A look at what goes into everyday practice and competition for a Temple program that has been around for 43 years.
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The touch of a blade sends light from an electronic scoring machine often placed at the side of a strip.
The specific color of light set off depends on which part of the body gets hit.
In the sport of fencing, red and green lights indicate that a fencer has scored valid touches, while a white beam shows that a fencer has hit an off-target area for both sabre and foil fencers.
Yet, it didn&#;t always used to be this way, as the NCAA gradually incorporated the electronic scoring system from its inception in the s until the s, when the system was first universally accepted for all weapon types in collegiate fencing.
In a room with shining wood flooring in Pearson-McGonigle halls, Temple&#;s fencers use this system on a daily basis. The sport has changed in many ways since coach Nikki Franke oversaw the team&#;s first season in , but the equipment her program uses today shares many similarities to what has been used for much of the past four decades.
And as Temple fencers can attest to, there&#;s a lot more to the sport than a mask and a sword.
The Connections
The way in which the hits are registered is dependent on the weapon that is used. But, across the board, the setup is always the same, as Temple volunteer assistant coach Josh Herring said each strip is equipped with two reels, two floor cables and a scoring box.
Each fencer on the strip has a body cord that goes through lamés, or an electronic conductive jacket worn by foil and saber fencers that connects to the weapon and runs to the tip of the blade. While the opposite end of the wire is connected to the scoring machine, the reel, which is fastened to the wires, dictates how fast the fencer can move up and down the strip.
So, when the tip of the weapon touches the target area, the electrical waves are transmitted through the body cord to the machine, which sets off the lights, signaling a point for the fencer.
The Weapons
Foil, epee and sabre.
Each of the three possible weapons a fencer can use is constructed differently, originally designed for specific tasks. Foil weapons are the lightest of the three and have a small bell guard used to secure the hand. The epee weapon is heavier than the foil weapon, featuring a larger bell guard that protects the hand, which is considered a target area.
Herring said foil and epee weapons are considered thrusting and stabbing weapons, while the sabre weapons are considered a cutting and slashing weapon.
&#;Foil and epee came from a traditional musketeer fighting &#; in all the old movies where someone gets stuck in the chest and kills them instantly,&#; Herring said. &#;Sabre comes from tabular, where you are generally on a horse and you are riding, so there is a lot more cutting and slashing. So it&#;s kind of where the brutal weapons came from.&#;
As foil and epee fencers score their points by thrusting their weapons, there is usually a tip at the end of their blade, which helps to record them.
Junior foilist Demi Antipas said there is a required amount of pressure that needs to be applied to the tip before touches can translate to points.
&#;In foil, it takes 500 grams of pressure to depress the tip,&#; Antipas said. &#;It&#;s very specific. For epee, it&#;s 700 grams. It&#;s harder to push down. So when that pushes down on the opposite person&#;s vest, [it] scores a point.&#;
The Uniforms
With three different weapons come three different uniforms.
Although all fencers wear the same base layers, the uniforms are made according to a fencer&#;s target area. An epeeist wears all white because their entire body is a target area, while the area is more limited for foil and sabre fencers.
&#;You have your knickers, which are the pants, and then you have a chest protector for the girls,&#; Antipas said. &#;What we call a plastron, it&#;s like a half-sleeve &#; because there is a lot of nerve-endings underneath your arms. It&#;s just an extra piece of fabric so you don&#;t get nerve damages from getting hit, and then you wear a white cotton jacket.&#;
Foil and sabre fencers also wear lamés &#; an electrionically-conductive jacket that help record their points. The target area of a foilist is the torso. Yet, sabre fencers wear a jacket that indicates additional target areas such as the arms, hands and head. Alongside the lamés, sabre fencers have to wear an electronic conductive mask to record points.
Dry Fencing
Before electronic scoring became popular within the sport, fencers took part in what is now called dry fencing.
Similar to the sport of Taekwondo, Herring recalls a time when fencing required a minimum of four judges and a maximum of eight.
