towards a wearable device for skill assessment and skill acquisition of a tennis player during the...

Research ArticleDOI: 10.1002/jst.112

Towards a wearable device for skill assessment and skillacquisition of a tennis player during the first serveAmin Ahmadi1,2, David Rowlands1 and Daniel Arthur James1,2,�

1 Centre for Wireless Monitoring and Applications, Griffith University, Australia

2Centre of Excellence for Applied Sport Science Research, Queensland Academy of Sport, Australia

In this article, the possibility of using wearable gyroscope sensors for skill

assessment and skill acquisition was investigated. Marker-based methods were

used initially to capture the fast rotational motions and simulate the outputs of

gyroscope sensors. Utilizing the marker-based methods, the angular velocity of

the upper arm internal rotation, wrist flexion, and shoulder rotation were

calculated for a range of athletes using the trajectory of Vicon markers with

respect to the Plug-in Gait model during the first serve in tennis. Participants

from amateur to elite participated in this study. Thirty successful serves from

each participant were assessed. The results showed that the peak values of the

upper arm internal rotation, wrist flexion, and shoulder rotation just before

impact are indicative in classifying the participants’ skill level. It was shown

that all the three parameters, as well as the racquet head speed, increased as

the level of proficiency of the participants increased. A line (R2 5 0.89) was

fitted to the scatter data containing the upper arm internal rotation, wrist

flexion, and racquet head speed. The fit line is a function of upper arm rotation

and wrist flexion. The fit line can be used as a potential skill acquisition tool to

provide feedback on which variables (upper arm internal rotation, wrist

flexion, or shoulder rotation) need to be improved. The positions of three

gyroscope sensors to detect the same trends as those from the marker-based

methods were determined. Therefore, it is envisaged that gyroscope sensors

could be used for skill assessment and skill acquisition for a first tennis serve.

r 2010 John Wiley and Sons Asia Pte Ltd

1. INTRODUCTION

Evaluating the performance of athletes during competition or

even during training sessions has always been a hot topic

among coaches and sports scientists [1]. This is important, as

the correct evaluation feedback could result in enhancing the

performance of athletes. One common and traditional way to

assess the performance is based on the observation of an expert

person, such as a coach. However, there are two disadvantages

associated with this subjective method. First, since it is a sub-

jective method, different coaches could have slightly different

ideas based upon their experience. Second, there are some fast

motions during an action that cannot be captured by human

eyes. Therefore, the need for an objective method rather than

a subjective method was raised.

Videography was used by sports scientists to monitor and

study the biomechanics of various actions, such as the tennis

serve, to provide insight into physical activity levels associated

with performance, as well as the skill-based technique involved

*Griffith School of Engineering, Nathan Campus, Griffith University,

170 Kessels Road, Nathan, QLD 4111, Australia.

E-mail: [email protected]

Keywords:. tennis. serve. assessment. skill. inertial sensor

Sports Technol. 2009, 2, No. 3–4, 129–136 & 2010 John Wiley and Sons Asia Pte Ltd 129

Tennis skill assessment

in the activity [1]. There are some disadvantages associated with

this method. One of the main disadvantages is that it is not

possible to provide real-time feedback to the athletes, and in

particular, tennis players during a training session, as tedious

post-processing is required to extract and analyze the collected

data. This leads to use other technologies to monitor the

athletes during sporting activities. Inertial sensor technology as

one of the growing technologies in the field of sports monitoring

is becoming more popular, as it has some advantages over the

previous method.

Improvements in microelectronics and other microtech-

nologies have made it possible to take advantage of using

miniaturized, light, inexpensive inertial sensors, including

accelerometers and gyroscopes to capture and analyze the

movements of athletes during many sporting activities. For

instance, acceleration sensor technology has been used to

analyze kinetic processes for golfers’ lateral swing [2] and in

swimming [3].Within many sporting applications, the sensors

are now used to measure and classify activity and effort

levels [4–5]. For instance, inertial sensors were employed to

distinguish between amateur and subelite tennis players during

the first serve [6]. It is envisaged that these inertial sensors can

be worn by athletes at all levels to monitor their performance

without hindering it. In spite of all the advantages, inertial

gyroscope sensors are not fast enough to capture fast-rate

rotational motions. Therefore, a method to simulate the

behavior of the gyroscope sensors is required.

