Skeleton Tracking Accuracy and Precision Evaluation of Kinect V1, Kinect V2, and the Azure Kinect

Tölgyessy, Michal; Dekan, Martin; Chovanec, Ľuboš

doi:10.3390/app11125756

Cited by 63 publications

(39 citation statements)

References 31 publications

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“…The technical validation of the Azure Kinect sensor has been reported in previous studies 28 – 30 , which indicates that the officially stated values are accurate, namely s.d. ≤17 mm, and distance error <11 mm.…”

Section: Technical Validationmentioning

confidence: 53%

Functional movement screen dataset collected with two Azure Kinect depth sensors

et al. 2022

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This paper presents a dataset for vision-based autonomous Functional Movement Screen (FMS) collected from 45 human subjects of different ages (18–59 years old) executing the following movements: deep squat, hurdle step, in-line lunge, shoulder mobility, active straight raise, trunk stability push-up and rotary stability. Specifically, shoulder mobility was performed only once by different subjects, while the other movements were repeated for three episodes each. Each episode was saved as one record and was annotated from 0 to 3 by three FMS experts. The main strength of our database is twofold. One is the multimodal data provided, including color images, depth images, quaternions, 3D human skeleton joints and 2D pixel trajectories of 32 joints. The other is the multiview data collected from the two synchronized Azure Kinect sensors in front of and on the side of the subjects. Finally, our dataset contains a total of 1812 recordings, with 3624 episodes. The size of the dataset is 190 GB. This dataset provides the opportunity for automatic action quality evaluation of FMS.

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Section: Technical Validationmentioning

confidence: 53%

Functional movement screen dataset collected with two Azure Kinect depth sensors

et al. 2022

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“…The validity and accuracy of the vision based method strongly depends on the precision and accuracy of the skeleton tracking (human keypoint estimation). There are previous studies that evaluated the Azure Kinect and its discontinued predecessors, which found that the Azure Kinect surpasses the Kinect v2 both in precision and accuracy [34,35].…”

Section: Discussionmentioning

confidence: 99%

Pilot Feasibility Study of a Multi-View Vision Based Scoring Method for Cervical Dystonia

Xiao

et al. 2022

Sensors

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Abnormal movement of the head and neck is a typical symptom of Cervical Dystonia (CD). Accurate scoring on the severity scale is of great significance for treatment planning. The traditional scoring method is to use a protractor or contact sensors to calculate the angle of the movement, but this method is time-consuming, and it will interfere with the movement of the patient. In the recent outbreak of the coronavirus disease, the need for remote diagnosis and treatment of CD has become extremely urgent for clinical practice. To solve these problems, we propose a multi-view vision based CD severity scale scoring method, which detects the keypoint positions of the patient from the frontal and lateral images, and finally scores the severity scale by calculating head and neck motion angles. We compared the Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS) subscale scores calculated by our vision based method with the scores calculated by a neurologist trained in dyskinesia. An analysis of the correlation coefficient was then conducted. Intra-class correlation (ICC)(3,1) was used to measure absolute accuracy. Our multi-view vision based CD severity scale scoring method demonstrated sufficient validity and reliability. This low-cost and contactless method provides a new potential tool for remote diagnosis and treatment of CD.

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“…The Kinect 2.0 sensor provides three types of raw data: color stream, depth stream, and raw audio stream. The Kinect 2.0 sensor has three process functioning systems: skeletal tracking, speech, and pipeline/identify [ 29 , 30 ]. In this experiment, we mainly used the skeletal tracking function system.…”

Section: Methodsmentioning

confidence: 99%

Recognition and Analysis of an Age-Friendly Intelligent Sofa Design Based on Skeletal Key-Points

Zhou

Huang

Luo

et al. 2022

IJERPH

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The aging population has a higher level of consumption willingness, higher quality of life demands, and more diversified spiritual pursuits. In recent years, age-friendly industries have entered a ’blue ocean of development’; intelligent recreation and age-friendly industries have become new growth points in the double-cycle situation; however, the current generalized design of the market is not enough to meet the needs of its elderly users. Through an extensive research and demand analysis focused on the highly-frequent use of sofas by elderly individuals, an AHP analysis showed that smart sofa design guidelines are among the most important functional indicators; user ’pain points’ focused on low seat surfaces caused by the difficulty in getting up. To further refine this action behavior, a Kinect-based experimental device was used to capture the behavior of the elderly user during the sit-to-stand transition. The experimental data were collected from 25 key skeletal points in the human body and further investigated by converting the skeletal points into 12 key joint angles to refine the joint transition threshold risk during the sit-to-stand transition for the elderly user. The test results show that the most important joint angle affecting the sit-to-stand transition process is the trunk thigh angle was θ5-2, with an important value of 0.122. The two-dimensional body data of the elderly user was mapped to the joint angles under the three-dimensional activity threshold to build a comfort model of the elderly user’s sofa, providing a theoretical basis for the design parameters of the aging sofa. In response to the research results, an intelligent age-friendly sofa with three forms was designed and prototyped independently, meeting the universal size of elderly users in terms of dimensional parameters, and iterating leisure and assisted standing transformation in terms of function (to reduce the risk levels of the sitting and standing transformations of elderly users).

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Skeleton Tracking Accuracy and Precision Evaluation of Kinect V1, Kinect V2, and the Azure Kinect

Cited by 63 publications

References 31 publications

Functional movement screen dataset collected with two Azure Kinect depth sensors

Functional movement screen dataset collected with two Azure Kinect depth sensors

Pilot Feasibility Study of a Multi-View Vision Based Scoring Method for Cervical Dystonia

Recognition and Analysis of an Age-Friendly Intelligent Sofa Design Based on Skeletal Key-Points

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