Visibility Analysis on Swing Motion of the GolfPlayer Based on Kinect

Zhelin Li1(&), Songbin Ye1, Lijun Jiang1, Yaqi Wang1, Deli Zhu2,and Xiaotong Fu3

1 School of Design, South China University of Technology,Guangzhou 510006, China

{zhelinli,ljjiang}@scut.edu.cn, gzwoods@qq.com2 Department of Sports Management,

Guangdong Vocational Institute of Sports, Guangzhou 510663, China491014214@qq.com

3 School of Computer Science and Engineering,South China University of Technology, Guangzhou 510006, China

Abstract. This article presents an analysis of using the second generationMicrosoft Kinect to track user’s skeletal joints on golf swing motion. Theskeletal joints tracking status data were collected in the experiment based on tengolf players, including four swing postures of eight swing directions. Varianceand average value are used to figure out the distribution rule of skeletal jointstracking status information in the eight swing directions. The result shows thatthe visibility ratio of skeletal joints is between 12.67% and 13.51% in eightswing directions. When swinging directions on −135-degree, −45-degree,45-degree relative to the Kinect Y-axis plane, it gets the high confidence level.This conclusion can apply to the golf swing motion analytical system based onKinect sensors.

Keywords: Swing motion � Skeletal joints � Swing direction � Visibility

1 Introduction

People can fully enjoy golf at any age or skill level, for its widespread and increasingpopularity. In order to increase Golf-swing playability and make it more suitable for anamateur to practice, the analytical of the golf swing is imperative. Golf professionals aimto educate a golfer on the best approach to utilize the body and club during the swingwhich will transfer the most amount of energy into the ball and maximize the drivingdistance [1]. In this paper, Kinect was used to assess the joints’ visibility (skeletal jointstracking status information) and extracted joints’ coordinate 3D-dimension data.

The capture and analysis of human behaviors such as jumping, running, swing, arecommon in some domains, including sports science, musculoskeletal injury manage-ment and the human-computer interaction [2, 3]. It requires highly accurate motioncapture in the analysis of joint angle, position, and angular velocities. It is outside thereach of most users of the highly accurate motion capture systems, whether based oncamera or inertia sensor. To popularize motion capture technology, Microsoft

© Springer International Publishing AG 2017V.G. Duffy (Ed.): DHM 2017, Part I, LNCS 10286, pp. 115–126, 2017.DOI: 10.1007/978-3-319-58463-8_11

cooperation has rolled out the Kinect which bases on depth camera, as low-costalternatives. The Kinect uses an infrared based active stereo vision system to get adepth map of the observed scene, and it was designed to recognize human gestures andskeleton joints. There are still challenging problem due to strong noise in-depth dataand self-occlusion when using Kinect to analyze the golf swing motion. Yeung et al.[4] evaluated Kinect as a clinical assessment tool for Total body center of mass swaymeasurement, and their results revealed the Kinect system produced a highly correlatedmeasurement of Total body center of mass and comparable intra-session reliability toVicon (Motion Capture Systems). Previous studies have indicated a positive relation-ship between Microsoft Kinect and OptiTrack motion tracking system. Høilund et al.[5] investigated the precision between the Microsoft Kinect and OptiTrack and foundthat the OptiTrack and the Kinect has been shown to deliver approximately the sameresults, with some restrictions. Wang et al. [6] examined the first generation Kinect andthe second generation Microsoft Kinect and found that the second generation Kinectprovides better accuracy in joint estimation while providing skeletal tracking that ismore robust to occlusion and body rotation than the first generation Kinect released at2010. Kinect has limited visible area and could not track the human limbs whichbehind others limbs. Thus, just one Kinect is not enough for tracking the golf swingmotion. However, multiple Kinect sensors have broader visible area even 360 degrees,and users do not need facing the Kinect directly. Williamson et al. [7] usingmulti-Kinect tracking for dismounted soldier training, and within the Microsoft KinectSoftware Development Kit that can be merged using commercially available tools andadvanced fusion algorithms to produce better quality representations of users in the realworld within a virtual environment. Furthermore, several Kinect coupled with inferencealgorithms can produce a much better tracked representation as users move around.

In order to figure out accurately joint 3D-dimensional data, Kinect was using totrack the joint and figure out each joint visibility ratio during one full golf swing.Tracking a golf swing with only one Kinect sensor could cause tracking all the swingmotion to be difficult since swing speed is typically fast, both arms overlap each other,and the self-occlusion in the motions being captured. For obtaining larger visible area,we prepare to use Multiple-Kinect to capture the golf swing.

