Neuromuscular Performance and Injury Risk Assessment Using Fusion of Multimodal Biophysical and Cognitive Data: In-field Athletic Performance and Injury Risk Assessment

Abstract

Athletes rely on rationally bounded decisions of coaches and sports physicians to optimize performance, improve well-being, and reduce risk of injuries. These decisions are subjective or require costly tests that are not necessarily predictive of in-game performance or cannot predict risk of injury. This paper presents an approach to remedy this shortcoming by providing coaches and sports medicine teams with reliable tools for objective, quantitative assessment of in-field performance and risk of injury. The proposed method uses advanced physiological signal processing, data driven modelling, and multi-modal data fusion techniques applied to data recorded from unobtrusive wearable sensors in tasks and conditions that closely resemble those observed in the field during training or even a game. We postulate that the required data for this prediction task include joint kinematics from inertial measurement units or accelerometers, muscle surface electromyography, ground reaction force, electrocardiography, heart rate and heart rate variability, oxygen saturation, respiration rate, and pupillometry data. The required analysis methods include physiological signal processing, feature extraction, and data-driven modeling techniques to estimate neuromuscular properties, identify joint and leg stiffness, and assess cognitive performance from pupillometry and heart rate variability.