Perspective on ?in the wild? movement analysis using machine learning

Recent advances in wearable sensing and machine learning have created ample opportunities for in the wild movement analysis in sports, since the combination of both enables real-time feedback to be provided to athletes and coaches, as well as long-term monitoring of movements. The potential for real-time feedback is useful for performance enhancement or technique analysis, and can be achieved by training efficient models and implementing them on dedicated hardware. Long-term monitoring of movement can be used for injury prevention, among others. Such applications are often enabled by training a machine learned model from large datasets that have been collected using wearable sensors. Therefore, in this perspective paper, we provide an overview of approaches for studies that aim to analyze sports movement in the wild using wearable sensors and machine learning. First, we discuss how a measurement protocol can be set up by answering six questions. Then, we discuss the benefits and pitfalls and provide recommendations for effective training of machine learning models from movement data, focusing on data pre-processing, feature calculation, and model selection and tuning. Finally, we highlight two application domains where in the wild data recording was combined with machine learning for injury prevention and technique analysis, respectively.

Automated summary

Recent advances in wearable sensing and machine learning have enabled real-time movement analysis in sports, providing feedback to athletes and coaches, as well as long-term monitoring of movements. This perspective paper provides an overview of approaches for analyzing sports movement using wearable sensors and machine learning. It discusses the establishment of a measurement protocol, effective training of machine learning models from movement data, and highlights two application domains for injury prevention and technique analysis.