2 Materials and Methods

2.1 Experimental Equipment

Kinect for Windows 2.0 (Xbox one), Dell M8600 mobile workstation, tripods, iphone7take photos, Cougar as the hardware. We used the Kinect SDK-v2.0_1409-Setup.exe,skeletal Joint analysis program as the software.

2.2 Experimental Scheme

In this experiment, the subjects are the sophomore professional golf students. TheMicrosoft Kinect v2 was used to collect four feature golf swing motions whichincluding golf swing setup posture, the top of the swing, impact time and finish swing

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motions and captured ten students including eight golf swing directions. Golf swingdirections take turns every 45-degree from facing directly to the Kinect to −45-degreeangle to Kinect coordinate Y-axis, as showed in Fig. 1.

2.3 Experimental Procedures

Before the experiment, the eight swing directions position was marked on the ground.Every participant posed the four feature golf swing motion and did the next swingdirection. All participants took turns to pose the eight swing directions exactly. Thenwe collected the skeletal data when giving a sign (Fig. 2).

(a) Human skeletal joints (b) Swing directions

Fig. 1. (a) Human skeletal joints by Kinect. (b) Eight swing directions

(a) Setup (b) Top (c) Impact (d) Finish

Fig. 2. Four feature swing postures. (a) Swing setup, prepare to upon the club. (b) Top of swingposition. (c) Time of shots the ball. (d) End of golf swing motion

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3 Data Analyses

Kinect may perform differently on the same swing posture. In swing directions, theparticipants were asked to hold on one swing motion posture until we collected 30frames data. To improve the data reliability, the least joint amount of frames will beextracted as the current swing posture data.

3.1 Joints Moving Distance Analysis of One Full Golf Swing Motion

Each joint has different moving distance on full golf swing. Thus, some joints will beweighted up, and some joints will be weighted down. Figure 3 shows which jointshould be weighted up or weighted down using the joints moving distance.

Figure 3 describes that joints of SpineMid, Neck, Head, ShoulderLeft, Shoul-derRight, HipLeft, KneeLeft, FootLeft, AnkleLeft, HipRight, KneeRight, AnkleRight,FootRight and SpineShoulder’s performance are relatively stable. SpineBase, Elbow-Left, WristLeft, HandLeft, ElbowRight, WristRight, HandRight, HandLeft, Thum-bLeft, HandRight and ThumbRight joints’ performance are unstable. Joints’movingdistance averages show how long they go through during full swing motion. The longerdistance averages, the more vitality of joint. Combine with joints tracking status ofTable 3, some joints will be weighted up or weighted down among these joints.

3.2 Joint Tracking Status Data on Four Swing Postures of Eight SwingDirections

The two following figures demonstrate joint tracking status data which was collectedfrom 10 participants. And the X-axle value means eight swing directions; the Y-axlevalue means joint accurate tracking status amount. The joints visibility status waspresented graphically, as shown in Fig. 4. Total information was shown in Tables 4and 5. For example, visibility of eight swing directions of ElbowLeft swing_setup

Fig. 3. Joint moving distance on full golf swing motion

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posture was presented on scatter diagram. The scatter diagram illustrates that on theswing directions of 3 and 4, the joint tracking status amount of ElbowLeft achieve ninewhile others are below seven. This statistics data of swing directions 3 and 4 showshigh tracking confidence level, while averages the joints data possess highly accurate.

According to the Fig. 4 analytical process, the joints tracking status data wascategorized into high tracking confident level and low tracking confident level.

3.3 Joint Average and Variance

Statistic method of the average value and variance value is used to assess the jointstracking data performance. Joint tracking status data on four swing postures of eightswing directions of ten participants are presented in the two following table.

To assess each joint tracking amount on four swing postures of eight swingdirections, the average is used for evaluating that. The average for each joint trackingamount is computed as

�x ¼ 18

X8

i¼1

xi ð1Þ

xi value means each joint tracking status amount of eight swing directions, �x valuemeans the joint tracking status amount average value.

The variance for each joint tracking amount is computed as

r2 ¼ 18

X8

i¼1

xi� �x ð2Þ

r2 value means the variance of each joint tracking status amount of eight swingdirections.

