Accelerometer Based Data Can Provide a Better Estimate of Cumulative Load During Running Compared to GPS Based Parameters.

Running is a popular way to become or stay physically active and to maintain and improve one's musculoskeletal load tolerance. Despite the health benefits, running-related injuries affect millions of people every year and have become a substantial public health issue owing to the popularity of running. Running-related injuries occur when the musculoskeletal load exceeds the load tolerance of the human body.

Predicting Future Perceived Wellness in Professional Soccer: The Role of Preceding Load and Wellness.

Purpose: The influence of preceding load and future perceived wellness of professional soccer players is unexamined. This paper simultaneously evaluates the external load (EL) and internal load (IL) for different time frames in combination with presession wellness to predict future perceived wellness using machine learning techniques.

Methods: Training and match data were collected from a professional soccer team.

Data fusion of body-worn accelerometers and heart rate to predict VO2max during submaximal running.

Maximal oxygen uptake (VO2max) is often used to assess an individual's cardiorespiratory fitness. However, measuring this variable requires an athlete to perform a maximal exercise test which may be impractical, since this test requires trained staff and specialized equipment, and may be hard to incorporate regularly into training programs. The aim of this study is to develop a new model for predicting VO2max by exploiting its relationship to heart rate and accelerometer features extracted during submaximal running.

Relationships Between the External and Internal Training Load in Professional Soccer: What Can We Learn From Machine Learning?

Purpose: Machine learning may contribute to understanding the relationship between the external load and internal load in professional soccer. Therefore, the relationship between external load indicators (ELIs) and the rating of perceived exertion (RPE) was examined using machine learning techniques on a group and individual level.

Methods: Training data were collected from 38 professional soccer players over 2 seasons.

Surface effects on dynamic stability and loading during outdoor running using wireless trunk accelerometry.

Despite frequently declared benefits of using wireless accelerometers to assess running gait in real-world settings, available research is limited. The purpose of this study was to investigate outdoor surface effects on dynamic stability and dynamic loading during running using tri-axial trunk accelerometry. Twenty eight runners (11 highly-trained, 17 recreational) performed outdoor running on three outdoor training surfaces (concrete road, synthetic track and woodchip trail) at self-selected comfortable running speeds.