Biomechanical Analysis of the Cross, Hook, and Uppercut in Junior vs. Elite Boxers: Implications for Training and Talent Identification

Impact of Leg Strength on the Force Produced During a Cross-Punch in Highly Trained Amateur Boxers

ABSTRACT

Satkunskiene, D, Bruzas, V, Mantas, M, Snieckus, A, and Kamandulis, S. Impact of leg strength on the force produced during a cross-punch in highly trained amateur boxers leg strength impact on cross-punch force. J Strength Cond Res XX(X): 000-000, 2024-The purpose of this study was to determine the impact of leg strength on force generation during the execution of a cross-punch by highly trained amateur boxers. This study involved 12 male, amateur boxers with a body mass range of 64.5-112.2 kg and a mean of 89.2 ± 17.0 kg. This study used an isokinetic dynamometer to measure the peak torque of the knee extensor and flexor muscles at 60°/s. The force plate was used to collect three-dimensional ground reaction forces (GRF), moments (GRM), and the center of pressure (COP) during a maximal cross-punch. The Kiktest-100 system was used to determine the force of the punch, while the Hykso punch tracker accelerometer measured the maximum fist velocity. This study revealed a positive correlation between knee extensor strength and GRFz, GRFy, and COPy during punch performance. In addition, the GRMz gradient and GRMz impulse during the execution phase have a significant positive effect on cross-punch force. In conclusion, the force of a straight punch in highly trained, amateur boxers is related to a greater angular velocity about the vertical axis during body rotation.

The Effects of Boxing Glove Design on Thumb Position When Making a Fist for Striking

ABSTRACT

Peveler, WW, Schoffstall, J, Coots, J, Kilian, J, and Glauser, J. The effects of boxing glove design on thumb position when making a fist for striking. J Strength Cond Res 38(5): 948-950, 2024-It has been suggested that boxing glove design alters thumb position increasing the risk of injury. The purpose of this study was to determine the effects of boxing glove design on thumb joint angles when making a fist. Ten experienced fighters participated in this study. A DEXA scan was used to produce an x-ray image of thumb position for all conditions (no gloves and 10-oz boxing gloves). Mean values for dependent measures were compared using a paired-sample T test and an alpha of 0.05. The carpometacarpal (CMC) joint angle was significantly different between no glove (14.1 ± 6.54°) and boxing glove (34.2 ± 7.60°) at p ≤ 0.001. The metacarpophalangeal (MP) joint angle was significantly different between no glove (132.6 ± 12.74°) and boxing glove (149.40 ± 8.15°) at p ≤ 0.001. The IP joint angle was not significantly different between no glove (135.50 ± 19.12°) and boxing glove (144.40 ± 17.39°) at p = 0.269. The perpendicular distance from the second metacarpal of the hand to the center of the MP joint was significantly different between no glove (0.48 ± 0.54 cm) and boxing glove (1.84 ± 0.29 cm) at p ≤ 0.001. Use of a boxing glove resulted in abduction of the thumb away from the hand and increased CMC and MP joint angles that were significantly different in relation to making a fist without a glove. Information from this study may provide insight into the high rate of thumb injury and provide insight for future boxing glove design.

Validity of Commercially Available Punch Trackers

ABSTRACT

Omcirk, D, Vetrovsky, T, Padecky, J, Malecek, J, and Tufano, JJ. Validity of commercially available punch trackers. J Strength Cond Res 37(11): 2273-2281, 2023-This study determined how well data from commercially available punch trackers (Corner, Hykso, and StrikeTec) related to gold-standard velocity and force measures during full-contact punches. In a quasi-randomized order, 20 male subjects performed 6 individual rear straight punches, rear hooks, and rear uppercuts against a wall-mounted force plate. Punch tracker variables were compared with the peak force of the force plate and to the peak (QPV) and mean velocity (QMV) assessed through Qualisys 3-dimensional tracking. For each punch tracker variable, Pearson's correlation coefficient, mean absolute percentage error (MAPE), and mean percentage error (MPE) were calculated. There were no strong correlations between punch tracker data and gold-standard force and velocity data. However, Hykso "velocity" was moderately correlated with QMV ( r = 0.68, MAPE 0.64, MPE 0.63) and QPV ( r = 0.61, MAPE 0.21, MPE -0.06). Corner Power G was moderately correlated with QMV ( r = 0.59, MAPE 0.65, MPE 0.58) and QPV ( r = 0.58, MAPE 0.27, MPE -0.09), but Corner "velocity" was not. StrikeTec "velocity" was moderately correlated with QMV ( r = 0.56, MAPE 1.49, MPE 1.49) and QPV ( r = 0.55, MAPE 0.46, MPE 0.43). Therefore, none of the devices fared particularly well for all of their data output, and if not willing to accept any room for error, none of these devices should be used. Nevertheless, these devices and their proprietary algorithms may be updated in the future, which would warrant further investigation.

