Predicting Net Joint Moments During a Weightlifting Exercise with a Neural Network Model

The purpose of this study was to develop and train a Neural Network (NN) that uses barbell mass and motions to predict hip, knee, and ankle Net Joint Moments (NJM) during a weightlifting exercise. Seven weightlifters performed two cleans at 85% of their competition maximum while ground reaction forces and 3-D motion data were recorded. An inverse dynamics procedure was used to calculate hip, knee, and ankle NJM. Vertical and horizontal barbell motion data were extracted and, along with barbell mass, used as inputs to a NN. The NN was then trained to model the association between the mass and kinematics of the barbell and the calculated NJM for six weightlifters, the data from the remaining weightlifter was then used to test the performance of the NN – this was repeated 7 times with a k-fold cross-validation procedure to assess the NN accuracy. Joint-specific predictions of NJM produced coefficients of determination (r2) that ranged from 0.79 to 0.95, and the percent difference between NN-predicted and inverse dynamics calculated peak NJM ranged between 5% and 16%. The NN was thus able to predict the spatiotemporal patterns and discrete peaks of the three NJM with reasonable accuracy, which suggests that it is feasible to predict lower extremity NJM from the mass and kinematics of the barbell. Future work is needed to determine whether combining a NN model with low cost technology (e.g., digital video and free digitising software) can also be used to predict NJM of weightlifters during field-testing situations, such as practice and competition, with comparable accuracy

PREDICTING NET JOINT MOMENTS DURING A HANG-POWER CLEAN FROM GROUND REACTION FORCES WITH A NEURAL NETWORK

The purpose of this study was to develop a deployable neural network (NN) to predict hip, knee, and ankle Net Joint Moments (NJM) from Ground Reaction Force (GRF) data during the hang-power clean. Thirteen male lacrosse players performed the hang-power clean exercise at 70% of their one-repetition maximum while GRF and 3-D motion data were acquired. An inverse dynamics procedure was used to calculate hip, knee, and ankle NJM. Center-of-mass velocity, position, and power were calculated from the GRF data and used as inputs to a NN that predicted hip, knee, and ankle NJM. Predicted NJM from the trained NN exhibited acceptable root mean squared errors, but produced large percentage differences between predicted and calculated peak NJM when tested on new data, which likely resulted from overfitting during open loop training or insufficient closed loop training

CONTROL AND REGULATION OF GROUND REACTION FORCES DURING THE PULL-PHASE OF THE SNATCH AND CLEAN

The purpose of this study was to determine how weightlifters control and regulate ground reaction forces (GRF) as they perform the snatch and clean. GRF were recorded as six skilled weightlifters participated in a weightlifting competition. Statistical parametric mapping was used to compare the vertical GRF, horizontal GRF, and GRF angle time-series data from both lifts. The results highlight tight control of horizontal GRF, which did not differ between lifts. The vertical GRF during the snatch and clean differed significantly during the first 35% of the entire pull-phase, likely due to the mass difference (~20kg) between the lifts. The GRF angle time-series differed only slightly between lifts. Regulating only the vertical GRF may provide an advantage in that weightlifters can use the same coordination patterns and simple scaling of muscle activations for the lifts

Biomechanical Determinants of the Reactive Strength Index During Drop Jumps

The Reactive Strength Index (RSI) is often used to quantify drop-jump (DJ) performance; however, not much is known about its biomechanical determinants. The purpose of this study was to investigate the correlations between the RSI and several biomechanical variables calculated from DJ performed with different initial drop heights. Twelve male NCAA Division I basketball players performed DJs from drop heights of 30, 45, and 60 cm. Force plates were used to calculate DJ performance parameters (ie, DJ height, contact time, and RSI) and DJ biomechanical variables (ie, vertical stiffness and eccentric/concentric energetics). Regression analyses were used to assess the correlations between variables at each drop height, and ANOVAs were used to assess the differences of all variables across drop heights. Follow-up analyses used 2 neural networks to determine if DJ performance and biomechanical data could accurately classify DJ trials by drop-height condition. Vertical-stiffness values were significantly correlated with RSI at each height but did not change across drop heights. Surprisingly, the RSI and other DJ parameters also did not vary across drop height, which resulted in the inability of these variables to accurately classify DJ trials. Given that vertical stiffness did not change across drop height and was highly correlated with RSI at each height, the RSI appears to reflect biomechanical behavior related to vertical stiffness during DJ. However, the inability of the RSI to accurately classify drop-height condition questions the use of RSI profiles established from DJs from different heights

Mechanical Demands of the Hang Power Clean and Jump Shrug: A Joint-level Perspective

The purpose of this study was to investigate the joint- and load-dependent changes in the mechanical demands of the lower extremity joints during the hang power clean (HPC) and the jump shrug (JS). Fifteen male lacrosse players were recruited from an NCAA DI team, and completed three sets of the HPC and JS at 30%, 50%, and 70% of their HPC 1-Repetition Maximum (1-RM HPC) in a counterbalanced and randomized order. Motion analysis and force plate technology were used to calculate the positive work, propulsive phase duration, and peak concentric power at the hip, knee, and ankle joints. Separate three-way analysis of variances were used to determine the interaction and main effects of joint, load, and lift type on the three dependent variables. The results indicated that the mechanics during the HPC and JS exhibit joint-, load-, and lift-dependent behavior. When averaged across joints, the positive work during both lifts increased progressively with external load, but was greater during the JS at 30% and 50% of 1-RM HPC than during the HPC. The JS was also characterized by greater hip and knee work when averaged across loads. The joint-averaged propulsive phase duration was lower at 30% than at 50% and 70% of 1-RM HPC for both lifts. Furthermore, the load-averaged propulsive phase duration was greater for the hip than the knee and ankle joint. The jointaveraged peak concentric power was the greatest at 70% of 1-RM for the HPC and at 30% to 50% of 1-RM for the JS. In addition, the joint-averaged peak concentric power of the JS was greater than that of the HPC. Furthermore, the load-averaged peak knee and ankle concentric joint powers were greater during the execution of the JS than the HPC. However, the loadaveraged power of all joints differed only during the HPC, but was similar between the hip and knee joints for the JS. Collectively, these results indicate that compared to the HPC the JS is characterized by greater hip and knee positive joint work, and greater knee and ankle peak concentric joint power, especially if performed at 30 and 50% of 1-RM HPC. This study provides important novel information about the mechanical demands of two commonly used exercises and should be considered in the design of resistance training programs that aim to improve the explosiveness of the lower extremity joints