Marker-based virtual gyroscopes (MBVG) were developed

to overcome the drawback associated with gyroscopes to mea-

sure the upper arm internal rotation [7]. The MBVG method

works with the help of optical monitoring motion capture sys-

tems, such as Vicon (Vicon Motion Systems Ltd., Oxford, UK).

The trajectory of three reflective markers, which were not in a

straight line in 3-D, were captured and passed to the MBVG

algorithm to measure the rate of rotational motion. The main

purpose of the MBVG is to predict the maximum value, as well

as the trends in rotational velocities when the inertial gyroscope

is not able to measure the action due to high-speed motion.

Also, the MBVG method can save a lot of time by eliminating

the trial-and-error method to find out the best sensor position.

Tennis is recognized as one of the most popular sports

around the world, as it is played at all levels, including socially

and professionally [8]. In order to win the match, or at least get

better results, athletes need to improve their ground strokes, as

well as their serves. According to Bahamonde [9], among

various strokes in tennis, the tennis serve is the most important

and critical stroke. It is also known that a fast serve can

dominant the game at the elite level [9–10]. Therefore, for a

tennis player to be more successful during the match, he/she

needs to master the serve action. In order to master the tennis

serve, it is important to recognize the main contributors to

produce the fast serve. According to Marshall and Elliott [11],

internal upper arm rotation, wrist flexion, and shoulder rota-

tion play critical roles in generatin the fast first serve from the

maximum knee flexion to hit the ball.

The aim of this article is show that gyroscope sensors as

wearable devices can possibly be used for skill assessment and

acquisition during the tennis serve motion once the fast-enough

gyroscopes are developed. In order to show this, the behavior of

gyroscopes was simulated using the marker-based methods. By

using the Vicon standard Plug-in Gait marker placement (Vicon

Motion Systems Ltd., Oxford, UK), four male tennis players

during the first tennis serve were assessed. One amateur, two

subelites, and one elite player were studied. In order to determine

the upper arm internal rotation, the MBVG method was used.

Other marker-based methods were used to determine the wrist

flexion and shoulder rotation. The upper arm internal rotation,

wrist flexion, and shoulder rotation velocity were measured,

since they were reported as the main contributors for the tennis

serve after the maximum knee extension [11]. Also, the output

from the developed methods were compared and found to be

closely matched with those from the gyroscope sensors.

2. METHODS

2.1 Marker-Based Technology

Four right-handed, male tennis players, including one

amateur, two subelites, and one elite tennis player were studied

in this experiment. The Vicon motion-capturing system using

the standard Plug-in Gait model was used, and optical markers

were attached on the upper body of each participant. The place-

ment of markers, with respect to the standard Plug-in Gait

model, is shown in Figure 1a. Eight cameras were used to record

the data at 100 frames per second. The participants were to

serve at a target region. If the serve was not inside the region, it

would not count as a successful serve. This corresponded to the

area needed to serve the ball into the service box. Thirty success-

ful first serves were collected from each player for analysis. All

the players used the same tennis racquet during the experiment.

Some marker-based algorithms were developed to calculate the

upper arm internal rotation, wrist flexion, and shoulder rotation

during the first tennis serve action.

Upper arm internal rotation is one of the main con-

tributors (54 per cent contribution) [11] to the forward speed of

the racquet at impact during a first tennis serve [11]. Three

markers on the right upper arm, including the right shoulder

(RSHO) marker, right elbow marker, and the right upper arm

Figure 1. (a) Marker placement with respect to the Vicon Plug-In Gait

model, as well as the required vectors, to determine the wrist flexion

(V1�!

and V2�!

) and shoulder rotation (U!

). (b) Markers M1 and M2

attached at the sides of the head of the tennis racquet. Centre point C

is also shown.

www.sportstechjournal.com & 2010 John Wiley and Sons Asia Pte Ltd Sports Technol. 2009, 2, No. 3–4, 129–136130

Research Article A. Ahmadi, D. Rowlands and D. A. James

marker, were used to measure the angular rotation and thus

the angular velocity of the upper arm using the MBVG

method. The calculation was based upon the developed algo-

rithm by Ahmadi et al. in 2009 [7]. The act of upper arm

internal rotation is shown in Figure 2.

