## 4 Discussion
This was especially true in runners who touched the ground with the ball of the foot first, but then touched the ground with the heel during mid-stance phase. The second measured peak could influence the automatic detection of FS, leading to FS detection errors when the minimum acceleration before the false peak tibial acceleration was used. M3 was conducted according to [PERSON] et al. [15], by using the vertical accelerometer axis of a heel mounted inertial measurement unit. This method revealed a great accuracy and the lowest difference compared to the reference method for RFS and FFS when running in all footwear conditions. Due to the wobbling-free attachment to the heel cap of the shoe, impact accelerations can be measuredwithout delay. For the RFS pattern, no influence of the shoe hardness in the rearfoot area on accuracy of FS detection could be found. However, a trend was found for the FFS pattern: the footwear with the hardest midsole in the forefoot area (PU100) showed the highest accuracy between the FS detection method and reference method (\(-\)0.95 \(\pm\) 2.05 ms), whereas the footwear with the softest midsole in this area (PU1000) showed the greatest delay (\(-\)2.37 \(\pm\) 1.93 ms). It seems that the impact attenuation is related to midsole hardness and--therefore--influences the accuracy of this FS detection method slightly during forefoot running. In contrast to this, precision and repeatability were not influenced by forefoot hardness of the shoes for the FFS pattern. M4 was based on previously published findings by [PERSON] et al. [17]. In contrast to M1, M2, and M3, gyroscope data (measured angular velocity in the sagittal plane) were used instead of the accelerometer signal to detect FS. Our results revealed a negative detection error between \(-\)16.68 \(\pm\) 2.73 ms (PU500) and \(-\)20.59 \(\pm\) 3.28 ms (PU1000) for the RFS pattern and a positive detection error between 5.13 \(\pm\) 3.01 ms (PU100) and 8.43 \(\pm\) 4.28 ms (PU1000) for the FFS pattern. It shows that the accuracy of this method is distinctly higher for the FFS pattern than for the RFS pattern. However, accuracy of M4 was significantly lower than accuracy of M3 for all footwear conditions and both foot strike patterns. [PERSON] et al. [17] compared their FS detection algorithm to footswitches and found a distinctly lower difference on average of \(-\)2 ms (95% CI: \(-\)16 to 12 ms). They investigated walking in a range between 0.83 and 1.7 m/s. Due to the altered lower limb kinematics when changing locomotion speed from walking to running, the angular velocity was modified slightly and a phase shift was observed. It seems that the signal characteristics which were used to define FS (angular velocity reaching the minimum value for the first time in the gait cycle; Figure 1) changed considerably. Additional phase shift was found when comparing the RFS and FFS pattern, which led to the opposed differences to the reference method between both running styles. Furthermore, accuracy of M4 was influenced by the midsole hardness in the forefoot area, when running with the FFS pattern. As already found for M3, the footwear with the hardest midsole in the forefoot area (PU100) revealed the highest accuracy between the FS detection method and reference method (5.13 \(\pm\) 3.01 ms), and the footwear with the softest midsole (PU1000) showed the greatest delay (8.43 \(\pm\) 4.28 ms). Presumably, due to different midsole hardness, foot strike angle varied during forefoot running and therefore, angular velocity phases and magnitudes were also influenced. However, foot strike angle was not monitored and cannot be further investigated. However, it can be stated that besides acceleration signals measured at the heel cap, gyroscope signals were also influenced by footwear hardness FFS running patterns. Four published FS detection methods were tested to determine whether they can accurately detect FS for distinctive running styles and different footwear conditions when running at a self-selected velocity.