## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & \multicolumn{4}{c}{**Day 1**} & \multicolumn{4}{c}{**Day 2**} & \multirow{4}{*}{**MDC**} \\ \cline{2-2} \cline{5-7}  & \begin{tabular}{c} **ICC\({}_{3,1}\)** \\ **(95\% CI)** \\ \end{tabular} & **CV\%** & **SEM** & \
\begin{tabular}{c} **MDC** \\ **(cm)** \\ \end{tabular} & **ICC\({}_{3,1}\)** & **CV\%** & **SEM** &
\begin{tabular}{c} **MDC** \\ **(cm)** \\ \end{tabular} \\ \hline My Jump 2 & 0.86 (0.72–0.93) & 6.45 & 3.07 & 8.51 & 0.88 (0.79–0.94) & 5.92 & 2.72 & 7.55 \\ HomeCourt & 0.83 (0.71–0.91) & 6.14 & 3.34 & 9.26 & 0.82 (0.68–0.90) & 5.52 & 3.14 & 8.71 \\ TVJM & 0.92 (0.85–0.96) & 4.70 & 2.32 & 6.43 & 0.95 (0.91–0.97) & 3.82 & 1.82 & 5.05 \\ \hline \end{tabular}
\end{table} 
Table 3: Within-day test–retest reliability of CMJ measurement devices. athlete's CoM away from the ground vertically, i.e., measuring how high an athlete reaches directly. AI motion tracking, a novel test technique, is adopted in the HomeCourt mobile application. It videotaped the jump and then analyzed the vertical displacement of the initial standing height and the peak jump height while no reflective marker was needed. The test requests participants to jump facing the camera in order to capture the jump in the frontal plane view. It is different from the other markerless motion capture with deep learning approaches, which quantifies the sagittal plane kinematics [31]. The developer has claimed that when used properly and under good lighting conditions, the error rate is under 5% [32]. CMJ can be assessed by numerous methods which are grounded in various techniques and technologies. Of the three measurements, the jump heights registered were varied. My Jump 2 was identified as the reference standard for the criterion-related validity comparison [16]. Previous studies found an almost perfect agreement (\(r>0.99\)) between the outputs of the jump height calculated using My Jump 2 and that of the force platform [11; 12]. Although they have different sampling rates for capturing the take-off and landing frames, they adopt similar jump height calculations by measuring the flight time [11; 17]. HomeCourt and the TVJM tended to have a higher mean jump height for CMJ (difference: 1.17-5.25 cm) compared with My Jump 2. This was probably due to the different technologies adopted for measurements. Particularly, the AI of HomeCourt used an object recognition technique while the TVJM measured the change of CoM directly. Noteworthy, the latter system may overestimate the jump height if the participant did not exactly land on the spot where s/he jumped. Furthermore, larger CVs in the two mobile applications were observed. The higher level of dispersion around the mean may be due to the blur of video motion caused by the current frame rate of the iPad camera (i.e., the sampling rate of the motion tracking) and the speed of the participant's motion. Due to the diverse limitations of the technologies, it is not recommended to compare the values among devices. Although the motion-tracking technology for assessing jump height in HomeCourt has been established for years, this study was the first to evaluate its validity and reliability. HomeCourt is simple to use and reliable (good between-day reliability) for measuring jump height. However, due to its moderate within-day ICC\({}_{3,1}\) (0.82-0.83), the average jump height, instead of the best value, was suggested for analysis [2]. Noteworthy, the outputs need to be interpreted with caution; specifically, only values over MDC should be viewed as real changes (e.g., changes over \(\pm\)6.71 cm in HomeCourt). The markerless motion-capturing approach of HomeCourt offers the potential to revolutionize scientific studies by allowing researchers and practitioners to collect data remotely outside of the laboratory. Furthermore, users of HomeCourt do not require any scientific knowledge or skills, e.g., the measurement of leg length, for CMJ assessment.