Method and system for injury risk prediction and corrective action for high contact type activity

A system for risk prediction and corrective action for a contact type activity, the system comprising:

• one or more memories;
• and
• one or more hardware processors, the one or more memories coupled to the one or more hardware processors, wherein the one or more hardware processors are capable of executing programmed instructions stored in the one or more memories to:
• generate a personalized full body musculoskeletal model to depict a knee and ankle joint behavior of a subject during the contact type activity;
• simulate one or more contact type activities using the personalized full body musculoskeletal model;
• identify a plurality of injury biomarkers based on the one or more contact type activities;
• analyze a plurality of parameters indicative of risk of injury to a plurality of participating muscle groups during the one or more contact type activities calculated with respect to the plurality of injury biomarkers to predict said risk of injury;
• and
• generate, based on at least the plurality of injury biomarkers, a plurality of optimal muscle co-activation parameters by a neuro-muscular controller, to adapt the plurality of participating muscle groups for providing the correct action against the risk of injury being predicted, the plurality of optimal muscle co-activation parameters indicative of muscle synergy during the one or more contact type activities.

One or more non-transitory machine readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors causes the one or more hardware processor to perform a method for risk prediction and corrective action for a contact type activity, said method comprising:

• generating a personalized full body musculoskeletal model to depict a knee and ankle joint behavior of a subject during the contact type activity, via one or more hardware processors;
• simulating one or more contact type activities using the personalized full body musculoskeletal model, via the one or more hardware processors;
• identifying a plurality of injury biomarkers based on the one or more contact type activities, via the one or more hardware processors;
• analyzing, via the one or more hardware processors, a plurality of parameters indicative of risk of injury to a plurality of participating muscle groups during the one or more contact type activities calculated with respect to the plurality of injury biomarkers to predict said risk of injury;
• and
• generating, based on at least the plurality of injury biomarkers, a plurality of optimal muscle co-activation parameters by a neuro-muscular controller via the one or more hardware processors, to adapt the plurality of participating muscle groups for providing the correct action against the risk of injury being predicted, the plurality of optimal muscle co-activation parameters indicative of muscle synergy during the one or more contact type activities.
• The one or more non-transitory machine readable information storage mediums of claim 19, wherein the personalized full body musculoskeletal model comprises a 92 muscle actuators, a 3 degrees of freedom (DoF) knee with ligament connectivity, and 9 ligament bundles designed and placed with respect to anatomical constraints and bounds of the subject.