Wrist flexion is the next main contributor (31 per cent con-

tribution) [11] to the forward speed of the racquet at impact.

Wrist flexion is the bending action of the wrist joint, as shown in

Figure 2. In order to determine the wrist flexion, three markers

were used: one on the forearm (RFRA), one on the wrist

(RWRB), and one on the hand (RFIN). Using the three mar-

kers, vector ~V1 and vector ~V2 were created, as shown in Figure

1a. The ~V1 was defined as a vector from RWRB to RFRA, and

the ~V2 was defined as a vector from RWRB to RFIN. The angle

between the two vectors was calculated and then differentiated

over time to obtain the wrist flexion angular velocity.

Forward shoulder rotation (positive rotation about the

medial axis) is another main contributor (10 per cent contri-

bution) [11] to the forward speed of a tennis racquet at impact.

In this article, instead of forward shoulder rotation, the term

‘shoulder rotation’ will be used. Shoulder rotation motion is

shown in Figure 3(a).

A marker on the RSHO and the left shoulder (LSHO)

joints were used to define vector ~U from marker point RSHO

to marker point LSHO. Vector ~U is shown in Figure 1.

In order to calculate the shoulder rotation, some terms need to

be defined as follows: ~Us, the ~U vector when an athlete is

standing upright without any movement prior to the serve; P,

horizontal plane (transverse plane) encompassing the ~U; and~U, is projected ~U onto the P plane through angle a.

Due to the normal movement of an athlete during the

tennis serve (trunk incline/decline), the ~U vector can make an

angle with the horizontal plane. Therefore, it is needed to

project the ~U vectors first on a horizontal plane to obtain ~UP

and then calculate the angle between the projected vectors ~UP

and the ~Us to determine the shoulder rotation angle. In other

words, shoulder rotation is angle b subtended between ~UP

and ~US on the P plane, as shown in Figure 3(b). The shoulder

rotation angular velocity can be then calculated by differ-

entiating the calculated shoulder rotation angle over time.

In order to calculate the forward racquet head speed, two

markers, M1 and M2, were attached on the sides of the head of

the tennis racquet in a way that the median point of the two

markers could define a point C as the centre of the head of the

racquet. The tennis racquet, the attached markers, and the

calculated centre point C are shown in Figure 1(b). It should be

noted that throughout this article, the term ‘racquet head speed’

is used instead of ‘forward racquet head speed’. The horizontal

component of the centre point of the racquet head (forward

motion) was extracted to calculate the linear forward racquet

head speed. The linear velocity of the extracted centre point was

calculated by differentiating the position of point C over time.

2.2 Inertial Sensor Technology

Three inertial sensor-based devices [12] were used in this

study. Each sensor-based device contained one 1D ADXRS300

gyroscope sensor (Brisbane, Queensland, Australia) and was

sampled at 100Hz [12].

The dimension of the sensor device is 52mm long� 34mm

wide� 12mm high, weighs approximately 22 g, and is small and

light enough to be mounted on different segments of an athlete.

It is a microcontroller-based platform contacting a tri-axial ac-

celerometer to measure acceleration, a 1-D gyroscope to measure

angular velocity, on-board memory to record the sessions, radio

frequency (RF) link to control the unit from distance, LCD

screen to interact with the device, USB port to download the

collected sessions and charge the device, and five-way push

buttons to turn the device on and off and control data recording.

The technical details of the device are summarized in Table 1.

Figure 2. Upper arm internal rotation and wrist flexion (with 54 per

cent and 31 per cent contribution during the forward swing of the

serve, respectively) [11].

Figure 3. (a) Shoulder rotation in the transverse plane about the medial

axis (10 per cent contribution during the forward swing of the serve) and

the direction of rotation are shown.(b) Horizontal plane (transverse plane)

(P), vector from right shoulder to left shoulder (U!

), vector in a stationary

position (U!

s), and the projected vector (U!

P), projected U!

onto the P

plane through angle a and the rotation angle b are shown.

Table 1. Technical specification for the inertial sensor device [12].

