately reflect the true handwriting speed. This work presents a real-world approach to estimating handwriting speed using inertial data and grip strength. We propose an algorithm that combines a mathematical model with 3D inertial time series and force data processing, incorporating gravity compensation in the acceleration time series, active writing period detection, and a drift correction approach. To evaluate our method, we performed a series of local tests using two-minute handwriting samples. Preliminary results demonstrate accurate speed estimation that effectively discriminates between writing and non-writing periods. These results highlight the potential of the proposed approach for natural and objective handwriting speed assessment.