Purpose: To examine the validity of 2 kinematic systems for assessing mean velocity (MV), peak velocity (PV), mean force (MF), peak force (PF), mean power (MP), and peak power (PP) during the full-depth free-weight back squat performed with maximal concentric effort. Methods: Ten strength-Trained men (26.1 ± 3.0 y, 1.81 ± 0.07 m, 82.0 ± 10.6 kg) performed three 1-repetition-maximum (1RM) trials on 3 separate days, encompassing lifts performed at 6 relative intensities including 20%, 40%, 60%, 80%, 90%, and 100% of 1RM. Each repetition was simultaneously recorded by a PUSH band and commercial linear position transducer (LPT) (GymAware [GYM]) and compared with measurements collected by a laboratory-based testing device consisting of 4 LPTs and a force plate. Results: Trials 2 and 3 were used for validity analyses. Combining all 120 repetitions indicated that the GYM was highly valid for assessing all criterion variables while the PUSH was only highly valid for estimations of PF (r = .94, CV = 5.4%, ES = 0.28, SEE = 135.5 N). At each relative intensity, the GYM was highly valid for assessing all criterion variables except for PP at 20% (ES = 0.81) and 40% (ES = 0.67) of 1RM. Moreover, the PUSH was only able to accurately estimate PF across all relative intensities (r = .92-.98, CV = 4.0-8.3%, ES = 0.04-0.26, SEE = 79.8-213.1 N). Conclusions: PUSH accuracy for determining MV, PV, MF, MP, and PP across all 6 relative intensities was questionable for the back squat, yet the GYM was highly valid at assessing all criterion variables, with some caution given to estimations of MP and PP performed at lighter loads.

No description available.

Objectives: The purpose of this study was to examine the relationship of velocities in the back squat between one repetition maximum (1RM) and submaximally loaded repetition maximum (RM) conditions, specifically in regard to what has been described as the minimal velocity threshold (MVT). The MVT describes a minimum concentric velocity that an individual must reach or surpass in order to successfully complete a repetition. Design: To test the presence of a MVT, participants were tested for 1RM and RM back squat ability. The mean concentric veloci ties (MCV) of the last successful repetition of each condition were then compared. Methods: Fourteen male participants familiar with the back squat volunteered to participate in the current study (age = 25.0 y ± 2.6, height = 178.9 cm ± 8.1, body mass = 88.2 kg ± 15.8). The mean concentric velocity (MCV) during the last successful repetition from each testing condition was considered for the comparison. Results: Results indicated a non-significant negative relationship of MCV between the 1RM and RM conditions (r = -0.135), no statistical difference between testing conditions (p = 0.266), with a small-to-moderate effect size (d = 0.468). Conclusions: The results of this study suggest that MVT should be further investigated to enhance its use in the practical setting. Additionally, coaches considering using a velocity-based approach for testing athletes should use data from either 1RM or RM conditions, but not both interchangeably. Coaches should be cautious when considering group averages or comparing velocity data between athletes, which may not be appropriate based on our results.

Background: The intensity or load of a strength training exercise is commonly considered to be the most important factor contributing to muscular strength and power. Traditionally in strength training, intensity is defined as the percentage of the maximum weight that can be lifted once i.e. 1 repetition maximum. For power development exercises, the velocity can be used to measure the intensity. A linear position transducer is able to measure kinetic and kinematic variables. Velocity-based training refers to the usage of a linear position transducer to track movement velocity of an exercise and thus, using velocity, rather than load, as a measurement of intensity. Purpose: The purpose of this systematic review was to provide an analysis of the existing velocity-based training research utilizing a linear position transducer. The study also aimed to investigate the validity and reliability of different commercial linear position transducers for kinetic and kinematic measurements.   Method: A systematic review was conducted from 19 studies on velocity-based training that met the selection criteria and underwent a quality assessment. Results: It was possible to predict the 1 repetition maximum using velocity and the minimal velocity threshold was stable across different relative intensities. Performing squats at either maximal velocity, or stopping at a velocity loss of <40% could significantly improve 1 repetition maximum, increase mean velocity during a set of squats as well as vertical jump performance. Two linear position transducer were found to have excellent validity and reliability for both kinetic and kinematic measurements. Conclusion: Velocity-based training was beneficial for enhancing neuromuscular adaptions and could be used to predict the 1 repetition maximum. When using of a linear position transducer for power development, it is suggested that it is valid and reliable for both kinetic and kinematic measurements.

