Accuracy of KinectOne to quantify kinematics of the upper body
Introduction
The quantitative description of human motion finds application in research and in clinical settings. A common approach is with marker-based systems (MBS), where markers are placed on the skin. Such systems are used widely in research laboratories and are highly accurate [1]. However, their use has disadvantages: data collection and processing are time-consuming, require highly trained personnel, and are restricted to the laboratory setting. Markerless systems have evolved alongside the technical advancement of cameras and sensors. The Kinect™ from Microsoft, which was developed to control video games through body movements, has become of interest to the research community. The Kinect™ is able to track three-dimensional motion by combining information from a color camera and a depth-sensing infrared camera. It is of particular interest for clinical settings, since it is relatively low-cost, does not require time-consuming setup, can be used in various spaces and is easy to use.
In order for the Kinect™ to be used in clinical settings from a biomechanical perspective, the system needs to have sufficient validity to measure kinematic changes. This would allow, for example, determining the reduced shoulder range of motion (ROM) of a frozen shoulder patient and the monitoring of their progress during physiotherapy on a monthly basis. To achieve this, the system needs a measurement error of ROM of less than 7.7° (flexion), 6° (abduction) and 3.7° (rotation) [2].
Different studies have examined the accuracy of the Kinect for tracking the human body. For shoulder abduction in the frontal plane, a good correlation of ROM between the Kinect and a MBS was found; while for elbow flexion in the sagittal plane, a decreased correlation was obtained [3]. Accordingly, a larger bias for shoulder flexion than abduction was reported [4]. This indicates a dependability of the validity of the Kinect on the plane of motion. Generally, larger differences in kinematic measures were found for lower extremities compared to upper extremities [3], [5], [6], [7]. Clark et al. found a bias proportional to the measured value of Kinect compared to a MBS for the pelvis and sternum, but not for the hand [8], while others noticed a poorer correlation for the trunk than the shoulder angle for the Kinect compared to a MBS [4]. This shows a difference in validity between the core of the body and the extremity for the Kinect. Most studies examined accuracy in the standing position [3], [4], [8]
Most previous studies were executed with the first generation Kinect (KinectV1) [3], [4], [5], [6], [7], [8]. In 2014, the new Kinect™ for Xbox one (KinectOne) was released by Microsoft. This system is based on higher quality sensor technology (1920 × 1080 instead of 640 × 480 resolution for the color and 512 × 424 instead of 320 × 240 resolution for the depth-sensing camera), as well as an enlarged field of view compared to KinectV1. Additionally, according to the manufacturer's specification, the algorithm for motion detection has been improved.
It can be speculated that the technological improvements result in higher accuracy in body tracking and, consequently, a higher validity of KinectOne to track movements. A study has found that, generally, KinectOne has excellent concurrent validity for spatiotemporal measurements and anterior–posterior measures during dynamic and static balance tests, but consistently poor to modest validity for kinematic parameters of the lower body and medial-lateral measures during balance tests [9], [10]. Therefore, the aim of this study was to determine the concurrent validity and intra-session reliability of the KinectOne compared to a MBS for measuring segment angles of the trunk and upper extremities during functional movements.
Section snippets
Methods
Twenty subjects participated (age: mean ± SD: 33 ± 9 years; height: 173.7 ± 8.4 cm; weight 65.9 ± 10.6 kg; 10 female) and signed informed written consent. The study was approved by the local ethics committee. The subjects wore tight-fitting shorts (women with bra). Before data collection, each subject was equipped with 39 reflective markers, according to the plug-in-gait full body model [11]. Data were simultaneously collected using a 6-camera Vicon System (200 Hz, VICON, UK) and the KinectOne (30 Hz,
Results
On average, between-system differences of 3.9 ± 4.0° and 0.1 ± 3.8° were found for arm and trunk motion, respectively. For inclination and rotation exercises the direction of bias for the arm was positive (overestimation). Contrary, KinectOne overestimated inclination of the trunk (2.4 ± 2.8°) but underestimated rotation (−3.3 ± 2.0°). RC was found to be smaller than the range from lower to upper LoA for both segments in all exercises (Table 1). Results of system comparisons are shown in Table 1
Discussion
To determine the accuracy of KinectOne in tracking human motion we analyzed concurrent validity and intra-session reliability of KinectOne and a MBS. The motion of the arm and the trunk were recorded in different planes of motion while sitting and standing using both systems simultaneously.
Literature reports lower accuracy of KinectV1 in tracking trunk motion compared to motion of the upper extremities [4], [8]. Our data showed the opposite result for the absolute bias. However, we have to
Conclusion
The results of this study revealed that the accuracy of KinectOne in tracking arm motion is sufficient for clinical settings, with the exception of standing shoulder flexion. The recommendation is that the movements be performed seated. Although absolute bias of trunk motion was generally smaller, KinectOne is not able to track small changes in trunk motion due to the high RC/SRD and low CMC. Future research is needed to improve tracking of the trunk, and to establish whether a different
Conflict of interest statement
None of the authors have any financial or personal relationship with other people or organizations that could inappropriately influence their work.
Acknowledgments
The authors would like to acknowledge the assistance of Mariella Oswald for data collection, Andre Meichtry for statistical consultation, and Christian Schärli for providing software.
There was no study sponsor with influence on this study.
References (18)
The measurement of human motion: a comparison of commercially available systems
Hum. Mov. Sci.
(1999)- et al.
Shoulder kinematic features using arm elevation and rotation tests for classifying patients with frozen shoulder syndrome who respond to physical therapy
Manual Ther.
(2008) - et al.
Validity and reliability of the Kinect within functional assessment activities: comparison with standard stereophotogrammetry
Gait Posture
(2014) - et al.
Accuracy of the Microsoft Kinect sensor for measuring movement in people with Parkinson's disease
Gait Posture
(2014) - et al.
Validity of the Microsoft Kinect for providing lateral trunk lean feedback during gait retraining
Gait Posture
(2013) - et al.
Accuracy of the Microsoft Kinect™ for measuring gait parameters during treadmill walking
Gait Posture
(2015) - et al.
Gait assessment using the Microsoft Xbox One Kinect: concurrent validity and inter-day reliability of spatiotemporal and kinematic variables
J. Biomech.
(2015) - et al.
Reliability and concurrent validity of the Microsoft Xbox One Kinect for assessment of standing balance and postural control
Gait Posture
(2015) - et al.
A gait analysis data collection and reduction technique
Hum. Mov. Sci.
(1991)