5
thAsia-Pacific Congress on Sports Technology (APCST)
On the difference in swing arm kinematics between low
handicap golfers and non-golfers using wireless inertial
sensors
Daniel T. H. Lai
ab*, Marco Hetchl
b, XiaoChen Wei
a, Kevin Ball
c, Patrick
Mclaughlin
caCentre of Telecommunications and Microelectronics, School of Engineering and Science, Victoria University, Australia bUniversity of Applied Science Regensburg, Prüfeninger Strasse 58, 93049 Regensburg, Germany
cInstitute of Sport, Exercise and Active Living, School of Sport and Exercise Science, Victoria University, Australia Received 4 April 2011; revised 14 May 2011; accepted 16 May 2011
Abstract
The biomechanics of the golf swing has been an area of intense research interest in an attempt to improve swing performance and minimize golf related injuries e.g. lower back pain, golfer’s elbow and tendon injuries. Measurements of golf swing kinematics have been previously done using optical motion capture systems e.g. Vicon, Optotrak, which require expensive bulky cameras and long setup times. Further, these systems have largely been limited to the lab or controlled outdoor environments. The recent introduction of micro-electromechanical systems (MEMs) inertial sensors has opened up new avenues to measuring human movement in sports during actual play and competition performance. These sensors have a small form factor, are lightweight, portable and cost effective. In this paper, we analyse the golf swing kinematics measured from inertial sensors to establish sensor parameters which could differentiate between skilled golfers and non-golfers (high handicap). It was found that the sensor data from the hand and upper arm were most discriminative of the two groups. Skilled golfers were able to consistently deliver high acceleration swings with lower pelvis movement compared to beginner golfers.
© 2011 Published by Elsevier Ltd.
Selection and peer-review under responsibility of RMIT University Keywords: Accelerometers; gyroscopes; inertial sensors; golf swing
* Corresponding author. Tel.: +61399194425; fax: +6139919408. E-mail address: Daniel.lai@vu.edu.au.
1877–7058 © 2011 Published by Elsevier Ltd. doi:10.1016/j.proeng.2011.05.076
Procedia Engineering 13 (2011) 219–225
Open access under CC BY-NC-ND license.
1. Introduction
From the combined developments of golf increasing in popularity and miniaturisation of technology, there is an emerging interest to improve play through the use of new sports technology. Good golf swing technique has been identified as a crucial factor for achieving a good golf shot or putt in the game [1]. Unfortunately, the golf swing is one of the most difficult biomechanical motions in sport to execute as the accuracy of hitting a ball is highly dependent on correct limb coordination. A thorough understanding of this coordination in the field gained from measurement of the limb kinematics of the golf swing would be beneficial to both professional and amateur golfers.
The golf swing is a complex athletic movement requiring an efficient sequence of movements, including address, backswing, downswing, impact, and follow-through [2]. A good swing results in good distance, accuracy, and consistency. However, incorrect and poor golf-swing mechanics increases the risk of injury e.g. tendon injuries, as the swing requires higher velocity in certain body segments to increase power at the expense of inducing additional stress in other segments [3, 4]. The main research objective in this field would be to improve golf-swing motion, which will consequently result in better performance in distance, accuracy and consistency and minimize injury risk.
Past studies on the measurements of golf swing kinematics revealed that the back swing and down swings are achieved by rotational motions of the trunk, pelvis, arm, hand, and club. Kinematic analysis of body segments is an efficient way for golf swing analysis [5]. The Direct Linear Transformation (DLT) is the most common method for kinematical analysis and measurement in various sports [6, 7]. Elliott et al. [8] examined the relationship between the upper limb segment rotations and the velocity of a racket-head in the squash forehand. Stuelcken et al. [9] measured the impact of the offside front foot drive on men’s high performance cricket using DLT. Moreover, this method has been used to measure translations, rotation, mass and stiffness coefficients of areas on the player’s body and golf club based on extracted motion data [10]. However, there are limitations in DLT as a wide range of motion prevents measurement of all kinematic parameters. Frequently markers of segments such as the wrist and the ankle are occluded to the camera resulting in poor measurements [11]. More recently, optoelectronic motion capture has been utilized in kinematic measurements. However, difficulties such as positioning of multiple cameras, alignment issues, occlusion and calibration of high-speed cameras still exist [12, 13]. In addition, the number of markers supported by the system for the required sampling rates appear to be insufficient for full body modelling [14, 15].
