# Skeleton Tracking Accuracy and Precision Evaluation of Kinect V1, Kinect V2, and the Azure Kinect
## 1 Introduction  
The Kinect Xbox 360 has been a revolution in affordable 3D sensing. Initially meant only for the gaming industry, it was soon to be used by scientists, robotics enthusiasts, and hobbyists all around the world. It was later followed by the release of another Kinect--Kinect for Windows. We will refer to the former as Kinect v1 and to the latter as Kinect v2. Both versions have been widely used by the research community for various scientific purposes, such as object detection and object recognition [1; 2; 3], mapping and SLAM [4; 5; 6], gesture recognition and human-machine interaction (HMI) [7; 8; 9], telepresence [10; 11], virtual reality, mixed reality, and medicine and rehabilitation [12; 13; 14; 15; 16]. However, both sensors are now discontinued and are no longer being officially distributed and sold. In 2019, Microsoft released the Azure Kinect, which is no longer meant for the gaming market in any way; it is promoted as a developer kit with advanced AI sensors for building computer vision and speech models. Numerous papers focusing on the skeleton tracking capabilities of Kinect v1 and Kinect v2 have been published [17; 18; 19; 20; 21; 22; 23]. New ideas making use of the new Azure Kinect have already been published. [PERSON] et al. evaluate the pose tracking performance of the Azure Kinect and Kinect v2 for gait analysis [24]. [PERSON] et al. developed a real-time hand gesture recognition for tabletop holographic display interaction using the Azure Kinect [25]. [PERSON] et al. developed a body-tracking-based low-cost solution for monitoring workers' hygiene best practices during pandemics using the Azure Kinect [26]. [PERSON] at al. proposed a robust extrinsic calibration of multiple RGB-D cameras with body tracking and feature matching [27]. Precise 3D body joint detection is crucial for gesture-based and human motion analysis applications. It is especially important in the human-robot interaction field, namely in vision based HRI. This is clearly demonstrated in the Laws of Linear HRI defined in [28]:  
1. Every pair of two joints of a human sensed by a robot form a line. 2. Every line defined by first law intersects with the robot's environment in one or two places. 3. Every intersection defined by the second law is a potential navigation target or a potential object of reference for the robot. For a thorough investigation of the new Azure Kinect, please see [29]. This paper focuses solely on the skeleton tracking capabilities of all Kinect versions. Nevertheless, we performed initial experiments to compare the depth-sensing precision and accuracy of the examined sensors. In the second experiment, we mounted a human-sized figurine to the end effector and placed it in the same positions as the test plate. Positions were located 400 mm from each other. In each position, we measured relative accuracy and precision (repeatability) of the detected figurine body joints. The main novelty of this paper lies in a skeleton tracking comparison of all three Kinect versions and in using a human-sized figurine precisely positioned by a robotic manipulator with focus on the new Azure Kinect. Evaluation of the new sensor in this regard is very useful for scientists and researchers developing designs and applications where joint tracking without wearable equipment is needed. Since human subjects cannot prevent slight motions while standing still, the plastic figurine is an essential tool to provide reliable precision and accuracy measurements. Skeleton tracking binaries provided by Microsoft are based on the work of [PERSON] et al. [30], shown in Figure 1.  
The paper is organized as follows. Firstly, we present specifications of all examined sensors. Then, we perform experiments to determine the general precision and accuracy of each sensor. Then, we present experiments where we mounted a plastic figurine on a robotic manipulator to determine the skeletal tracking (body joint detection) precision and accuracy of each sensor.