&#;Instead of a round button that looked like a button [at the tip of the weapon], they used to have cones and little domes that had teeth on them,&#; Herring said. &#;The idea was you were supposed to stick it into your target and let it stay there for a second so that they could see it.&#;
Herring said fencers would also dip the tip of their weapons into powder or colored chalk so that when the fencers connected with the target area, it would be noticeable on the all-white uniform.
Junior sabreist Petra Khan said when she first started the sport, she competed in dry fencing for a time, but after she started to advance in the sport, she started wearing her lamé.
&#;It wasn&#;t too hard of an adjustment,&#; Khan said of the transition to electronic fencing. &#;But what was great about it was that you can hit, and see the light when you hit. You didn&#;t have to rely on the other person, saying, &#;Oh, you hit me or you didn&#;t hit me.&#; It was a nice adjustment.&#;
Wireless Fencing
Collegiate fencing remains a sport far different from that at the Olympic level.
&#;There [are] wireless fencing reels,&#; Herring said. &#;If you look at the Olympics, a lot of times you see them carrying a pack on their backs, a little battery-pack looking thing, that is the entire machine more or less contained on their body.&#;
When fencers scores a valid touch during the Olympics, the entire strip lights up. But, Herrings said it may take some time before the NCAA incorporates wireless fencing into its own level of competition.
Herring fears cheating and cost will challenge the NCAA to make the change in the near future.
Nevertheless, Herring said the sport has improved.
&#;It has evolved greatly,&#; he said.
Danielle Nelson can be reached at or on Twitter @Dan_Nels.
This paper is an extended version of our conference paper: Nita, V.A.; Magyar, P. Smart IoT device for measuring body angular velocity and centralized assessing of balance and control in fencing. In Proceedings of the International Symposium on Signals, Circuits and Systems (ISSCS), Iasi, Romania, 13&#;14 July ; pp. 1&#;4. https://doi.org/10./ISSCS..
Fencing, a sport emphasizing the equilibrium and movement control of participants, forms the focal point of inquiry in the current study. The research endeavors to assess the efficacy of a novel system designed for real-time monitoring of fencers&#; balance and movement control, augmented by modules incorporating visual feedback and haptic feedback, to ascertain its potential for performance enhancement. Over a span of five weeks, three distinct groups, each comprising ten fencers, underwent specific training: a control group, a cohort utilizing the system with a visual real-time feedback module, and a cohort using the system with a haptic real-time feedback module. Positive outcomes were observed across all three groups, a typical occurrence following a 5-week training regimen. However, noteworthy advancements were particularly discerned in the second group, reaching approximately 15%. In contrast, the improvements in the remaining two groups were below 5%. Statistical analyses employing the Wilcoxon signed-rank test for repeated measures were applied to assess the significance of the results. Significance was solely ascertained for the second group, underscoring the efficacy of the system integrated with visual real-time feedback in yielding statistically noteworthy performance enhancements.
This current study examines an expanded iteration of the system introduced in prior work [ 28 ], aiming to enhance its functionalities beyond measuring and monitoring balance and movement control. The extended system is designed to actively improve fencers&#; capacities by incorporating real-time haptic and visual feedback mechanisms. The experimental design involves three distinct groups of fencers: a control group comprising 10 participants who will undergo training without the utilization of the proposed system, another group of 10 fencers who will engage with the system incorporating visual feedback, and a third group of 10 fencers who will interact with the system comprising haptic feedback. This intervention will span five weeks.
The angular velocity of the torso can be monitored in time using a gyroscope worn on the back of the fencer.
