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Proceedings ArticleDOI

Kinect v2 tracked Body Joint Smoothing for Kinematic Analysis in Musculoskeletal Disorders

01 Jul 2020-Vol. 2020, pp 5769-5772
TL;DR: This work proposes a filter combined with the concept of body kinematics to remove noise and enhances the quality of 3D coordinates in body frame data and compares Range of Motion (RoM) values obtained from the proposed filter with the gold standard goniometry.

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Abstract: Body joint monitoring is essential for disorder diagnosis and assessment of treatment effectiveness. Microsoft Kinect v2 is a low-cost and markerless human motion-tracking RGB-D sensor that provides spatial locations of tracked skeletal joints in the form of 3D coordinates. Sometimes, body tracking of kinect v2 produces erratic 3D coordinates, which affects the real-time tracking performance of the sensor. A careful study of the literature suggests that skeletal tracking of kinect v2 needs further exploration. This work proposes a filter combined with the concept of body kinematics to remove noise and enhances the quality of 3D coordinates in body frame data. Also, it generates "Motion Signature" of the tracked joint, which shows movement pattern for kinematic analysis, and helpful in joint monitoring of Musculoskeletal Disorders (MSD). The clinically relevant anatomical movement was executed, to evaluate the performance of the proposed filter. We compared Range of Motion (RoM) values obtained from the proposed filter with the gold standard goniometry. Results indicate that RoM values from the proposed filter are in high correlation with the goniometry with an Intraclass Correlation Coefficient values ranging between 0.95 to 0.98 authenticating that it improves the skeletal joint tracking of kinect v2.

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Topics: Kinematics (51%)
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Journal ArticleDOI
Peter Beshara, Judy F. Chen1, Andrew C. Read, Pierre Lagadec  +2 moreInstitutions (1)
17 Dec 2020-Sensors
TL;DR: The results indicated that the HumanTrak system is an objective, valid and reliable way to assess and track shoulder ROM.

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Abstract: Background: Objective assessment of shoulder joint active range of motion (AROM) is critical to monitor patient progress after conservative or surgical intervention. Advancements in miniature devices have led researchers to validate inertial sensors to capture human movement. This study investigated the construct validity as well as intra- and inter-rater reliability of active shoulder mobility measurements using a coupled system of inertial sensors and the Microsoft Kinect (HumanTrak). Methods: 50 healthy participants with no history of shoulder pathology were tested bilaterally for fixed and free ROM: (1) shoulder flexion, and (2) abduction using HumanTrak and goniometry. The repeat testing of the standardised protocol was completed after seven days by two physiotherapists. Results: All HumanTrak shoulder movements demonstrated adequate reliability (intra-class correlation (ICC) ≥ 0.70). HumanTrak demonstrated higher intra-rater reliability (ICCs: 0.93 and 0.85) than goniometry (ICCs: 0.75 and 0.53) for measuring free shoulder flexion and abduction AROM, respectively. Similarly, HumanTrak demonstrated higher intra-rater reliability (ICCs: 0.81 and 0.94) than goniometry (ICCs: 0.70 and 0.93) for fixed flexion and abduction AROM, respectively. Construct validity between HumanTrak and goniometry was adequate except for free abduction. The differences between raters were predominately acceptable and below ±10°. Conclusions: These results indicated that the HumanTrak system is an objective, valid and reliable way to assess and track shoulder ROM.

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2 citations


Cites methods from "Kinect v2 tracked Body Joint Smooth..."

  • ...[46] compared the Kinect (v2) and goniometry and reported a high correlation for ROM measurements with ICC values ranging between 0....

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References
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Journal ArticleDOI
Terry K. Koo1, Mae Y. Li1Institutions (1)
TL;DR: A practical guideline for clinical researchers to choose the correct form of ICC is provided and the best practice of reporting ICC parameters in scientific publications is suggested.

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Abstract: Objective Intraclass correlation coefficient (ICC) is a widely used reliability index in test-retest, intrarater, and interrater reliability analyses. This article introduces the basic concept of ICC in the content of reliability analysis.

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6,924 citations


"Kinect v2 tracked Body Joint Smooth..." refers methods in this paper

  • ...The ICC values are in the range between 0.95 to 0.98, which show excellent reliability of proposed filter’s result as compared with [14] and [15]....

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  • ...Two-way random effects, absolute agreement, and multiple measurement model ICC (2,k) [13], was chosen in this study....

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  • ...Intraclass Correlation (ICC) [13], which is a modification of the Pearson correlation coefficient, was used to find agreement between the RoM values obtained from both the methods....

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  • ...Table I shows that ICC values are in the range between 0.95 to 0.98. and mean differences of RoM measurements are in the range between 3.8 to 6.1 degree, which shows high agreement for RoM values between the proposed filter and gold standard goniometry....

