Reaction RMO Pro vs MetaBrain ChatBot

This app allows you to determine the user's ability to react to a moving object. One of the methods for monitoring the functional state of the human central nervous system is to determine the ratio of excitation and inhibition processes during the reaction to a moving object (RMO). The human reaction to a moving object refers to the ability to determine the spatial and temporal parameters of the visual stimulus movement, and to quickly and accurately perform the necessary actions in response. To perform the test tasks, you need to click on the red circle when it matches the blue one. When performing this test, you can choose one of three testing modes. Each test task consists of three stages. You can choose between 10 to 30 attempts per stage. The demo mode 'Demo' allows for trial attempts. The standard mode 'Main mode' offers a test task where the visual stimulus speed is low in the first stage, increases by 1.5 times in the second stage, and has added distracting visual stimuli in the third stage. The 'Slow mode' offers a test task where the visual stimulus speed is low in all stages. The 'Fast mode' offers a test task where the visual stimulus speed is high in all stages. The program provides the following information about the measurements: - average value of the reaction (by module) for the whole test (Mean, ms), standard deviation (SD, ms); - the predominant direction of excitation or inhibition processes for the entire test ("Premature" - premature reactions, "Delayed" - reactions with a delay, "Accurate" - accurate reactions); - number of attempts in the stage; - test mode; - average values of reactions (by module, ms), standard deviations (ms), Shapiro-Wilk criterion (W) for each stage separately; - percentage of reactions for the entire test ("Premature, %" - premature reactions, "Delayed, %" - reactions with a delay, "Accurate, %" - accurate reactions). This app can be used by athletes and teachers of specialised educational institutions in their scientific research. The “Reaction RMO Pro” app includes an on-device machine learning model (Core ML) that automatically classifies reaction-to-a-moving-object (RMO) performance into sample-derived High, Medium, and Low levels based on the results of a three-stage test. The Boosted Tree model uses nine stage-specific predictors: mean absolute temporal deviation and temporal-deviation variability at each test stage, together with the percentages of premature, delayed, and accurate responses. The RMO_Index is calculated separately from standardised overall mean absolute temporal deviation and response variability, with higher values indicating more accurate and stable response timing. The app displays the ML-predicted RMO level, predicted class probabilities, the separately calculated RMO_Index, and descriptive characteristics of response timing. The High, Medium, and Low levels are sample-derived reference categories and should not be interpreted as universal normative levels or as validated indicators of competitive performance, fatigue, injury risk, or training outcomes. The model provides automated interpretation of RMO test results and may support future athlete-monitoring applications; however, external and longitudinal validation is required before broader practical use. All model inference and result processing are performed locally on the device without transmitting personal data to an external server. Results should be interpreted together with the athlete’s training status, testing conditions, and other relevant assessment data. The app is not a medical device and is not intended to provide medical diagnoses.

Fundamentally, our behavior is steered by deeply ingrained reactive patterns, which we refer to as mindsets residing in our unconscious as succinct, ready-to-execute instructions. These can be as primal as “run,” “fight,” “flight,” or “freeze, which produces a quick reaction (for safety).” A similar mechanism governs our responses to various situations, whether they are perceived as threatening or benign. The crux of the MetaBrain solution revolves around the *transformation of these mindsets to alter our automatic reactions. This is where the MetaBrain Chatbot plays a pivotal role, meticulously designed to discern the foundational mindset behind undesired thoughts, emotions, or behaviors and facilitate the transition to a personally selected one. Our approach involves the utilization of a lie-detection wearable to validate the correlation between a specific thought, feeling, or behavior and the mindset underpinning it. This process of validation empowers precise adjustments, effectively ushering in a shift from one mindset to another. To illustrate this process, consider the example of shifting from an old mindset of “I worry all the time” to a new mindset of “I worry very little.” Even a slight adjustment like this, achieved through understanding the underlying reasons, can have a profound impact. These reasons often trace back to past experiences, such as observing a family member’s chronic worrying or receiving messages like “Be cautious, something bad might happen,” leading to incessant worry. This learned behavior can become debilitating as days are consumed by ruminating over potential dangers that may or may not materialize. However, by making a small shift in mindset, this behavior can be effectively halted. This is the essence of what MetaBrain strives for – empowering individuals to *purposefully modify their mindsets in alignment with their desired outcomes, transcending the limitations they may have inherited, no matter where they originated.