Fig. 4. ElbowLeft Swing_Setup tracking status amount

Visibility Analysis on Swing Motion of the Golf Player 119

Joint tracking status data offour swing postures of eight swing directions’ average andvariance is presented in Table 3. Table 3 illustrates that joints of SpineBase, SpineMid,Neck, Head, HipLeft, KneeLeft, HipRight, KneeRight, and SpineShoulder maximumaverage and minimize variance receive high average value and variance. The average ofthose joints’ average is between 8.875 and 10, and the variance is between 0 and 1.6875.Statistic data of average value and variance value represents high tracking confident level.And joints of ShoulderLeft, ElbowLeft, WristLeft, HandLeft, ShoulderRight, Elbo-wRight, WristRight, HandRight, AnkleLeft, FootLeft, AnkleRight, FootRight, Hand-TipRight, ThumbLeft, HandTipRight, and ThumbRight are categorized into low trackingconfident level since receiving low average value and high variance value.

Combine with the above three tables, joints of SpineBase, SpineMid, Neck, Head,HipLeft, KneeLeft, HipRight, KneeRight and SpineShoulder perform high trackingconfident level, while could apply these joints 3D-dimension data to golf swing ana-lytical system. Take ElbowLeft for example. The statistical data shows that the swingposture of swing_setup, ElbowLeft joint obtains nine out of 10 times accurate trackingdata on swing directions of 3 and 4. On the swing posture of swing_Top, ElbowLeftjoint obtains 10 times accurate tracking data on the swing directions of 1 and 9 out of10 times on the swing directions of 4. On the swing posture of swing_impact,ElbowLeft joint obtains 10 times accurate tracking data on the swing directions of 1, 2and 8, 9 out of 10 times on the swing directions of 3 and 4. On the swing posture ofswing_Finish, ElbowLeft obtains 10 times accurate tracking data on the swing direc-tions of 4, 5, and 6, and 9 out of 10 times on the swing directions of 3.

Without description all of the joints tracking status information, the two followingtables show how to extract to value joint information. These two tables distinguish thejoint tracking status data confident level from the different geometric figure. The circle( ○) means joint tracking data receive high tracking confident level, the square (□)means joint tracking data receive moderate tracking confident level, triangle ( ▵) meansjoint tracking data receive low tracking confident level and the null cells means the dataof the joint can’t be applied to the application.

Fig. 5. Total joint tracking data of eight swing directions

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3.4 Joints Weight Analysis

From the above discussion results (Fig. 5 and Tables 1, 2, and 3), it shows that joints ofWristLeft, WristRight, HandLeft and HandRight have a big contribution to the fullswing motion, but have poor performance for the small average value. Joints ofWristLeft, WristRight, HandLeft, and HandRight should be weighted down whenapplying to the golf swing motion analysis system. Joints of SpineBase, SpineMid,Neck, Head, HipLeft, HipRight, SpineShoulder have highly visible and stable per-formance on the full swing motion. On the basis of golf characteristic, pelvis hasimportant significance on a swing [8]. Thus, HipLeft and HipRight should be weightedup. Joints of ElbowLeft, ElbowRight, KneeLeft, KneeRight, AnkleLeft, AnkleRight,FootLeft, and FootRight receive high average value, also make an important contri-bution to golf swing should be weighted down. While HandTipLeft, HandTipRight,ThumbLeft, ThumbRight make little contribution to golf swing according to sportsbiomechanical principles [9].

Table 1. 10 participants joint tracking status data on Swing_Setup and Swing_Top postures ofeight swing directions

JointName Swing_Setup Swing_Top1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8