Recently, inertial sensors consisting of accelerometers and gyroscopes have been used in sports. Shinichiro and Okuno [16] applied inertia sensors in swimming stroke analysis and could discriminate stroke phases, allowing real time coach feedback from the poolside. Wireless inertial sensors have also been positioned on the golf club to measure club head kinematics [17]. In this paper, we show that inertial sensors can be used to reveal swing pattern differences which are related to the accuracy of hitting a golf ball. It was found that golf players with low handicap had consistent inter-trial swing kinematics, were able to get more angular velocity in a shorter amount of time and had higher peak accelerations, compared to beginner players.
2. Experimental Methods
Ten healthy subjects, consisting of six beginner golfers (B) and four skilled low handicap golfers (P, handicap < 10), were recruited for the study. The beginner golfers consisted of players who occasionally played but had minimal experience. All golfers played golf for recreational purposes. Inertial sensors consisting of tri-axis accelerometers and tri-axis gyroscopes (MTX, Xsense) were attached to the swing lead hand, swing lead arm, pelvis and upper trunk of the subjects. The sampling frequency was set to 100
Hz. Experiments were conducted in the Biomechanics Laboratory of Victoria University. Players were positioned on a hexagonal golf mat with a rubber golf tee. A net was positioned approximately 3 m from the tee in the direction of the swing. All players used the same driver club. Players first performed five swing trials without hitting the golf ball. They were then allowed several trials to hit the golf ball to familiarize themselves with the club. Once they were comfortable with club dimensions, they were given directions to drive the ball down the line towards the middle of the net. A hit trial was considered successful if players managed to hit the ball into the net, while a miss trial was recorded otherwise. Each player was asked to perform 10 successful ball hits.
2.1. Sensor data pre-processing
The raw inertial sensor data was exported using the MT Manager Software (XSens Technologies) to ASCII text-files for processing, resulting in 4 separate limb files for each swing trial. Each limb file contained tri-axis accelerometer and gyroscope data. Data was imported to DIAdem (National Instruments) software for further data processing and calculation. All data was time-aligned to a common reference point due to the differences in swing kinematics between subjects. The common swing event was selected to coincide with the maximum negative peak in the X-axis for the hand gyroscope sensor (Figure 1). This point was made frame 200 (Figure 1) for all trials and subjects during the swing to obtain a uniform reference for comparison.
Fig. 1. Example of aligned X-axis gyroscope reading with maximum negative peak at frame 200 and swing phases. 2.2. Analysis methods
Each subject’s hit accuracy was calculated and tabulated in Table I. A successful hit was defined as a ball strike which sent it into the net. The mean and standard deviation over all successful hit trials were independently calculated per subject for each sensor axis. For each sensor (A=accelerometer, G=gyroscope) axis stream, (S = hand, arm, pelvis or upper back) the sum of the x, y and z components were calculated using the formula;
ܣ௦ൌ ඥܽ௫ଶ ܽ௬ଶܽ௭ଶ (1)
The peaks of the AS and GS graphs were extracted for each trial e.g. arm sensor data. In addition, the corresponding coefficient of variation (CV) was also computed as the ratio of the mean and standard
deviation of these peaks. Scatter plots of were constructed as in Figure 3 for each type were plotted for each successful trial
Fig. 2. (a) Arm acceleration in the x-axis for 3 trials
Fig. 3. Scatter plot of average AARM versus CV fo
subjects with high swing peak accelerations and bet
Fig. 4. Graph depicts sample inter-trial peak accele peak accelerations (105-115 m/s2) than subjects
B1-f average peak AS and GS against CV values B1-for all h sensor position. Finally the peak values of a partic l hit for all subjects. Hand acceleration data is shown i
s of a skilled golfer (P1); (b) The same data for 3 trials of a beginner
or all 10 subjects. Note that this plot clearly depicts low handicap tter consistencies throughout trials compared to the beginner group.
eration for the hand for 6 subjects. Note subjects P1-P3 have consi -B3. The standard deviations of peak accelerations are larger howev
10 subjects ular sensor n Figure 4. r golfer (B3). p (pro-golfers) istently higher ver for B1-B3.