The purpose of the current study was two-fold: (1) To examine the variation in velocity and power with increasing intensity in the back squat among subjects; and (2) To explore individual subject characteristics as possible explanations for variations of velocity in the back squat. Fourteen recreationally trained male subjects with experience in the back squat agreed to participate in the study (age = 25.0 ± 2.6 years, height = 178.9 ± 8.1 cm, body mass = 88.2 ± 15.8 kg). One-repetition maximums (1RM) were performed for each subject on force platforms with four linear position transducers attached to the barbell. The 1RM assessment was immediately preceded by warm-up sets at 65%, 75%, 85%, and 95% of estimated 1RM for 5, 3, 2, and 1 repetitions, respectively. Mean concentric velocity (MCV) and mean power were recorded for each intensity condition and were analyzed using Pearson correlation to determine the relationship between each variable and relative intensity (%1RM). Statistically significant negative relationships existed between %1RM and MCV (r = −0.892) and mean power (r = −0.604). Between-subject coefficient of variation tended to increase as %1RM increased for both MCV and mean power. These results suggest that MCV is superior to mean power as an indicator of relative intensity in the back squat. Additionally, the between-subject variation observed at higher intensities for MCV and mean power support the use of velocity ranges by strength and conditioning coaches.

The aim of this study was to evaluate the level of agreement in measuring back squat kinematics between an inertial measurement unit (IMU) and a 3D motion capture system (3DMOCAP). Kinematic variables included concentric peak velocity (CPV), concentric mean velocity (CMV), eccentric peak velocity (EPV), eccentric mean velocity (EMV), mean propulsive velocity (MPV), and POP-100: a proprietary variable. Sixteen resistance-trained males performed an incrementally loaded one repetition maximum (1RM) squat protocol. A series of Pearson correlations, 2 × 4 RM ANOVA, Cohen's effect size differences, coefficient of variation (CV), and standard error of the estimate (SEE) were calculated. A large relationship existed for all variables between devices ( 0.78-0.95). Between-device agreement for CPV worsened beyond 60% 1RM. The remaining variables were in agreement between devices with trivial effect size differences and similar CV magnitudes. These results support the use of the IMU, regardless of relative intensity, when measuring EMV, EPV, MPV, and POP-100. However, practitioners should carefully select kinematic variables of interest when using the present IMU device for velocity-based training (VBT), as certain measurements (e.g., CMV, CPV) do not possess practically acceptable reliability or accuracy. Finally, the IMU device exhibited considerable practical data collection concerns, as one participant was completely excluded and 13% of the remaining attempts displayed obvious internal error.

This work explores the application of Artificial Intelligence (AI) in sports, specifically comparing. You Only Look Once (YOLO) version 8 and version 9 models in the context of Velocity-Based Training and resistance training. It aims to evaluate the models’ performance in instance segmentation and their effectiveness in estimating velocity metrics. Additionally, methods for pixel to meter conversion and centroid selection on barbells are developed and discussed. The field of AI is growing vastly with great practical possibilities in the sports industry. Traditional methods of collecting and analyzing data involving sensors are often expensive and not available to many coaches and athletes. By leveraging AI techniques, this work aims to provide insights to more cost-effective solutions. An experiment was conducted where YOLOv8 and YOLOv9 models of different sizes were trained on a custom dataset. Using the resulting model weights, key Velocity-based Training (VBT) metrics were extracted from videos of squat, bench press and deadlift exercises, and compared with sensor data. To automatically track the barbell in the videos, the centroids of bounding boxes were used. Additionally, to acquire the velocity in meters per second, pixel-to-meter conversion ratios were obtained using the Circular Hough Transform. Findings indicate that the YOLOv8x model generally excels according to performance metrics, however recording high mean inference time. Additionally, the YOLOv8m model showed overestimation in mean velocity, peak velocity and range of motion highlighting potential challenges for real-time VBT applications. Otherwise, all models performed very similar to sensor data, occasionally differing in scale stemming from faulty pixel to meter conversions. In conclusion, this work underscores AI’s potential in the sports industry while identifying areas for further enhancement to ensure accuracy and reliability in applications.