Profiling the Physical Performance of Young Boxers with Unsupervised Machine Learning: A Cross-Sectional Study

Mexico City is the location with the largest number of boxers in Mexico; in fact, it is the first city in the country to open a Technological Baccalaureate in Education and Sports Promotion with a pugilism orientation. This cross-sectional study aimed to determine the physical-functional profile of applicants for admission to the baccalaureate in sports. A total of 227 young athletes (44F; 183M; 15.65 (1.79) years; 63.66 (14.98) kg; >3 years of boxing experience) participated in this study. Body mass (BM), maximal isometric handgrip (HG) strength, the height of the countermovement jump (CMJ), the velocity of straight boxing punches (PV), and the rear hand punch impact force (PIF) were measured. The young boxers were profiled using unsupervised machine learning algorithms, and the probability of superiority (ρ) was calculated as the effect size of the differences. K-Medoids clustering resulted in two sex-independent significantly different groups: Profile 1 (n = 118) and Profile 2 (n = 109). Except for BM, Profile 2 was statistically higher (p < 0.001) with a clear distinction in terms of superiority on PIF (ρ = 0.118), the PIF-to-BM ratio (ρ = 0.017), the PIF-to-HG ratio (ρ = 0.079) and the PIF-to-BM+HG ratio (ρ = 0.008). In general, strength levels explained most of the data variation; therefore, it is reasonable to recommend the implementation of tests aimed at assessing the levels of isometric and applied strength in boxing gestures. The identification of these physical-functional profiles might help to differentiate training programs during sports specialization of young boxing athletes.

Effects of lower and upper body fatigue in striking response time of amateur karate athletes

In combat sports, strikes or counter-strikes response time (RT) can be related to performance and sporting success. Moreover, training sessions are usually highly fatiguing, which is expected to impair basic skills, such as RT. Thus, this study aimed to investigate the effect of fatigue on punch and kick RTs of karate practitioners. Twelve individuals of both sexes from different levels (three yellow belts, three red belts, two orange belts, two green belts, one brown belt, and one black belt) were selected. Participants were aged 22 ± 3 years old, with a stature of 169.1 ± 6.5 cm, and a body mass of 65.5 ± 10 kg. Six visits were held with each participant. On the first 2 days, the RT of punches and kicks was measured by a validated smartphone app (TReaction). For the subsequent visits, a randomized incremental test for the upper or lower body was adopted as motor fatigue protocol, immediately followed by punches and kicks RT tests, also in random order. For induction of lower and upper body-specific muscle fatigue, the ITStriker app was used, which operates by emitting sound signals transmitted by a smartphone. One-way repeated measures ANOVA was performed, and significance was set at p ≤ 0.05. Regarding the mean punches RT, significant effects between situations for the upper (F_(2,22) = 11.5; ω² = 0.23; p < 0.001) and lower body (F_(2,22) = 14.2; ω² = 0.18; p < 0.001) fatigue protocols were found. The negative effect of the lower body fatigue protocol in punches RT was evident regardless of the order of the tests (punch RT first: Δ = 10.5%; t = 4.4; p < 0.001; d = 1.0; kick RT first: Δ = 11.4%; t = 4.8; p < 0.001; d = 1.1). Regarding mean kicks RT, significant effects were found between situations for the lower (F_(2,22) = 16.6; ω² = 0.27; p < 0.001) but not for the upper (F_(2,22) = 2.3; ω² = 0.02; p = 0.12) body fatigue protocols. Kick RTs were negatively affected by the lower body fatigue protocol regardless of the RT order applied (punch RT first: Δ = 7.5%; t = 3.0; p = 0.01; d = 0.8; kick RT first: Δ = 14.3%; t = 5.7; p < 0.001; d = 1.5). Upper body fatigue does not impair punch or kick RTs. Thus, it is concluded that the specificity of fatigue protocols and striking order should be considered while performing RT demanding techniques in karate practice. Specifically, lower body motor fatigue may impair both kicks and punches RT, which highlights the role of lower limbs in punches performance. Otherwise, upper body motor fatigue seems to induce impairments that are limited to the specific motor actions of this body segment.

