## 4 Findings
### RQ 1: How Accurate Is ARKit's Human Motion Capture Compared to the Vicon System?  
To answer RQ 1, we investigated both the wMAE and the SRCC of the experiment data. A wMAE of 0\({}^{\circ}\) and an SRCC of 1.0 would represent a perfect accuracy of ARKit's human motion capture. The ARKit data showed a MAE of 18.80\({}^{\circ}\) and an average SRCC of 0.76 for the whole data set, with variations when examining different joints and exercises. Based on the results of the ANOVA analysis, the accuracy mainly depends on the observed angle and exercise. However, the accuracy could be influenced by other additional factors which were not specifically targeted by the performed experiment. Remarkably, ARKit was able to achieve an almost perfect correlation and accuracy for some exercise executions in specific angles (Figure 6). In many cases, the movement pattern is recognizable in the ARKit data. Still, the amplitude is reduced, or a baseline drift on the \(y\)-axis is observable (Figure 7, which explains the good correlation but relatively high wMAE values. In some cases, the ARKit data exhibits high wMAE values and no or even a negative SRCC. These effects often occurred in the elbow joints, especially when the lower body joints moved and the upper body joints were held straight, such as in the Squad or Side Squat exercises. In this situation, ARKit often failed at detecting the movement correctly (Figure 8), which is visible both in the high wMAE and the low to negative correlation values for the elbow angles. In general, the accuracy was lower in those exercises where the root position did not remain stable, including the Front Lunge, Side Squat, and Squat exercises. The results of the factor analysis further confirmed these results.  
To investigate whether a systematic baseline drift can be observed in the ARKit data, we aligned the ARKit and Vicon data via cross-correlation. We measured the \(y\)-axis offset (Figure 9). As the offset was normally distributed around 0, no systematic baseline drift was present in the recorded data set, indicating that other factors cause shifts.  
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline
**T** & **dof** & **Alternative** & \(p\)**-Value** & **CI95\%** & **Cohen-d** & **BF10** & **Power** & **Response** & **Categorical** \\ \hline \(-0.22\) & 966.81 & two-sided & 0.82 & \([-0.09,0.07]\) & 0.01 & 0.073 & 0.06 & LogMAE & View \\ \(-0.15\) & 725.74 & two-sided & 0.88 & \([-0.1,0.08]\) & 0.01 & 0.072 & 0.05 & LogMAE & LowerBody \\ \(-13.20\) & 1045.97 & two-sided & 0.00 & \([-0.59,\,-0.44]\) & 0.82 & \(3.266\times 10^{33}\) & 1.00 & LogMAE & CenterMoved \\ \hline \hline \end{tabular}
\end{table}
Table 6: The results of the Welch \(t\)-test Analysis.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Variable** & \(\beta\)**-\(coef\)** & **std** & **z** & **P \(>\) |z|** & **[0.025** & **0.975]** & _Pseudo-R\({}^{2}\)** \\ \hline View & 0.0141 & 0.003 & 4.329 & 0.000 & 0.008 & 0.020 & \(-0.019\) \\ Lower Body & 0.0684 & 0.005 & 13.374 & 0.000 & 0.058 & 0.078 & 0.165 \\ Center Moved & 0.0018 & 0.003 & 0.561 & 0.575 & \(-0.004\) & 0.008 & 0.000 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The results of the logistic regression.  
**Finding 1:** ARKit is able to track the general progression of a movement with good accuracy but with significant deviations from the actual values measured by the Vicon system. The performance is influenced by external factors such as the performed motion.  
Figure 8: Right elbow angle of one of the subjects in the Squat exercise in degrees, which shows bad tracking quality with a lot of noise compared to the Vicon data.  
Figure 6: Left hip angle of one of the subjects in the Single Leg Deadlift exercise in degrees, which shows a nearly perfectly overlapping curves of the ARKit and Vicon data.  
Figure 7: Left hip angle of one of the subjects in the Side Squat exercise in degrees. The plot shows that while the motion pattern is visible in both recordings, ARKit exposes a reduced amplitude and a shift on the \(y\)-axis.