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Proceedings ArticleDOI
Jamie Shotton1, Andrew Fitzgibbon1, Mat Cook1, Toby Sharp1  +4 moreInstitutions (1)
20 Jun 2011-
TL;DR: This work takes an object recognition approach, designing an intermediate body parts representation that maps the difficult pose estimation problem into a simpler per-pixel classification problem, and generates confidence-scored 3D proposals of several body joints by reprojecting the classification result and finding local modes.

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Abstract: We propose a new method to quickly and accurately predict 3D positions of body joints from a single depth image, using no temporal information. We take an object recognition approach, designing an intermediate body parts representation that maps the difficult pose estimation problem into a simpler per-pixel classification problem. Our large and highly varied training dataset allows the classifier to estimate body parts invariant to pose, body shape, clothing, etc. Finally we generate confidence-scored 3D proposals of several body joints by reprojecting the classification result and finding local modes. The system runs at 200 frames per second on consumer hardware. Our evaluation shows high accuracy on both synthetic and real test sets, and investigates the effect of several training parameters. We achieve state of the art accuracy in our comparison with related work and demonstrate improved generalization over exact whole-skeleton nearest neighbor matching.

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3,297 citations


7


Journal ArticleDOI
TL;DR: This work takes an object recognition approach, designing an intermediate body parts representation that maps the difficult pose estimation problem into a simpler per-pixel classification problem, and generates confidence-scored 3D proposals of several body joints by reprojecting the classification result and finding local modes.

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Abstract: We propose a new method to quickly and accurately predict human pose---the 3D positions of body joints---from a single depth image, without depending on information from preceding frames. Our approach is strongly rooted in current object recognition strategies. By designing an intermediate representation in terms of body parts, the difficult pose estimation problem is transformed into a simpler per-pixel classification problem, for which efficient machine learning techniques exist. By using computer graphics to synthesize a very large dataset of training image pairs, one can train a classifier that estimates body part labels from test images invariant to pose, body shape, clothing, and other irrelevances. Finally, we generate confidence-scored 3D proposals of several body joints by reprojecting the classification result and finding local modes.The system runs in under 5ms on the Xbox 360. Our evaluation shows high accuracy on both synthetic and real test sets, and investigates the effect of several training parameters. We achieve state-of-the-art accuracy in our comparison with related work and demonstrate improved generalization over exact whole-skeleton nearest neighbor matching.

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2,979 citations


"Kinect v2 tracked Body Joint Smooth..." refers background in this paper

  • ...It is reported in [12] that kinect v2 classify human body-...

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Journal ArticleDOI
Zhengyou Zhang1Institutions (1)
01 Apr 2012-IEEE MultiMedia
TL;DR: While the Kinect sensor incorporates several advanced sensing hardware, this article focuses on the vision aspect of the sensor and its impact beyond the gaming industry.

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Abstract: Recent advances in 3D depth cameras such as Microsoft Kinect sensors (www.xbox.com/en-US/kinect) have created many opportunities for multimedia computing. The Kinect sensor lets the computer directly sense the third dimension (depth) of the players and the environment. It also understands when users talk, knows who they are when they walk up to it, and can interpret their movements and translate them into a format that developers can use to build new experiences. While the Kinect sensor incorporates several advanced sensing hardware, this article focuses on the vision aspect of the Kinect sensor and its impact beyond the gaming industry.

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1,911 citations


"Kinect v2 tracked Body Joint Smooth..." refers methods in this paper

  • ...Adjeisah et al. [7] have proposed holt exponential smoothing filter [8] to smooth jitters present in tracked skeletal joints for gesture recognition in NUI based applications....

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  • ...Microsoft Kinect v2 is a low-cost, easy to set up, and vision-based marker-less motion sensor, based on Natural User Interface (NUI) [5]....

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Journal ArticleDOI
01 Jun 1988-The Statistician
TL;DR: The Holt-Winters forecasting procedure is a variant of exponential smoothing which is simple, yet generally works well in practice, and is particularly suitable for producing short-term forecasts for sales or demand time-series data.

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Abstract: The Holt-Winters forecasting procedure is a variant of exponential smoothing which is simple, yet generally works well in practice, and is particularly suitable for producing short-term forecasts for sales or demand time-series data. Some practical problems in implementing the method are discussed, including the normalisation of seasonal indices, the choice of starting values and the choice of smoothing parameters. There is an important distinction between an automatic and a nonautomatic approach to forecasting and detailed suggestions are made for implementing Holt-Winters in both ways. The question as to what underlying model, if any, is assumed by the method is also addressed. Some possible areas for future research are then outlined.

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273 citations


"Kinect v2 tracked Body Joint Smooth..." refers background in this paper

  • ...[7] have proposed holt exponential smoothing filter [8] to smooth jitters present in tracked skeletal joints for gesture recognition in NUI based applications....

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