The sole permissible rotation, albeit limited in magnitude, occurs along the Y-axis. This is due to the unique leg movement involved in fencing: when moving forward, the front leg is raised and advanced, followed by the back leg, resulting in a slight rearward tilt. Conversely, the back foot is repositioned before the front leg, causing a subtle forward tilt when moving backward. Because of this specific footwork, a fencer&#;s torso should ideally maintain a nearly constant zero angular velocity around the X and Z axes. Moreover, if the movement is executed with proper balance control, the angular velocity around the Y-axis should be kept to a minimum. A professional fencer&#;s movement should closely resemble a train on tracks, with smooth back-and-forth motion and minimal tilting, ideally exhibiting angular velocities of 0 along all three axes (X, Y, and Z).
illustrates a simplified representation of the motion dynamics in a fencing game. As depicted, the primary motion predominantly occurs along the X-axis. In this context, it is crucial to note that the torso&#;s angular velocity along the Z-axis is generally close to zero, with exceptions occurring during specific actions like counterattacks and close encounters. Additionally, any rotation around the X-axis by the fencer leads to undesirable side imbalance and is a behavior that should be avoided in all circumstances.
Current coaching practices rely heavily on visual assessment by experienced coaches, which is particularly challenging when dealing with a large cohort of athletes&#;perhaps 100 fencers in training and 10 to 20 in competition. Unfortunately, the traditional approach necessitates a sequential evaluation, limiting the feasibility of continuous assessment. In addressing this gap, a pioneering Internet of Things (IoT) system has been developed and successfully tested for automated real-time measurement of balance and movement control [ 28 ]. This innovative system empowers coaches to conduct comprehensive and instantaneous evaluations of balance and movement control for all their fencers, presenting a transformative solution to the existing challenges in the field. In his book, This is Fencing!, Ziemowit Wojciechowski (one of the world&#;s most renowned and sought-after foil coaches with a long and illustrious record of success) speaks about the importance of performance analysis, which can be &#;qualitatively based on observations or quantitatively based on factual or statistical data&#; [ 29 ].
Fencing, characterized by its occurrence on a specialized surface measuring 14 m in length and 1.5 m in width, akin to a chessboard for two participants, places significance on the positional dynamics between adversaries. Irrespective of the opponent&#;s physical attributes, celerity, or strength, effective counteraction hinges upon the judicious manipulation of distance and positioning. To optimize such elements, fencers must cultivate speed, agility, force, endurance, and an acute sense of balance and movement control through specific training. While extensive literature exists for the enhancement of speed and agility in general [ 21 , 22 , 23 , 24 ] and within the domain of fencing [ 25 ], the assessment and improvement of hand control [ 26 , 27 ], there is a conspicuous absence of tools or devices for measuring balance and movement control. Usually, IMUs are used for assessing injury risks, as in [ 9 , 10 , 11 , 12 ] and [ 14 ], while the proposed system is used to evaluate balance and movement control performance. It is essential to mention that the proposed system considers the particular limitations of the movements made in fencing.
The sports landscape is transforming through technology integration, particularly with the increasing prevalence of wearable devices utilizing inertial measurement units (IMUs). The extant body of research substantiates the efficacy of these devices in enhancing the training experience [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 ]. Traditionally positioned on the hip [ 9 ], alternative placements such as the wrist [ 10 , 11 ], thigh [ 12 ], knee [ 13 ], or even the back [ 14 ] are viable options contingent upon the specific demands of a given sport. Notably, across a spectrum of sports like tennis [ 15 ], football [ 16 ], basketball [ 17 ], handball [ 18 ], hockey [ 19 ], and martial arts [ 20 ], insufficient attention has been directed towards fencing in the existing research literature.
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delineates the primary components integrated into the proposed system. At the core of this system is the &#;Gyroscope sensor&#;, a pivotal element employed for real-time monitoring of fencers&#; balance. This is achieved by analyzing angular velocity along the fencer&#;s torso&#;s X, Y, and Z axes. The &#;Balance and movement control monitor&#; is an Android application used by fencers and coaches for comprehensive performance tracking over temporal intervals. The &#;Haptic feedback&#; module is embodied by a specialized smartwatch designed explicitly for fencing [30]. This device emits vibrations if the gyroscope sensor detects imbalances surpassing a predefined threshold. This threshold is adjustable per the fencers&#; anticipated performance levels, categorized based on their proficiency levels&#;ranging from beginner to professional. Concurrently, the &#;Visual feedback&#; mechanism manifests as a device featuring a colored LED signaling system. The LED emits a light green hue when the fencer&#;s movements align with acceptable balance standards corresponding to their skill levels. Conversely, an orange signal is activated if the fencer&#;s performance falls below the designated reference level, calibrated in accordance with their experience.
