Towards a wearable device for skill assessment and skill acquisition of a tennis player during the first serve

Abstract

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=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. © 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 subjective 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 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 microtechnologies 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 measure the upper arm internal rotation 7. The MBVG method works with the help of optical monitoring motion capture systems, 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 rotation 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 placement 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 successful 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.

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 ( and ) and shoulder rotation (). (b) Markers M1 and M2 attached at the sides of the head of the tennis racquet. Centre point C is also shown.

Upper arm internal rotation is one of the main contributors (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 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 algorithm by Ahmadi et al. in 2009 7. The act of upper arm internal rotation is shown in Figure 2.

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.

Wrist flexion is the next main contributor (31 per cent contribution) 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 markers, vector and vector were created, as shown in Figure 1a. The was defined as a vector from RWRB to RFRA, and the 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 contribution) 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).

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 (), vector in a stationary position (), and the projected vector (), projected onto the P plane through angle α and the rotation angle β are shown.

A marker on the RSHO and the left shoulder (LSHO) joints were used to define vector from marker point RSHO to marker point LSHO. Vector is shown in Figure 1. In order to calculate the shoulder rotation, some terms need to be defined as follows: , the vector when an athlete is standing upright without any movement prior to the serve; P, horizontal plane (transverse plane) encompassing the ; and , is projected onto the P plane through angle α.

Due to the normal movement of an athlete during the tennis serve (trunk incline/decline), the vector can make an angle with the horizontal plane. Therefore, it is needed to project the vectors first on a horizontal plane to obtain and then calculate the angle between the projected vectors and the to determine the shoulder rotation angle. In other words, shoulder rotation is angle β subtended between and on the P plane, as shown in Figure 3(b). The shoulder rotation angular velocity can be then calculated by differentiating 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 100 Hz 12.

The dimension of the sensor device is 52 mm long×34 mm wide×12 mm 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 accelerometer 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.

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

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.

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).

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 racquet 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 dependence 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 assessment 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 between 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.

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; ○ participant 2; + participant 3; ⋄ participant 4.

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 flexion as dependant variables, and the racquet head speed as an independent variable, are shown in Figure 6.

Figure 6. Line of improvement as a function of upper arm internal rotation, wrist flexion, and racquet head speed.

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=0.89). The equation of the fit line is:

• (1)

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 internal rotation, wrist flexion, and shoulder rotation) to the racquet head speed. This means that the values of the contributors 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).

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.

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 (r=0.8680, P<0.0001).

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.

Figure 8. Comparison between the gyroscope sensor output (measured) 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.

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.

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 requirements 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.

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. is the distance vector between P1 and P2.

The components of the distance vector 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 provide 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 data, a straight line was fitted to the data (direction vector , R²=0.85). It shows that the line of improvement is valid in the absence of the racquet head speed. Similar to the 3-D case, the line of improvement (direction vector , R²=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 segments 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 simulated 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.

Figure 10. (a) Skill assessment using the simulated gyroscope. (b) Line of improvement created using the simulated gyroscope. participant 1; ○ participant 2; + participant 3; ⋄ participant 4.

In order to determine the wrist flexion, a simulated gyroscope 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 simulated 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 , R²=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 gyroscope sensors are not yet capable of measuring the fast rotational 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 measurements 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 velocity of each main contributor to generate the serve. The peak 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 improvement 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 apparent 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 performance 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.