Components Description

Processor Atmel ATMEGA 128

Sensors Kionix KXM52-1050,3axis 2G accelerometer

ADXRS300 gyroscope

Radio Nordic NRF2401, 2.4 GHz radio with internal

patch antenna

Memory 128-MB flash memory

Inputs/outputs LCD screen, USB port, and a push button

Sports Technol. 2009, 2, No. 3–4, 129–136 & 2010 John Wiley and Sons Asia Pte Ltd www.sportstechjournal.com 131


The placement of the three gyroscope sensors to determine

the upper arm rotation, shoulder rotation, and wrist flexion is

shown in Figure 4. Gyroscope sensors were light enough to be

mounted on the body using double-sided tape. Gyroscope A,

which was mounted on the chest, determined the shoulder

rotation; gyroscope B, which was mounted on the upper arm,

determined the upper arm internal rotation; and gyroscope C,

which was mounted on the hand, determined the wrist flexion.

The correlation between the maximum peak of upper arm

internal rotation, wrist flexion, shoulder rotation, racquet head

speed, and skill level is presented in the Results and Discussion

sections. In addition, the output comparison between the

marker-based methods and gyroscope sensors is shown.

The following Results and Discussion sections are divided

into five topics: skill assessment, skill acquisition, removing rac-

quet head speed dependence, gyroscope sensor placement, and

simulated gyroscope. The focus of the skill assessment section is

to show how athletes can be assessed with respect to the peak

values of their main contributors during the first serve prior to

impact. The focus of the skill acquisition section is to show how

to apply the obtained results from the skill assessment section to

provide possible feedback to the athlete so that they are able to

compare their serves with an elite’s serves to try to improve their

swings. The focus of the removing racquet head speed depen-

dence is to show that skill assessment and skill acquisition can be

done in the absence of racquet head speed. The focus of the

gyroscope sensor placement section is to show that there is a close

relationship between the output of the sensors on the chest and

on the hand and those from the marker-based methods. Finally,

the focus of the simulated gyroscope section is to show that

simulated gyroscope sensors are capable of measuring skill as-

sessment and skill acquisition for high-speed serves.

3. RESULTS

3.1 Skill Assessment

The angular velocity of the upper arm internal rotation,

wrist flexion, and shoulder rotation was calculated for the 31st

serves for each athlete. Figure 5 shows the relationship bet-

ween the peak values of the main contributors containing the

upper arm internal rotation (54 per cent contribution), wrist

flexion (31 per cent contribution), and shoulder rotation

(10 per cent contribution), with respect to the racquet head

speed during the first tennis serve for all the participants.

Participant 1 was an amateur player, participants 2 and 3

were subelite players, and participant 4 was an elite player.

3.2 Skill Acquisition

In this section, a possible method for skill improvement is

shown. Scatter plots for the upper arm rotation and the wrist

Figure 4. Placement of gyroscope sensors on the chest to measure

shoulder rotation (gyroscope A), the upper arm to measure upper arm

internal rotation (gyroscope B), and the hand to measure the wrist

flexion (gyroscope C).

Figure 5. Peak of the angular velocity of (a) the upper arm internal

rotation, (b) the wrist flexion and (c) the shoulder rotation versus

racquet head speed. Participant 1 is an amateur, participants 2 and 3

were subelite, and participant 4 was an elite tennis player.

participant 1; J participant 2; + participant 3; } participant 4.



flexion as dependant variables, and the racquet head speed as

an independent variable, are shown in Figure 6.

Upper arm internal rotation and wrist flexion were chosen

as they have more contribution effects and importance to the

maximum racquet head speed after the maximum knee flexion

in the first tennis serve. As shown in Figure 6, there is well-

separated clustering for different skill levels. According to

the shape of the scatter data, a straight line can be fitted to the

data. The least-squared fit technique was applied to create

the fit line (R2 5 0.89). The equation of the fit line is:

x� 18:19

30:90¼

y� 5:45

19:04¼

z� 20:94

9:65¼ t ð1Þ

3.3 Removing Racquet Head Speed Dependence

It has already been shown in Figure 5 that there is a linear

relationship between each main contributor (upper arm in-

ternal rotation, wrist flexion, and shoulder rotation) to the

racquet head speed. This means that the values of the con-

tributors were increasing as the racquet head speed increased.