The &#;Audio feedback&#; component, still in the developmental phase, incorporates the utilization of headphones. It is designed to emit a distinct auditory cue when fencers exhibit suboptimal balance and movement control levels. It is imperative to note that this article focuses on the analysis of the &#;Visual feedback&#; and &#;Haptic features&#;, while the &#;Audio feedback&#; feature remains under active development.
The fundamental constituent within the envisaged sensor is a gyroscope, a device instrumental in the measurement and preservation of orientation and angular velocity. Consisting of a rotating wheel or rotor affixed to gimbals, this apparatus facilitates unimpeded rotation in all directions. Upon the initiation of rotor motion, its axis of rotation remains steadfast, unaffected by the device&#;s movements.
The gyroscope&#;s functioning is grounded in the conservation of angular momentum. As the rotor spins, it possesses a fixed amount of angular momentum, resisting alterations in its orientation. Consequently, if the device is rotated, the rotor maintains its original orientation, causing the gimbals to revolve around it.
depicts the hardware components of the balance sensor, including an mAh battery, a Wemos Lolin 32 Lite (an Arduino-type board) equipped with Wi-Fi capabilities, and an MPU sensor with a 3-axis gyroscope (offering programmable full-scale ranges of ±250, ±500, ±, and ± degrees per second, and minimal noise at 0.01 degrees per second per square root of Hertz), an accelerometer, and a digital motion processor. The accelerometer features user-programmable full-scale ranges of ±2 g, ±4 g, ±8 g, and ±16 g. Initial sensitivity calibration for both sensors minimizes production-line calibration needs. This device is designed to operate in temperatures ranging from &#;40 °C to 80 °C and with voltages between 1.71 V and 3.6 V. With Wi-Fi continuously active, it typically consumes an average of 150 mA, providing about 7 h of autonomy with a mAh battery. This translates to approximately three fencing training sessions before requiring recharging. The total estimated cost of the components is roughly $15, with an additional $5 for a 3D-printed enclosure (as shown in ) and a strap for wearing the sensor on the back, positioned between the shoulders.
Open in a separate windowOpen in a separate windowRecognizing the niche nature of fencing as a sport and the limited demand for such a device, the enclosure ( ) has been designed for 3D printing per order. It has dimensions of 60 mm by 60 mm and a height of 20 mm, featuring a screwless design for easy manual assembly. The design is based on the model available at [31]. The total 3D printing time is approximately 3 h, requiring around 30 g of PLA filament. The final enclosure weighs less than 100 g and can be comfortably worn with a back strap without hindering a fencer&#;s performance on the fencing piste.
The equilibrium sensor depicted in establishes connectivity with the internet through Wi-Fi, facilitating data storage in a cloud-based database. This configuration allows fencers to monitor their performance metrics about balance contemporaneously. Authorization to access this data is contingent upon individual fencer preauthorization, and further permissions can be granted to share this information with their respective coaches. This collaborative feature furnishes coaches with a comprehensive overview of the progress exhibited by all athletes under their tutelage.
Data access is facilitated for both fencers and coaches, subject to explicit permission granted by the fencers, and is executed through a server infrastructure. Data processing occurs on the server, while the smartphone application retains a transient copy of the data. This temporary copy is automatically deleted if fencers revoke access privileges for a specific coach.
Two discrete cohorts of fencers were meticulously selected to establish benchmarks utilizing the data from the intelligent balance sensor. The initial group, encompassing novice practitioners, comprised ten individuals with fewer than twelve months of fencing experience. Conversely, the second group, comprised of proficient fencers, consisted of ten individuals with a notable 4 to 5 years of fencing practice. The observation of these fencers transpired during their traversal along a 14 m fencing strip, both at 50% of their maximum speed and at full acceleration, denoted as 100% speed, as detailed in a prior study [28].
