Wearable sensors are important in today's athlete training ecosystems and also for the monitoring of therapeutic rehabilitation processes or even the diagnosis of diseases. In the future, wearables will be integrated directly into clothing and require dedicated, low-energy consuming algorithms that still maintain high accuracy. We developed a novel algorithm for the task of movement speed determination based on wearables that track only the acceleration of one foot. It consists of three algorithm blocks that perform step segmentation, step detection and speed estimation, all having linear computation complexity and able to work in real-time on state-of-the-art embedded microprocessors. Using a reference dataset collected from a motion capturing device for nine subjects and 795 steps in total, a parametric regression algorithm was trained and evaluated using a comprehensive leave-one-subject-out crossvalidation. It is able to estimate the movement speed with a mean relative error of 6.9 ± 5.5 %. Furthermore, we evaluated our approach on lightgate-based reference measurements using 12 subjects and different running movement styles. Here, our algorithm achieved a mean relative error of 16.5 ± 8.4 %. A final evaluation with realistic football-specific movements in a three-aside cage-based soccer game was done with a GPS-based reference measurement system, where the speed profile over a 30 minutes game of our method had a Pearson correlation of 0.85 to the GPS-based reference speed profile.