A processor-implemented method for risk prediction and corrective action for a contact type activity, the method comprising: generating a personalized full body musculoskeletal model with a 3 degrees of freedom (DoF) knee with ligament connectivity to facilitate a sliding motion, upon acquiring an input data from a device,, 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 (404, 504) in a simulation platform, 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, wherein the plurality of injury biomarkers are identified through inverse dynamics simulation of the personalized full body musculoskeletal model; 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, including muscle condition, joint kinematics and kinetics associated with said risk, calculated with respect to the plurality of injury biomarkers to predict said risk of injury;, designing a plurality of neuro-muscular controllers, which adjust muscle co-activation and synergy of a combination of groups of participating muscles, by varying the co-activation level during the contact type activity, performing, for each combination calculating a muscle tendon dynamics using muscle characteristics and force, velocity relation, computing multibody dynamic equation and deriving kinematic parameters, the kinematic parameters comprising acceleration, velocity and position; selecting the neuro-muscular controller capable of generating a plurality of optimal muscle co-activation parameters, which muscle co-activation parameters are indicative of muscle synergy during the one or more contact type activities, generating, based on at least the plurality of injury biomarkers, a plurality of optimal muscle co-activation parameters by the selected designed 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. generating a personalized full body musculoskeletal model with a 3 degrees of freedom (DoF) knee with ligament connectivity to facilitate a sliding motion, upon acquiring an input data from a device,, 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 (404, 504) in a simulation platform, 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, wherein the plurality of injury biomarkers are identified through inverse dynamics simulation of the personalized full body musculoskeletal model; 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, including muscle condition, joint kinematics and kinetics associated with said risk, calculated with respect to the plurality of injury biomarkers to predict said risk of injury;, designing a plurality of neuro-muscular controllers, which adjust muscle co-activation and synergy of a combination of groups of participating muscles, by varying the co-activation level during the contact type activity, performing, for each combination calculating a muscle tendon dynamics using muscle characteristics and force, velocity relation, computing multibody dynamic equation and deriving kinematic parameters, the kinematic parameters comprising acceleration, velocity and position; calculating a muscle tendon dynamics using muscle characteristics and force, velocity relation, computing multibody dynamic equation and deriving kinematic parameters, the kinematic parameters comprising acceleration, velocity and position; selecting the neuro-muscular controller capable of generating a plurality of optimal muscle co-activation parameters, which muscle co-activation parameters are indicative of muscle synergy during the one or more contact type activities, generating, based on at least the plurality of injury biomarkers, a plurality of optimal muscle co-activation parameters by the selected designed 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.