Therefore, it is possible to remove the racquet head speed and

define the line of improvement in 2-D by only using the upper

arm data and wrist data instead of the 3-D case as shown in

Figure 7(a). It is also possible to remove the racquet head

speed and define the line of improvement in 3-D by using the

upper arm, wrist, and shoulder data as shown in Figure 7(b).

3.4 Gyroscope Sensor Placement

The aim of this section is to show that gyroscope sensors

could be used as wearable devices to determine the peak of the

upper arm internal rotation, wrist flexion, and shoulder rotation

during the forward motion of the tennis serve and thus, they can

be used as a potential skill assessment and acquisition tool. Due

to the limitation of gyroscope sensors to detect the fast rate of

rotational motions, slow motion serves rather than a normal

power first serve were performed. The biomechanic movement

of the slow motion serve was observed to be similar to that of

the normal speed serve, except that ball was hit with less power.

Pearson’s correlation coefficient (r) and significant difference

test results (P) were used to quantify the relationship between

the slow motion serve and the normal speed serve action. The

correlation between the slow motion serve and the normal speed

serve was found to have similar trends (r5 0.8680, Po0.0001).

A previous study [7] has shown that a gyroscope can

follow the trends of a slow motion serve. Figure 8(a,b) shows

that a gyroscope can follow the trends of the wrist flexion and

the shoulder rotation for a slow motion serve. This shows that

gyroscopes are capable of following the trends of a serve.

It was shown that gyroscopes can capture the components

of the movements for a tennis serve. Further in the text,

simulated gyroscopes are developed and used for classifying

athletes during a high-speed first serve in tennis.

4. DISCUSSION

4.1 Skill Assessment

In Figure 5(a–c), clear bands can be seen between the main

contributors and the racquet head speed. A relationship between

each main contributor and the racquet head speed can be seen for

each banding. The band shows that the racquet head speed is

increased for increasing skill level as expected from the literature

[11]. It can be seen that the upper arm internal rotation, wrist

flexion, and shoulder rotation are increasing for increasing skill

level. For instance, participant 4 (elite player) has higher peak

values than participant 1 (amateur player), and the peak values

from participants 2 and 3 are higher than those of participant 1

and lower than those of participant 4.

Figure 6. Line of improvement as a function of upper arm internal

rotation, wrist flexion, and racquet head speed.

Figure 7. Use of a line of improvement in the absence of racquet head

speed by (a) using upper arm and wrist data and (b) upper arm, wrist,

and shoulder data.



Also, distinct clustering can be seen between the different

skill levels. In Figure 5, it can be seen that the lower cluster

belongs to an amateur player, the middle cluster relates to the

subelite players, and the top cluster corresponds to the elite

player. This is also expected due to the fact that elite players

generate more racquet head speed, which means that upper

arm internal rotation, wrist flexion, and shoulder rotation are

also increased, since they contribute approximately 85 per cent

to the racquet head speed at impact. Therefore, According to

Figure 5a–c, athletes can be assessed and classified with respect

to the peak of the upper arm internal rotation, wrist flexion,

and shoulder rotation respectively prior to impact.

4.2 Skill Acquisition

It can be seen in Figure 6 that higher values on the line

correspond to more skilful athletes. For instance, low racquet

head speed, upper arm, and wrist values belong to amateur

player right at the bottom of the line. Those values are growing

for subelite players and the highest values correspond to the elite

players. The line shows a progression from amateur to subelite to

elite, so it is possible to think of this line as a line of improvement.

It indicates that the higher one is on the line, the closer to the

professional serve. This line is not the line of ‘best technique’, but

indicates a traversal path that can be followed to improve from

amateur to elite. It should be noted that this line is generated

from the available population of athletes and would benefit from

a greater number of players and serves. However, the line is still

an indicator of skill acquisition and skill improvement.

All athletes have different needs, so the different require-

ments from athletes in different levels dictate the way the line is

used. It is up to the coaches and sport scientists to interpret

the results and provide the relevant feedback to a player. An

example method of using the line of improvement for the 3-D

case is as follows. Data point P1 consists of the racquet head

speed, wrist flexion, and upper arm internal rotation collected

from a player during the first serve in tennis. The collected data

point (P1) can then be mapped onto the line of improvement to

give point P2, as shown in Figure 9. P2 is obtained in such a way

that both points (P1 and P2) have the same racquet head speed.