To conduct a performance evaluation between novice and experienced fencers, a meticulous measurement of their angular velocity along the X, Y, and Z axes was undertaken, employing a time resolution of 100 milliseconds. The resultant data were aggregated for each fencer by applying the mean absolute deviation (MAD).
MAD, as described in [32], is a statistical metric for assessing the average deviation between individual data points and the mean of the dataset. Its calculation involves determining the absolute difference between each data point and the dataset&#;s mean, summing these fundamental differences, and dividing the total by the number of data points in the set.
The mean absolute deviation (MAD) is expressed in the same units as the original dataset, which measures how dispersed the data are from their mean. A higher MAD value in our datasets indicates that a fencer exhibits unsteady or imbalanced movement, while a lower MAD signifies a fencer with advanced fencing skills. We chose MAD as a preferable alternative to the standard deviation, another commonly used measure of data spread. Unlike standard deviation, MAD is less affected by outliers, making it particularly valuable when extreme values, such as those caused by a contra attack resulting in high angular velocity on the Z-axis, might distort standard deviation calculations.
Each fencer&#;s movement control and balance performance were quantified using a trio of values: MAD of angular velocity on the X, Y, and Z axes. Subsequently, an unsupervised machine learning algorithm, K-means, was employed to partition the results of the two groups and evaluate whether these results could effectively differentiate between novice and advanced fencers.
K-means clustering, a well-established unsupervised machine learning algorithm [33], categorizes and segments data into clusters based on their similarities. This algorithm divides data points into k clusters with a centroid or central point. The algorithm initiates with random centroids and assigns data points to the nearest centroid. Centroids are then updated to reflect the mean of the given data points. This process repeats until convergence, achieved when centroids no longer change significantly or when a maximum number of iterations is reached.
displays the results at 50% speed, while presents the results at 100% speed. These figures are two-dimensional, considering only the MAD of angular velocity on the X and Y axes, though clustering also employs the Z-axis data. The automatic separation into two clusters remains consistent in both scenarios, demonstrating the reliability of the monitored data for distinguishing between experienced and inexperienced fencers. The centroids obtained can serve as reference points to identify fencers making progress in their movement control and those who may benefit from additional preparation.
Open in a separate windowOpen in a separate windowFurthermore, these data enable coaches to spot outliers, such as highly talented fencers who could be primed for high-performance training or those with consistently poor results, who may be better suited for recreational fencing rather than pursuing international medals. It is important to note that these proposed indicators rely solely on balance and movement control and should not be the sole performance metrics. They can be complemented by indicators of speed, reaction time, and precision abilities to enhance the selection of fencers with potential for high performance [28].
The haptic module is in the form of a smartwatch developed specifically for fencing [30].
In , the schematic representation illustrates the configuration of the haptic module integrated into the proposed system. This module is designed to furnish real-time feedback to fencers in the event of heightened imbalance, as expounded upon in greater detail in [30], which provides an exhaustive delineation of its features. For the immediate context, two primary functions are harnessed: the vibration motor, constituting the haptic feedback mechanism directed towards the fencers, and the Wi-Fi capabilities, essential for the reception of real-time commands from the balance sensor. At the core of this module resides an Arduino-based board, specifically the NodeMCU, as depicted in . This board encapsulates a Wi-Fi ESP ESP microchip, confined within a compact two by three cm rectangle, incurring an approximate cost of five euros.
Open in a separate windowillustrates the schematic representation of the enclosure design for the haptic module.
Open in a separate windowThe visual feedback module facilitates interaction with the balance sensor by utilizing the identical NodeMCU employed in the haptic feedback module. This NodeMCU is intricately linked to an 8.6 cm WS RGB LED ring. The WS delineates a smart LED light source family characterized by integrating the control circuit and the RGB chip within a compact -packaged unit. depicts the configuration of the visual feedback module. The LED light is strategically positioned at one extremity of the fencing strip, ensuring its perpetual visibility to the fencer undergoing training. In this scenario, fencers are necessitated to maintain continuous attention on the LED, mirroring the visual vigilance imperative in a fencing bout where constant visual analysis of the opponent is required. The LED emits a light green hue when the fencer&#;s movements align with acceptable balance standards corresponding to their skill levels. Conversely, an orange signal is activated if the fencer&#;s performance falls below the designated reference level, calibrated in accordance with their experience.
