## 1 Introduction  
Biomechanical investigations of running require accurate detection methods of foot strike (FS), i.e., the exact moment when the foot touches the ground. This is of great importance to determine the FS angle in continuous kinematic data stream signals of foot orientation in the sagittal plane. This was performed by [PERSON] et al. [1], [PERSON] and [PERSON] [2], and [PERSON] et al. [3] when investigating the FS angle under various conditions (footwear conditions or barefoot vs. shot) in the laboratory. Furthermore, the accuracy of electromyographical (EMG) investigations (EMG onset and EMG peak value) of lower limb muscles is based on an accurately determining FS. Relative to FS, [PERSON] et al. [4] found EMG peaks of 6.8 \(\pm\) 2.8 ms for rectus femoris, 11.1 \(\pm\) 5.3 ms for gastrocnemius, and 9.5 \(\pm\) 4.7 ms for tibialis anterior muscles when running at 4.2 m/s. The short latency between FS and EMG peak emphasizes the importance of accurate and precise FS detection. Moreover, exact FS detection enhances data accuracy and consequently minimizes defective variability in running parameters, e.g., stride length and stride frequency, which have been investigated by various authors [5; 6; 7; 8]. To detect FS, force plates are frequently used as the method of choice. However, force plates are often restricted to indoor use in specific gait laboratories and limited to a small number of consecutive gait cycles for each trial. In addition, force plate targeting of subjects results in slightly adjusting the natural running pattern in order to hit the force plate, which may distort accuracy of running data [9]. On the other hand, micro-electromechanical sensors (MEMS) can be an alternative for detecting the moment of FS when force plates are not available. These wearable motion sensors (e.g., gyroscopes and accelerometers) are easy to use, low-cost, and suitable for field measurements [10; 11; 12; 13]. Furthermore, these sensors offer the advantages of collecting multiple consecutive gait cycles and eliminate the issue of targeting. Numerous authors have already used MEMS to detect FS in a variety of conditions [8; 14; 15; 16; 17; 18; 19; 20]. Nevertheless, just one study was found which investigated the accuracy of MEMS for detecting FS during running by comparing those data to a synchronized force plate signal [21]. Those authors used a shank-mounted accelerometer, measuring accelerations along the longitudinal axis of the tibia in sixteen rearfoot runners (4.0 m/s). They found an average error of 1.68 ms (95% CI: \(-\)2.94 to 6.25 ms) and an absolute error of 5.46 ms (95% CI: 1.89 to 9.03 ms), respectively. Furthermore, [PERSON] et al. [17] used a gyroscope signal (sagittal plane) to determine FS during walking (0.83 to 1.7 m/s). The accuracy of FS was compared to footswitches (small force sensors--Tekscan A201), which were placed under the calcaneus (heel) and the hallux (big toe), respectively. Authors found an average difference of \(-\)2 ms (95% CI: \(-\)16 to 12 ms). Further studies compared stride and stance duration during running by using inertial sensors, however they did not mention the accuracy for determining FS [19; 22]. To the best of the authors' knowledge, there are currently no studies which address whether published FS detection methods are able to accurately detect the initial ground contact for forefoot strike (FFS) pattern. In contrast to rearfoot strike (RFS) pattern, the FFS pattern is characterized by altered kinematics of the lower limbs, resulting in initial ground contact with the ball of the foot [23]. This running style is becoming more popular among runners due to presumed protection against impact-related injuries [23; 24]. Consequently, this increases the necessity for more research on the accurate and precise detection of the moment when the foot touches the ground, related to, e.g., kinematics, muscle activity, and spatio-temporal parameters.