Load-velocity relationships may vary between people of different strength levels and across different loads. The purpose of this dissertation was to investigate how external loads influence the velocity characteristics of the back squat exercise, and the influence of strength on these variables. Healthy male students with a history of resistance training completed repetitions at specified intensities of their estimated one-repetition maximum (1RM) until they reached 1RM. Back squat 3D motion analysis was captured using four Vicon T010 cameras (Vicon Motion Systems Ltd.; Oxford, UK) and Vicon Nexus 1.8.5 software. Data were transported into R custom coding statistical analysis software (version 3.5.2; The R Foundation) to calculate velocity analyses which determined mean and peak concentric (MCV, PCV) and eccentric (MEV, PEV) values. Participants were grouped by their relative strength (body mass/1RM) in the back squat, as well as their ability to move often prescribed loads with greater speed (63-70%1RM, 83-87%1RM). Between-groups comparisons were made for MCV at all loading conditions, and correlational relationships between all velocity measures (MEV, PEV, MCV, PCV) were examined for each group. For all subjects, there was a significant effect for relative intensity (%1RM) on MCV, but only for the groups organized by MCV at 63-70%1RM and 83-87%1RM was there a between-subjects effect for group. Correlational analyses between velocity measurements during concentric and eccentric phase of the back squat showed a tendency for high relationships (r = 0.5-0.69) between all phases that weakened as the relative intensity increased. These differences were illustrated uniquely between subject grouping conditions. These results indicate that load-velocity characteristics of the back squat cannot necessarily be positively related to strength level in the movement, and that profiling athletes by their velocities at specific relative intensities could be an effective means of organization.

The purpose of the present study was to evaluate the concurrent validity, intra-unit, and interunit reliability of the IMU Vmaxpro for measuring velocity for VBT purposes. Two protocols were constructed to cover velocities seen in the practical environment. Utilizing the 1080 Quantum the first protocol collected data without the use of subjects. For the second protocol, six well-trained men (age: 25.7 ± 4.2 years, standing height: 185.8 ± 10.6 cm, bodyweight: 87.3 ± 9.1 kg) performed loaded countermovement jumps. Two Vmaxpros and a 1080 Quantum simultaneously recorded repetitions for mean and peak velocity. Linear regression, RMSE, Bland-Altman, TE, and SDC were used to evaluate the concurrent validity, intra-unit, and inter-unit reliability. A strong relationship was found for MV and PV (R2 = 0.991– 0.997, RMSE = 0.044 – 0.05), p < 0.05) as well as a strong agreement on both protocols (bias: -0.039 – 0.072 on Protocol 1 and bias: 0.155 – - 0.005 on Protocol 2). The Vmaxpro showed strong reliability scores for within (MV: TE = 0.013 – 0.021; PV: TE = 0.017 – 0.023), and between sessions (MV: TE = 0.014 – 0.020; PV: TE = 0.019 – 0.027). Inter-unit reliability was acceptable to strong for both MV (TE = 0.012 – 0.034) and PV (TE = 0.021 – 0.057). The Vmaxpro can provide valid and reliable measurements for VBT purposes when using a single sensor. However, the inter-unit reliability showed a magnitude of variance which suggests practitioners not to use multiple devices interchangeably, and if so, it should be done with caution.