# Skeleton Tracking Accuracy and Precision Evaluation of Kinect V1, Kinect V2, and the Azure Kinect
## 3 Experiments
### Sensor Accuracy  
In Figure 5, the distance variation between measured positions is shown. In an ideal case, the measured variation should be 400 mm between each position since the end effector of the robotic manipulator was programmed to move the test plate in such manner along the depth axis of each sensor. As can be seen in the figure, Kinect v1 had the worst performance, as in the case of precision. For both available resolutions, the difference between the measured and expected value between the first and last position is more than 130 mm. Hence, it is obvious that accuracy of Kinect v1 decreases with growing distance (this behavior is expected due to the projected pattern technology used). As shown in the figure, the position variation of Kinect v2 oscillates around 400 mm. The greatest error was 12 mm but shows little signs of cumulation. The difference between the real and measured position variation of the first and last position is only 10 mm. It can be assumed that it would possible to locate a more distant position where the sum of all errors would be 0 mm. The Azure Kinect performed even better; for the NFOV mode, the difference between the real and measured position variation of the first and last position is under 0.5 mm, and for the WFOV mode, it is 1.03 mm. In the WFOV mode, however, up to a particular distance, the Azure Kinect measured higher values, and after that, lower values. Therefore, with growing distance, the accuracy is likely to worsen. The previous experiment serves as an introduction to the core experiment of this paper; its purpose is to evaluate and compare the skeleton tracking and joint detection of all Kinect versions. In this experiment, the alignment of each sensor was the same as in the former one; this time a fixed human-sized figurine was moved along the depth axis of each sensor (Figure 6). This way, every detected body joint moved 400 mm in between measured positions. As before, we measured relative accuracy, precision, and skeleton detection quality. In Figure 6C, there is an example of the detected body joints of a person. It is clear that the feet are undetected because the sensor is unable to see them. Each SDK provides the parameters of reliability for all joints, termed the confidence level. We considered a joint to be undetected when the confidence level was lower than the highest level possible. Therefore, even though the figurine could be seen, clothes sometimes caused some joints' confidence level to be lower than the highest level possible, and we considered this case to be an undetected joint. Figure 4: General precision measurements of all three Kinect versions. Figure 5: General accuracy measurements of all three Kinect versions. Figure 6: Cont. We evaluate the skeleton quality as the percentage of undetected joints for a particular sensor and scanning position. The numbers in brackets represent values for joints common for all sensors, as the Azure Kinect gives 32 distinct joints (12 more than Kinect 1). Furthermore, it must be noted that each sensor probably has slightly different metrics for evaluating the reliability of a detected joint (different binary libraries and SDKs); therefore, the presented comparison is only approximate. The result is presented in Table 2.  
Figure 6: (**A**) Plastic figurine moved along the Z axis of the examined sensor. (**B**) Picture of the actual laboratory experiment. (**C**) Example of detected and undetected body joints. Concerning outage character, most of the time data loss occurred in the form of missing a particular joint. Rarely did a joint drop out back and forth in the same measurement set. Kinect v2 had the least joint outages throughout the tested range; however, it must be noted that its algorithm detects 7 joints less than the Azure Kinect. Most failures happened in the WFOV mode of the Azure Kinect. Next, we evaluate the precision (repeatability) and accuracy of the skeleton tracking.