Open in a separate windowThis investigation engaged juvenile athletes from the ACS Floreta Fencing Club in Timișoara aged 11 to 14. Explicit written authorization from the parents or legal guardians of the athletes was secured to facilitate their involvement in the research. To augment the authenticity and scholarly import of the study, a meticulous selection procedure was implemented, adhering to precisely delineated inclusion and exclusion criteria. These criteria were systematically classified into two distinct groups to ensure transparency, as outlined below:
Subjects must be between 11 and 14 years old at the time of selection.
They should have 4&#;5 years of experience in fencing.
They must have the written consent of their parents/legal guardians for their participation in the study.
Inclusion criteria:
Unmotivated absence from training sessions (not more than 4 times/5 weeks) and tests.
Excused absence from more than four training sessions during the study. This kind of absence can be encountered in the context of competitions, training camps, school exams, or the occurrence of some illnesses.
Exclusion criteria:
The athletes were divided into three distinct groups: the control group (CG), the experimental group utilizing the visual feedback module (VG), and the experimental group employing the haptic feedback module (HG). Homogeneity across the groups was maintained concerning age, gender, and training proficiency, and the allocation process was executed through randomization, employing a lot-drawing method for both female and male participants.
Each group adhered to a regimen of four training sessions per week, wherein 30 min per session was designated explicitly for targeted exercises aimed at improving movement control. Notably, all groups engaged in identical exercises, with the sole divergence being that the VG and HG groups had access to the system incorporating the feedback module.
The evaluative benchmark comprises a structured 4-2-2-4 scenario using three strategically positioned poles at varying distances. These poles are situated at the commencement line, 2 m from the starting line, and 4 m from the starting line, respectively. Fencers are tasked with traversing the prescribed course, involving movement from the start point to the 4 m pole and back, followed by a sequence of movements from the starting point to the 2 m pole and back repeated twice. Subsequently, they navigate once more from the start point to the 4 m pole and back. Cumulatively, this entails forward and backward movements of 12 m each, encompassing seven alterations in movement direction. Comprehensive assessments were conducted on all fencers at the initiation of the study and subsequently repeated after a 5-week interval from the commencement of the investigation.
The angular velocity during movement is compared with reference thresholds. Determination of reference thresholds emanates from the cluster&#;s centroid associated with advanced fencers, as depicted in , and is delineated in . A performance falling below the reference threshold indicates commendable results, while a performance surpassing the threshold is deemed undesirable. To quantify improvements in balance and movement control throughout the study, the temporal aspect has been encapsulated by measuring the time percentage. This metric represents the proportion of time relative to the entirety of the benchmark test, during which fencers perform above the reference thresholds. To mitigate the potential impact of measurement errors throughout the comprehensive evaluation, an over-threshold performance is defined when at least two thresholds are exceeded or when all three thresholds are concurrently surpassed.
The comparative analysis between the initial and final test results entailed a relative average comparison within each group. Additionally, the Wilcoxon signed-rank test for repeated measures was employed to ascertain the statistical significance of improvements within each group. Furthermore, the inter-group improvements were subject to scrutiny through the Kruskal-Wallis [34] test, applied to the difference vectors derived from the initial and final measurements. Pairwise comparisons between groups, specifically CG vs. VG, CG vs. HG, and VG vs. HG, were conducted using the Mann-Whitney U test [35], with Bonferroni-corrected p-values [36]. Notably, non-parametric tests were selected for these analyses due to the limited sample size. The objective was to monitor the statistical significance of the outcomes, considering the inherent constraints associated with the modest number of samples.
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