SpineBase 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10SpineMid 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10Neck 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10Head 10 10 10 10 10 10 10 10 10 10 10 8 10 10 10 10ShoulderLeft 10 9 10 9 8 1 1 10 9 10 10 5 8 1 1 10ElbowLeft 2 5 9 9 3 7 6 6 10 0 5 9 3 7 6 6WristLeft 2 5 5 8 1 6 5 5 8 2 6 8 1 6 5 5HandLeft 1 4 6 4 0 1 3 2 5 1 6 5 0 1 3 2ShoulderRight 9 9 3 4 8 10 10 10 8 7 8 6 8 10 10 10ElbowRight 1 4 5 4 4 7 6 6 8 6 3 5 4 7 6 6WristRight 1 4 6 6 2 2 5 6 6 2 1 8 2 2 5 6HandRight 0 3 5 3 1 1 4 5 2 0 2 8 1 1 4 5HipLeft 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10KneeLeft 10 10 8 7 10 9 7 10 10 10 9 9 10 9 7 10AnkleLeft 10 10 10 6 8 10 6 9 10 10 10 8 8 10 6 9FootLeft 8 6 7 9 7 9 5 7 7 7 7 9 7 9 5 7HipRight 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10KneeRight 10 10 5 10 10 10 9 10 10 9 9 8 10 10 9 10AnkleRight 10 9 4 10 9 8 9 10 10 8 3 10 9 8 9 10FootRight 9 8 5 8 9 7 7 7 8 7 8 9 9 7 7 7SpineShoulder 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10HandTipLeft 10 10 10 10 4 7 7 10 8 7 7 9 4 7 7 10ThumbLeft 10 10 10 10 4 7 6 10 7 6 6 7 4 7 6 10HandTipRight 10 10 8 9 8 9 10 10 3 6 4 7 8 9 10 10ThumbRight 10 8 8 9 8 9 10 10 2 6 4 7 8 9 10 10

Visibility Analysis on Swing Motion of the Golf Player 121

3.5 Total Joint Tracking Data of Eight Swing Directions

From what has been discussed above, the invalid joint of HandLeft and HandRight arewiped out of the statistical table. And the value joint tracking data is presented inFig. 5. Figure 5 shows that swing directions 8 receive the highest joint tracking rate of13.51%, while swing directions 5 receives the lowest joint tracking rate of 11.92%.This result can be applied to Multiple Kinect golf swing analytical system. When usingtwo Kinects, we can locate the Kinect to swing directions 8 and 4, while Kinect shouldface directly to the user. While using three Kinect, we can locate the Kinect to swingdirections 8, 4 and 1, while Kinect should face directly to the user. The more Kinectused, we can locate the Kinect following the law which shows in Fig. 5.

Table 2. 10 participants’ joint visibility status data on Swing_Impact and Swing_Finishpostures of eight swing directions

JointName Swing_Impact Swing_Finish1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8

SpineBase 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10SpineMid 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10Neck 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10Head 10 10 10 8 10 9 10 10 10 10 10 10 9 10 10 10ShoulderLeft 10 10 9 5 9 5 2 10 9 9 7 8 10 9 9 10ElbowLeft 10 10 9 9 3 7 5 10 4 4 9 10 10 10 8 4WristLeft 10 10 7 8 1 2 5 10 1 2 6 10 10 10 7 3HandLeft 9 5 6 5 0 2 4 7 1 0 2 10 9 7 4 4ShoulderRight 10 8 5 6 6 9 9 10 8 9 9 8 4 0 8 10ElbowRight 10 8 10 5 3 8 6 10 7 5 9 10 9 8 7 10WristRight 9 7 6 8 3 6 7 9 5 4 6 9 8 2 8 7HandRight 1 1 6 8 2 2 5 6 2 1 2 1 2 1 7 8HipLeft 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10KneeLeft 10 10 10 9 9 9 7 10 8 10 10 9 10 9 9 7AnkleLeft 10 10 10 8 9 9 5 8 9 9 10 8 7 10 7 9FootLeft 9 6 8 9 7 8 7 7 10 7 9 7 6 6 9 10HipRight 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10KneeRight 10 10 10 8 10 8 10 10 9 10 10 7 9 10 10 9AnkleRight 10 9 6 10 8 7 8 10 10 10 9 8 8 9 9 8FootRight 9 7 7 9 7 6 8 5 10 9 5 7 2 5 8 9SpineShoulder 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10HandTipLeft 10 10 10 9 3 7 10 10 7 5 7 10 10 10 8 4ThumbLeft 10 10 10 7 3 7 10 10 5 5 6 10 10 10 8 4HandTipRight 10 10 9 7 6 10 9 10 7 4 8 10 10 10 9 10ThumbRight 10 10 9 7 4 10 9 10 7 3 7 10 10 10 9 10

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4 Discussion

This study evaluated the human skeletal joint tracking status information withMicrosoft Kinect sensor. Low cost, makerless, high tracking accurately makes Kinectbe most popular motion captures sensor. Though Kinect does the good performance inthe game field, still contains the disadvantage, like low frames, self-occlusion, datajitter.

