Performance measurements for the Microsoft Kinect skeleton

Livingston, Mark; Sebastian, Jay; Ai, Zhuming; Decker, Jonathan W.

doi:10.1109/vr.2012.6180911

Cited by 110 publications

(62 citation statements)

References 2 publications

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“…In Livingston et al (2012), human skeletons tracked by a Microsoft software development kit are evaluated based on the positions of body joints (e.g., arms and hands) along a meter stick, and the average error and standard deviation in this experiment are 5.6 mm and 8.1 mm, respectively. Fernandez-Baena et al (2012) conduct an experiment associated with rehabilitation treatments to compare the accuracy between a Kinect-combined with Natural Interaction Technology for End-user (NITE)-and a VICON in terms of the rotation angles of knee, hip, and shoulder joints, defined as angles between two vectors of body parts (e.g., one from knee to foot); the results show that the differences in rotation angles range from 6.78 to 8.98 degrees for a knee, from 5.53 to 9.92 degrees for a hip, and from 7 to 13 degrees for a shoulder.…”

Section: Previous Work On the Performance Evaluation Of An Rgb-d Sensormentioning

confidence: 99%

Empirical assessment of a RGB-D sensor on motion capture and action recognition for construction worker monitoring

et al. 2013

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Background: For construction management, data collection is a critical process for gathering and measuring information for the evaluation and control of ongoing project performances. Taking into account that construction involves a significant amount of manual work, worker monitoring can play a key role in analyzing operations and improving productivity and safety. However, time-consuming tasks involved in field observation have brought up the issue of implementing worker observation in daily management practice. Methods: In an effort to address the issue, this paper investigates the performances of a cost-effective and portable RGB-D sensor, based on recent research efforts extended from our previous study. The performance of an RGB-D sensor is evaluated in terms of (1) the 3D positions of the body parts tracked by the sensor, (2) the 3D rotation angles at joints, and (3) the impact of the RGB-D sensor's accuracy on motion analysis. For the assessment, experimental studies were undertaken to collect motion capture datasets using an RGB-D sensor and a markerbased motion capture system, VICON, and to analyze errors as compared with the VICON used as the ground truth. As a test case, 25 trials of ascending and descending during ladder climbing were recorded simultaneously with both systems, and the resulting motion capture datasets (i.e., 3D skeleton models) were temporally and spatially synchronized for their comparison. Results: Through the comparative assessment, we found a discrepancy of 10.7 cm in the tracked locations of body parts, and a difference of 16.2 degrees in rotation angles. However, motion detection results show that the inaccuracy of an RGB-D sensor does not have a considerable effect on action recognition in the experiment. Conclusions: This paper thus provides insight into the accuracy of an RGB-D sensor on motion capture in various measures and directions of further research for the improvement of accuracy.

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Section: Previous Work On the Performance Evaluation Of An Rgb-d Sensormentioning

confidence: 99%

Empirical assessment of a RGB-D sensor on motion capture and action recognition for construction worker monitoring

et al. 2013

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“…Augmented video was adopted in order to avoid participants' distractions while performing the tasks. For instance, a person may judge his/her movements based on a hand cursor and try to make adjustments [20], especially because of the sensor noise [21] [10] [12] which should be avoided. We fine-tuned these experimental conditions through a set of trial experiments.…”

Section: Apparatus and Methodsmentioning

confidence: 99%

Estimating Production Time of Touchless Hand Drawing Gestures

Erazo

Pino

Antunes

2015

Human-Computer Interaction – INTERACT 2015

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“…The accuracy of Kinect's 3D map [10][11][12] and the ability of its software tool in estimating the position of joints have been evaluated by di erent researchers [13][14][15][16]. As shown in these works, Kinect's accuracy in estimating static joint positions is about 4-7 cm [16].…”

Section: Introductionmentioning

confidence: 99%

Hand acceleration measurement by Kinect for rehabilitation applications

2017

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Abstract. A ordable motion sensors that are recently developed for video gaming have formed a budding line of research in the eld of physical rehabilitation. These sensors have been used in many task-based applications to analyze the patients' status based on their completion of assigned tasks. However, as the accuracy of such sensors is lower than that of the clinical ones, their measured data has had very limited use in quantitative motion analysis to this date. The aim of this article is to determine Kinect's ability and accuracy in calculating higher-order kinematic parameters, such as velocity and acceleration, in hand movements. Four methods, i.e. moving average, Butterworth lter, B-spline, and Kalman lter, were proposed to calculate velocity and acceleration from Kinect's raw position data. The results were experimentally compared with two established motion capture systems, i.e. Vicon and Xsens, to analyze the strengths and weaknesses of each method. The results show that B-spline is the best method for calculating velocity and acceleration from Kinect's position data. Using this method, these parameters can be measured with an acceptable accuracy.

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Performance measurements for the Microsoft Kinect skeleton

Cited by 110 publications

References 2 publications

Empirical assessment of a RGB-D sensor on motion capture and action recognition for construction worker monitoring

Empirical assessment of a RGB-D sensor on motion capture and action recognition for construction worker monitoring

Estimating Production Time of Touchless Hand Drawing Gestures

Hand acceleration measurement by Kinect for rehabilitation applications

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