Systems and methods for predicting and optimizing physiological performance

Description (excerpt)

CROSS REFERENCE TO RELATED APPLICATION This application is a continuation of U.S. application Ser. No. 17/400,565 filed Aug. 12, 2021, which claims the benefit of U.S. application Ser. No. 16/105,569, filed Aug. 20, 2018 (pending), which claims the benefit of and priority to co-pending U.S. Provisional Application Ser. No. 62/547,449 filed Aug. 18, 2017. The contents of these applications are incorporated herein by reference in their entirety. GOVERNMENT INTEREST The invention described herein may be manufactured and used by or for the Government of the United States for all governmental purposes without the payment of any royalty. FIELD OF THE INVENTION The present invention relates generally to assessing individual and team physiology, physical activity, cognitive activity, and so forth. More particularly, the present invention relates to correlating individual and team physiology, physical activity, cognitive activity, and so forth to performance. BACKGROUND OF THE INVENTION Countless physiological and activity (e.g., body movement) related parameters can now be measured non-invasively using various commercially-available wearable sensor and related software technologies including, but not limited to, products under the trade names Fitbit®, Apple Watch®, Zebra Motionworks®, Zephyr™ Performance Systems, Omegawave®, etc., Examples of the physiological parameters that are often tested include heart rate, heart rate variability (sympathetic and parasympathetic), respiration rate, blood oximetry, etc. Activity-related parameters include change of direction, acceleration, distance ran/walked, speed, explosiveness, etc. Additional parameters may be derived from physiological parameters, activity-related parameters, or combinations thereof with personal data regarding the user. For example, mostly all commercially-available fitness tracking products estimate caloric burn using height, weight, a level physical activity detected, and physiological parameters (such as a heart rate during the physical activity). Similarly, Omegawave® includes a “CNS” (central nervous system) score that uses “DC-Potential measurement to monitor and manage signs of fatigue in your Central Nervous System.” The Zephyr™ system uses “physiological load” (defined generally as a cumulative index of effort based on heart rate over a period of time), “physiological intensity” (defined generally as an instantaneous index of effort based on heart rate at that moment), “mechanical load” (defined generally as a cumulative index of effort based on acceleration over a period of time), “mechanical intensity” (defined generally as an instantaneous index of effort based on acceleration at a particular moment), “training load” (an average of the mechanical and physiological load), and “training intensity” (average of the mechanical and physiological intensity), to name a few. While these commercially-available products are good at collecting and tracking many types of data for a particular individual, there remains a need for improvement as to how this data is used to predict and/or optimize performance. For example, the current commercially-available products are typically focused on displaying data being collected to give immediate feedback during training. In other words, the conventional products are typically focused on providing a “snap shot” of a particular individual's health at a particular moment in time without providing information that could be used to optimize the individual's future performance (over the next days or weeks, for example). Another deficiency of conventional fitness tracking devices is that the comparing of each particular individual's data to so-called “normal ranges” that are based solely or primarily on physical and measurable information provided by the particular individual to the device (for example, age, height, weight). However, analysis of the data collected is quite user-dependent and “normal ranges” often specific to the particular individual. While certain evaluations are conventionally used to determine an individual's performance (such as physical battery tests, cognitive scores, blood biomarkers, heart rate, heart rate variability, autonomous and central nervous system responses, etc.), the results from each test is highly variable by individual. As a result, data obtained from these tests, when compared against normal ranges generalized to a large population of similar age, height, and weight, will not provide a complete picture for the particular individual. Another difficulty with these conventional devices is the abundance of data, which complicates the sorting and identifying of which parameters are most important or significant for the particular individual. Moreover, parameters are activity dependent. That is, a parameter may be important for one type of sport or activity but not for a different sport or activity. What is needed therefore is a system for identifying parameters importance for a particular individual based on the sport or activity the individual