Destelle et al. [10] proposed to fuse the joint position information obtained from thepopular Kinect sensor with more precise estimation of body segment orientations pro-vided by a small number of wearable inertial sensors. The use of inertial sensors can helpto address many of the well-known limitations of the Kinect sensor and enhance the jointangle measurement accuracy. We assessed the skeletal joint tracking amount of differentswing directions and extract the valuable joint data to form an integral skeleton.

Table 3. Joints tracking status average and variance value

JointName Average VarianceSetup Top Impact Finish Setup Top Impact Finish

SpineBase 10.000 10.000 10.000 10.000 0.000 0.000 0.000 0.000SpineMid 10.000 10.000 10.000 10.000 0.000 0.000 0.000 0.000Neck 10.000 10.000 10.000 10.000 0.000 0.000 0.000 0.000Head 10.000 9.750 9.625 9.875 0.000 0.438 0.484 0.109ShoulderLeft 10.000 6.750 7.500 8.875 13.438 13.438 8.250 0.859ElbowLeft 5.875 5.750 7.875 7.375 5.609 8.938 6.109 7.234WristLeft 4.625 5.125 6.625 6.125 4.234 5.609 11.484 12.359HandLeft 2.625 2.875 4.750 4.625 3.484 4.359 6.938 11.984ShoulderRight 7.875 8.375 7.875 7.000 6.859 1.984 3.359 9.750ElbowRight 4.625 5.625 7.500 8.125 2.984 2.234 6.000 2.609WristRight 4.000 4.000 6.875 6.125 3.750 5.750 3.359 4.859HandRight 2.750 2.875 3.875 3.000 3.188 6.109 6.359 7.000HipLeft 10.000 10.000 10.000 10.000 0.000 0.000 0.000 0.000KneeLeft 8.875 9.250 9.250 9.000 1.609 0.938 0.938 1.000AnkleLeft 8.625 8.875 8.625 8.625 2.734 1.859 2.484 1.234FootLeft 7.250 7.250 7.625 8.000 1.688 1.438 0.984 2.500HipRight 10.000 10.000 10.000 10.000 0.000 0.000 0.000 0.000KneeRight 9.250 9.375 9.500 9.250 2.688 0.484 0.750 0.938AnkleRight 8.625 8.375 8.500 8.875 3.484 4.734 2.000 0.609FootRight 7.500 7.750 7.250 6.875 1.500 0.688 1.688 6.359SpineShoulder 10.000 10.000 10.000 10.000 0.000 0.000 0.000 0.000HandTipLeft 8.500 7.375 8.625 7.625 4.500 2.734 5.484 4.734ThumbLeft 8.375 6.625 8.375 7.250 4.984 2.484 5.734 5.688HandTipRight 9.250 7.125 8.875 8.500 0.688 6.109 2.109 4.000ThumbRight 9.000 7.000 8.625 8.250 0.750 7.250 3.984 5.438Average 7.905 7.605 8.310 8.135

Visibility Analysis on Swing Motion of the Golf Player 123

Table 4. Value of joint swing posture on setup and top swing postures

Table 5. Value of joint swing posture on impact and finish swing postures

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To ascertain the joint accurate position is common of two studies. Zhang et al. [11]displayed a method of scoring time-sequential postures of the golf swing. Unlike theirstudy, they extracted the time-sequential posture of golf swing features when swing wasperformed and used HMM-NF models for scoring. They proposed methods can beimplemented to identify and score the golf swing effectively with up to 80% accuracyrate.

The results presented here demonstrate the feasibility of using Kinect to analyzegolf swing motion. Above analysis and conclusion indicate that human body skeletaljoints can be capture most of the joints which extract from Kinect sensor. Tables 1 and2 show that, different swing directions due to different joint tracking amount. Statisticalanalyses show Kinect can be placed an ideal location for obtaining high joint trackingconfident, while this conclusion can be applied to the golf swing.

5 Conclusions

The overall goal of this study was to investigate visibility of each joint obtained byKinect on four swing postures of eight swing directions. The results indicate thatdifferent swing directions due to different joint tracking amount of swing posture. Ourstudy found that valuable joints can be extracted from various swing directions postureto fill a whole human skeleton. We propose a method of complementary joints whichcan enhance the valuable joint amount. This research can be applied to Multiple-Kinectgolf swing analytical system to enhance the system reliability and robustness.

Acknowledgments. Guangzhou science research special project (201607010308); Guangzhoupolytechnic of sport provided the experiment subjects and filed.

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