## 4 Discussion  
The aim of the present study was to examine whether four published foot strike detection methods--using wearable sensors--can accurately define the time when the foot touches the ground initially for distinctive running styles and different footwear conditions. A synchronized force plate was used as the reference. To determine FS as accurately as possible, a fast sampling rate of 3750 Hz was used for all methods. Inaccurate determination of FS can produce subsequent errors in investigated running and gait parameters (e.g., kinematics, muscle activity, and spatio-temporal parameters). We found that the analyzed FS detection methods showed large differences in accuracy and precision, which depends on running style and footwear condition. Test method M1 was conducted according to previously published procedures by using an accelerometer mounted to the tibia [21]. Authors also compared the accuracy of their FS detection algorithm with the vertical component of the ground reaction forces. They found differences in mean of 1.68 ms (95% CI: \(-\)2.94 to 6.25 ms) when investigating rearfoot runners. This difference is distinctly lower than what we found for the RFS pattern. The threshold they used to detect FS with the force plate was 20 N, whereby in the present study the threshold was set to 10 N. This threshold and the high sampling rate in our procedure could potentially explain the greater differences we found between M1 and the force plate in contrast to the results of [PERSON] et al. [21]. However, the different force plate thresholds do not explain all differences, due to the high force rising rates at the beginning of ground contact. It seems that there is a continuous offset in the RFS pattern, which is slightly higher in the FFS pattern (Tables 2-4). As a result of various influencing factors (sensor wobbling, shoe midsole deformation, heel fat pad cushioning, and foot pronation), accelerations were attenuated and captured with a delay. Interestingly, we found that different footwear hardness does not seem to influence the accuracy of FS detection (range between shoes--RFS: 1.8 ms; FFS: 0.4 ms), whereas the running style influences accuracy of FS detection slightly (within differences between RFS and FFS--PU100: 2.0 ms; PU500: 3.4 ms; PU1000: 2.4 ms). Due to the altered kinematics of the lower limbs for the FFS pattern, accelerations (crossing a threshold of zero \(g\) before peak tibial acceleration occurred) were measured with a greater delay, which also increased the difference to the reference method. Finally, it can be stated for M1 that when only one sensor (placed at the tibia) is available to detect FS, it can be detected with a low error of \(\pm\)4 ms under various conditions when subtracting 14 ms (mean of our found differences) from the detected FS. For M2, tibial acceleration along the longitudinal axis of the tibia was also used to detect FS [14]. Thereby, minimum acceleration before the peak tibial acceleration defined FS. We found that FS detection with this method is characterized by a low precision, shown by the great SD and low repeatability (RC). Despite this, great accuracy of mean \(<\)\(-\)1.42 ms was found for RFS when wearing the harder shoes PU100 and PU500. However, for PU1000 (RFS) and all footwear conditions when running with the FFS pattern, substantial differences up to \(-\)15.84 ms were observed between M2 and the reference method. As discussed for M1, accelerations measured at the tibia revealed delayed acceleration patterns, which resulted in the negative values for the differences between M2 and the reference method. Interestingly, the shoe with the lowest stiffness in the rearfoot area (PU1000) showed the lowest accuracy (\(-\)10.49 \(\pm\) 10.09 ms) for the RFS pattern. For the FFS pattern, the delay due to altered lower limb kinematics was more prominent (\(>\)14.65 ms) than the influence of the footwear conditions (range between shoes: 1.2 ms). Additionally, in a few trials, the tibial acceleration data of forefoot runners showed a double peak.