The components of the distance vector ~d between P1 and P2

can identify the amount of upper arm rotation and wrist flexion

improvement needed to approach the line of improvement.

Once the athlete has approached the line, it is possible to climb

up the line to obtain a higher skill level. The ability to traverse

the line may be limited based upon the physiology of the player,

which may prevent him/her traversing further.

As will be discussed in the next sections, the ultimate aim is

to use the gyroscope sensors and measure the players and pro-

vide real-time feedback on the field during a training session.

However, using the gyroscope sensors, the racquet head speed

cannot be easily determined. Therefore, it is needed to show that

skill assessment, as well as the skill acquisition, are still feasible in

the absence of the racquet head speed. In the following section,

it is shown that it is possible to remove the dependence of the

racquet head speed and still see banding and separate clustering.

4.3 Removing Racquet Head Speed Dependence

Figure 7(a) shows the relationship between the upper arm

and wrist data and the skill level. According to the shape of the

Figure 8. Comparison between the gyroscope sensor output (mea-

sured) and the marker-based developed methods (calculated) for (a)

shoulder rotation angular velocity (b) and wrist flexion angular velocity

during the slow motion tennis serve. measured; calculated.

Figure 9. Suggested method to use the line of improvement is shown.

P1 is a new collected data point and P2 is the mapped version of P1 in

a way that both P1 and P2 have the same racquet head speed. d!

is

the distance vector between P1 and P2.



data, a straight line was fitted to the data (direction vector~Vd1 ¼ 10; 16:5h i, R2 5 0.85). It shows that the line of im-

provement is valid in the absence of the racquet head speed.

Similar to the 3-D case, the line of improvement (direction

vector ~Vd2 ¼ 37:9; 9:05; 11:44h i, R2 5 0.87) can be defined in 4-

D by including the shoulder rotation as another dependant

variable and can be reduced to the 3-D case by removing the

racquet head speed as shown in Figure 7(b). Figure 7(b) shows

that the line of improvement can be defined by using the

data of the three main contributors (upper arm rotation,

wrist flexion, and shoulder rotation) in the absence of the

racquet head speed. The linear relationship between the

three main contributors and the skill level is clearly shown

in Figure 7(b). Therefore, skill assessment and acquisition

can be done without any knowledge of the racquet head

speed values.

4.4 Simulated Gyroscope

The aim of this section is to show that simulated gyroscope

sensors are capable of measuring skill assessment and skill

acquisition for high-speed serves. In the previous sections, the

marker-based methods were used to calculate the upper arm

internal rotation, wrist flexion, and shoulder rotation only,

and athletes were assessed based upon the peak values of the

angular velocities of those three elements. However, gyroscope

sensors show more complex rotations due to linkage of seg-

ments of the body. Therefore, simulated gyroscopes based

upon marker positions were developed to simulate the output

behavior of the gyroscope sensors. In this section, the effect of

segment linkage on the upper arm internal rotation, wrist

flexion, and shoulder rotation during the forward motion of

the tennis serve is discussed.

A previous study [7] indicated that the output of a simu-

lated gyroscope sensor on the upper arm is influenced by the

upper arm internal rotation, as well as the shoulder rotation.

Therefore, simulated gyroscope for the upper arm will contain

the summation of the calculated upper arm internal rotation

and the calculated wrist flexion only. In Figure 10(a), the peak

values for the shoulder rotation plus upper arm internal

rotation versus racquet head speed is shown during a high-

speed tennis serve. A clear banding can also be seen, as well as

different clustering for different skill levels. This indicates that

a gyroscope sensor on the upper arm can allow skill level to be

distinguished.

In order to determine the wrist flexion, a simulated gyro-

scope needs to be placed on the hand, as shown in Figure 4. As

can be seen in Figure 8(b), the measured sensor output and the

calculated wrist flexion angular velocity only are very close.

Thus, a simulated gyroscope for wrist flexion will consist of the

calculated wrist flexion only.

In order to determine the shoulder rotation, a simulated

gyroscope needs to be placed on the chest, as shown in Figure 4.