3. Experimental Results
The hit rate was 100% for all skilled golfers P1-P4 and 65−85% for the beginner golfer group of B1-B6 (Table I). The accuracy of ball placement was consistently higher in subject P1-P4. They could hit the ball to the middle of the net at least 90% of time. However, subjects B1-B6 drove the ball to the lower and upper right and left corners of the net more often than the center, managing to find the center only in 40-85% of their successful swings.
It was found that the standard deviation of sensor data was relatively lower in the skilled golfer group compared to the beginner golfers. For example, the standard deviation of the hand acceleration in all three axes ranged between 0.59-3.49 ms-2 for P1-P4, and 1.88-9.50 ms-2 for B1-B6. Subject B5 had exceptionally low standard deviation of 1.88 ms-2 while the rest had larger peak deviations (> 5.85 ms-2). The skilled golfer group had significantly larger peak accelerations AS for the (105-114 ms-2) hand,
(58-94 ms-2) arm and (28-78 ms-2) trunk segments compared to the beginner group i.e. (55-99 ms-2) hand, (21-58 ms-2) arm and (13-27 ms-2) trunk. Figure 2 shows the significant changes of the arm acceleration in x-axis between a beginner (B3) and a skilled golfer (P1). However there was less of a difference in pelvis motion, with the pros achieving 16-28 ms-2 peak values compared to 12-20 ms-2. Peak total gyroscope readings GS depicted similar trends, with the pro-golfer group producing significantly larger angular
rotations of the hand-arm-trunk segment. Scatter plots of peak sensor values versus their variation (CV) show clear clustering of the groups for hand, arm and trunk segments. Figure 3 depicts the plot for all 10 subjects and the mean points for each group for the arm. It was found that non-golfer subjects closer to the pro-golfer mean had higher hit accuracies with the exception of subject B5 (0.11,20) who was closer to the pro-golfer group but had a poor accuracy of 66.7%. Figure 4 shows the individual successful trial results for a sample of 3 pro-golfers and 3 beginners for data of the hand sensor. In all 10 trials, the pro golfers showed higher peak acceleration compared to the beginner group. Inter trial kinematics were less varied in the pro group i.e. less variation in peak acceleration delivered between trials.
Table 1. Hit rate of all 10 subjects, skilled golfers (P1-P4) and beginner golfers (B1-B6) to achieve 10 successful ball hits. Subject Hit rate in % Number of Misses
Skilled Golfer P1 100 0 P2 100 0 P3 100 0 P4 100 0 Beginner Golfer B1 78.6 3 B2 84.6 2 B3 84.6 2 B4 64.7 6 B5 78.6 3 B6 68.7 5 4. Discussion
In this paper, we have demonstrated that inertial sensors placed on the hand, arm, trunk and pelvic parts of the body can capture kinematic data during a golf swing. Statistical analysis and peak acceleration and angular rotation analysis could be used to differentiate between skilled and beginner
golfers. Results suggest that skilled golfers produced higher acceleration and rotation in their arm and hand linkages during the swing. This increase in performance appears to be due to higher trunk or upper body rotation speed, since pelvic peak readings were almost similar between the groups. This supported findings on the X factor in which the separation between hip and shoulder rather than absolute ranges was related to swing speed [18]. While the skilled group had better consistencies and higher peak readings, it was still insufficient to conclude that these were contributing factors to better ball hit accuracies. Subject B5 demonstrated almost similar characteristics as the pro group, but achieved a lower hit accuracy (~65%) compared to the other beginners. Future work will investigate swing timing and coordination d to determine if these could be used to predict golf swing kinematics (patterns) that are more responsible for good ball trajectories and accuracies.
5. Conclusion
We have shown that inertial sensor data can be successfully used to differentiate swing patterns between low handicap golf players and beginners. Skilled players had higher peak acceleration and peak angular rotations in the hand and arm, and more consistent swing patterns (highly repeatable) in the pelvis. However these characteristics do not always result in an accurate ball hit i.e. beginners could also consistently swing hard but miss. Future work will look at using these swing patterns as templates for training beginner golfers.
Acknowledgements
This research was supported by Victoria University Researcher Development Grant Scheme, grant RDGS 06/09.
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