# Skeleton Tracking Accuracy and Precision Evaluation of Kinect V1, Kinect V2, and the Azure Kinect
## 5 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & & **Pos 1-2** & **Pos 2-3** & **Pos 3-4** & **Pos 4-5** & **Pos 5-6** & **Pos 7-8** & \\ \hline Azure NFOV & Avg dst & 405.8667 & 403.1197 & 399.4731 & 401.0482 & 401.9613 & 400.6904 & 402.6862 \\ \hline  & Min dst & 390.037 & 382.7666 & 389.2931 & 396.2346 & 390.295 & 391.9491 & 392.9188 \\ \hline  & Max dst & 416.2143 & 413.291 & 404.8484 & 404.1863 & 412.4341 & 408.7557 & 417.5213 \\ \hline Azure WFOV & Avg dst & 405.8589 & 403.735 & 418.8697 & 400.3382 & 400.39 & 393.7127 & 392.0062 \\ \hline  & Min dst & 391.5172 & 389.5792 & 344.0758 & 292.5254 & 278.7621 & 296.1326 & 303.7741 \\ \hline  & Max dst & 440.8246 & 409.8515 & 485.4536 & 516.0719 & 539.9829 & 517.9588 & 509.3196 \\ \hline Kinect 2 & Avg dst & 409.6489 & 404.8747 & 385.5388 & 401.9627 & 418.7103 & 393.5865 & 0 \\ \hline  & Min dst & 396.249 & 397.1817 & 312.6303 & 385.3205 & 399.8347 & 377.1883 & 0 \\ \hline  & Max dst & 465.2497 & 411.3906 & 395.7215 & 472.7374 & 428.8178 & 416.779 & 0 \\ \hline Kin 1 & Avg dst & 422.1129 & 416.271 & 419.1457 & 419.7023 & 430.8499 & 383.1812 & 0 \\ \hline  & Min dst & 395.228 & 336.4606 & 395.0333 & 407.2178 & 367.0855 & 253.103 & 0 \\ \hline  & Max dst & 489.8325 & 442.156 & 445.1825 & 432.0966 & 464.095 & 466.0918 & 0 \\ \hline Kin 1 no smooth & Avg dst & 419.3267 & 415.4121 & 417.8968 & 420.5548 & 426.2065 & 402.8094 & 0 \\ \hline  & Min dst & 396.5619 & 346.3239 & 393.2796 & 406.5002 & 417.6078 & 306.1316 & 0 \\ \hline  & Max dst & 480.6929 & 446.8681 & 435.2085 & 435.3634 & 440.0262 & 474.2191 & 0 \\ \hline \end{tabular}
\end{table} 
Table 3: Key data for all positions and sensors. angular resolution than Kinect v2. Therefore, since previous Kinect versions have been discontinued, the new sensor can certainly stand in their place. Furthermore, the Azure detects more body joints and works on the Linux platform (unlike previous versions), which is prevalent, especially in robotics. Its WFOV mode is imprecise, but its wide field of view could be used for simple people detection applications. It is a hindrance of this study that the skeleton detection binaries were distributed only with each specific Kinect version, but based on our observations, the versions for Kinect v1 and v2 behave similarly. Namely, one often must perform motion towards the sensor for her or his body joints to be reliably tracked. This caused some problems while we were performing experiments, as we were forced to move the figurine back and forth before stabilizing it in its current measuring position. The Azure Kinect detects the skeleton much more smoothly, and no movement towards the sensor is necessary. Other hindrances include the reflectivity of the figurine. We had it clothed, including socks, and painted with a reflective paint, but it is possible that palm and face detection were affected by this. We were unable to align the origins of the Kinect frames; therefore, we could only measure relative accuracy. Our future work will be focused on advancing the field of HRI (human-robot interaction). We are working on gesture-based interactive robotic systems and plan to develop applications that make use of the Laws of Linear HRI. Furthermore, we plan to compare the Azure skeleton tracking capabilities with other available systems, such as Intel Real Sense technology, OpenPose, DeepPose, and VNect. To conclude, the Azure Kinect is a promising small and versatile device with a wide range of uses, ranging from object recognition, object reconstruction, mobile robot mapping, navigation, obstacle avoidance, and SLAM to object tracking, people tracking and detection, HCI (human-computer interaction), HMI (human-machine interaction), HRI (human-robot interaction), gesture recognition, virtual reality, telepresence, medical examination, biometry and more.