Accuracy of a markerless motion capture system in estimating upper extremity kinematics during boxing

Kinematic analysis of the upper extremity can be useful to assess the performance and skill levels of athletes during combat sports such as boxing. Although marker-based approach is widely used to obtain kinematic data, it is not suitable for “in the field” activities, i.e., when performed outside the laboratory environment. Markerless video-based systems along with deep learning-based pose estimation algorithms show great potential for estimating skeletal kinematics. However, applicability of these systems in assessing upper-limb kinematics remains unexplored in highly dynamic activities. This study aimed to assess kinematics of the upper limb estimated with a markerless motion capture system (2D video cameras along with commercially available pose estimation software Theia3D) compared to those measured with marker-based system during “in the field” boxing. A total of three elite boxers equipped with retroreflective markers were instructed to perform specific sequences of shadow boxing trials. Their movements were simultaneously recorded with 12 optoelectronic and 10 video cameras, providing synchronized data to be processed further for comparison. Comparative assessment showed higher differences in 3D joint center positions at the elbow (more than 3 cm) compared to the shoulder and wrist (&lt;2.5 cm). In the case of joint angles, relatively weaker agreement was observed along internal/external rotation. The shoulder joint revealed better performance across all the joints. Segment velocities displayed good-to-excellent agreement across all the segments. Overall, segment velocities exhibited better performance compared to joint angles. The findings indicate that, given the practicality of markerless motion capture system, it can be a promising alternative to analyze sports-performance.

Biomechanics of the Upper Limbs: A Review in the Sports Combat Ambit Highlighting Wearable Sensors

Over time, inertial sensors have become an essential ally in the biomechanical field for current researchers. Their miniaturization coupled with their ever-improvement make them ideal for certain applications such as wireless monitoring or measurement of biomechanical variables. Therefore, in this article, a compendium of their use is presented to obtain biomechanical variables such as velocity, acceleration, and power, with a focus on combat sports such as included box, karate, and Taekwondo, among others. A thorough search has been made through a couple of databases, including MDPI, Elsevier, IEEE Publisher, and Taylor & Francis, to highlight some. Research data not older than 20 years have been collected, tabulated, and classified for interpretation. Finally, this work provides a broad view of the use of wearable devices and demonstrates the importance of using inertial sensors to obtain and complement biomechanical measurements on the upper extremities of the human body.

Using an artificial neural network to develop an optimal model of straight punch in boxing and training in punch techniques based on this model and real-time feedback

The work was aimed to develop an optimal model of a straight punch in boxing based on an artificial neural network (ANN) in the form of a multilayer perceptron, as well as to develop a technique for improving the technique of punches in boxing based on feedback, when each punch delivered by a boxer was compared with the optimal model. The architecture of the neural network optimal punch model included an input layer of 600 nodes-the values of absolute accelerations and angular velocities, four hidden ones, as well as a binary output layer (the best and not the best punch). To measure accelerations and angular velocities, inertial measuring devices were attached to the boxers' wrists. Highly qualified participated in the data set for the development of the optimal model. The best punches were chosen according to the criteria of strength and speed. The punch force was determined using a boxing pad with the function of measuring the punch force. In order to be able to compare punches, a unified parameter was developed, called the punch quality, which is equal to the product of the effective force and the punch speed. To study the effects of biofeedback, the boxing pads were equipped with five LEDs. The more LEDs were turned on, the more the punch corresponded to the optimal model. As a result of the study, an almost linear relationship was found between the quality of the punch of entry-level boxers and the optimal model. The use of feedback allowed for an increase in the quality of punches from 11 to 25%, which is on average twice as high as in the group where the feedback method was not used. Studies have shown that it is possible to develop an optimal punch model. According to the degree of compliance with this model, you can evaluate and train boxers in the technique.