Validation of Metabolic Models for Estimation of Energy Expenditure During Isolated Concentric and Eccentric Muscle Contractions.

Musculoskeletal modeling uses metabolic models to estimate energy expenditure of human locomotion. However, accurate estimation of energy expenditure is challenging, which may be due to uncertainty about the true energy cost of eccentric and concentric muscle contractions. The purpose of this study was to validate three commonly used metabolic models, using isolated isokinetic concentric and eccentric knee extensions/flexions. Five resistance-trained adult males (25.6 ± 2.4 year, 90.6 ± 7.5 kg, 1.81 ± 0.09 m) performed 150 repetitions at four different torques in a dynamometer. Indirect calorimetry was used to measure energy expenditure during these muscle contractions. All three models underestimated the energy expenditure (compared with indirect calorimetry) for up to 55.8% and 78.5% for concentric and eccentric contractions, respectively. Further, the coefficient of determination was in general low for eccentric contractions (R2 < 0.46) indicating increases in the absolute error with increases in load. These results show that the metabolic models perform better when predicting energy expenditure of concentric contractions compared with eccentric contractions. Thus, more knowledge about the relationship between energy expenditure and eccentric work is needed to optimize the metabolic models for musculoskeletal modeling of human locomotion.

Prediction of movement in handball with the use of inertial measurement units and machine learning.

Inertial Measurement Units (IMU) and machine learning are strong tools in quantifying physical demands in sports, such as handball. However, the detection of both locomotion and throw events simultaneously has not been a topic for much investigation. Wherefore, the aim of this study was to publicise a method for training an extreme gradient boosting model capable of identifying low intensity, dynamic, running and throw events. Twelve adults with varying experience in handball wore an IMU on the back while being video recorded during a handball match. The video recordings were used for annotating the four events. Due to the small sample size, a leave-one-subject-out (LOSO) approach was conducted for the modelling and feature selection. The model had issues identifying dynamic movements (F1-score = 0.66 ± 0.07), whereas throw (F1-score = 0.95 ± 0.05), low intensity (F1-score = 0.93 ± 0.02) and running (F1-score = 0.86 ± 0.05) were easier to identify. Features such as IQR and first zero crossing for most of the kinematic characteristics were among the most important features for the model. Therefore, it is recommended for future research to look into these two features, while also using a LOSO approach to decrease likelihood of artificially high model performance.