It was found that the gyroscope sensor on the chest is not

greatly influenced by any segment linkages of the body during

the service action, as can be seen in Figure 8(a). Therefore, the

calculated shoulder rotation can be used for the simulated

gyroscope as it can follow the trends.

To determine if skill assessment can be seen using simu-

lated gyroscopes, the racquet head speed versus simulated

gyroscopes were plotted, as seen in Figure 10. In Figure 10(a),

well-separated clusters are apparent for different skill levels.

In Figure 10(b), different clusters for different skill levels are

shown. It can be seen that a line of improvement (direction

vector ~Vd3 ¼ 21:75; 5:5; 20:94h i, R2 5 0.87) can be fitted to the

clusters as a potential tool for skill assessment.

Since the simulated gyroscope sensors can be used to

model a high-speed serve, it can be suggested that the sensor

technology as a wearable technology can be used to assess the

performance of athletes and to provide the required feedback

to the athletes on the field during a training session. However,

due to the technology limitations, the currently-available gy-

roscope sensors are not yet capable of measuring the fast ro-

tational motion. As a result, as soon as technology is advanced

enough to develop high-range gyroscopes, it is feasible to

employ them as a training device on the tennis court.

5. CONCLUSION

In this study, athletes were assessed according to the mea-

surements of the main contributors between the point of the

maximum knee flexion and the point of impact during the first

serve in tennis. The trajectory of marker positions on the upper

body with respect to the Vicon Plug-in Gait model was used to

develop the marker-based methods to calculate the angular ve-

locity of each main contributor to generate the serve. The peak

Figure 10. (a) Skill assessment using the simulated gyroscope.

(b) Line of improvement created using the simulated gyroscope.

participant 1; J participant 2; + participant 3; } participant 4.



values of the upper arm internal rotation, wrist flexion, and

shoulder rotation angular velocities were calculated for athletes

with different skill levels and plotted against racquet head speed.

Clear banding and well-separated clustering were shown for

different skill levels. Due to the fact that skill acquisition was an

important aspect of this study, the line of improvement was

developed. The line was the best fit through the clusters obtained

from the available population and indicated the path of im-

provement from amateur players to elite player. The distance

vector between any new collected data and the mapped data on

the line contains required vector components information on

how to fix the deficiency. It was also shown that due to the linear

relationship between the racquet head speed and all the three

main contributors (upper arm internal rotation, wrist flexion,

and shoulder rotation), skill assessment/acquisition can be ap-

parent without the use of the racquet head speed.

It was also shown that there is a reasonably close match

between the calculated results and the measured results using

the inertial gyroscope sensors during the slow motion serve in

tennis. It was found that all the required angular velocities for

the skill assessment and skill acquisition can be obtained using

three gyroscope sensors mounted on the upper arm, the chest,

and the hand. Therefore, it will be possible to measure the

athletes on the field and provide them with time feedback when

the fast gyroscopes are developed.

In this study, three gyroscopes were suggested to measure

performance, and thus classify the athletes according to the

peak of the main contributors during the first tennis serve. In

future, further studies will be required to minimize the number

of sensors and ideally use only one sensor to capture the whole

swing.

Overall, this article suggests a method to examine the use

of gyroscope sensors as a wearable device to assess the per-

formance of tennis players during the first serve. This helps

athletes with different skill levels to be monitored and assessed

in the real environment (tennis court) instead of laboratories,

and obtains real-time or close to real-time feedback on the field

during training sessions. Also, since the gyroscope sensor

technology is cheap compared to the other technologies, it

makes it possible that a wide range of tennis players could

benefit from using the sensor technology.

REFERENCES

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8. Ahmadi A, Rowlands D, James DA. Technology to monitor andenhance the performance of a tennis player. In Pope N, Kuhn K, Forster J(Eds), Digital Sport for Performance Enhancement and Competitive Evolution:Intelligent Gaming Technology 2009; pp 101–121. USA: IGI Global.

9. Bahamonde R. Changes in angular momentum during the tennis serve.Journal of Sports Sciences 2000; 18: 579–592.

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Received 11 July 2009

Revised 9 October 2009

Accepted 12 October 2009

Published online 7 February 2010



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