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The Attention Meter algorithm indicates the intensity of mental “focus” or “attention.” The value ranges from 0 to 100. The attention level increases when a user focuses on a single thought or an external object, and decreases when distracted. Users can observe their ability to concentrate using the algorithm. In educational settings, attention to lesson plans can be tracked to measure their effectiveness in engaging students. In gaming, attention has been used to create “push” control over virtual objects.
The Meditation Meter algorithm indicates the level of mental “calmness” or “relaxation.” The value ranges from 0 to 100, and increases when users relax the mind and decreases when they are uneasy or stressed. The Meditation Meter quantifies the ability to find an inner state of mindfulness, and can thus help users learn how to self correct and find inner balance to overcome the stresses of everyday life. The algorithm is also used in a variety of game-design controls.
The Blink Detection algorithm signals a user’s blinks. A higher number indicates a “stronger” blink, while a smaller number indicates a “lighter” or “weaker” blink. The frequency of blinking is often correlated with nervousness or fatigue. Eye blinks are akin to a standard on/off binary system and therefore are valuable for controls that require definitive responses. For instance, in communication applications, one blink means no, two mean yes — giving individuals with a special needs a simple way to communicate.
The Mental Effort algorithm measures the mental workload while performing a task. The harder a user’s brain works on a task, the higher the value. The algorithm works well with both physical (e.g., drawing) and mental (e.g., reciting) tasks, and can be used for continuous real-time tracking and between-trial comparisons to measure the effects of multi-tasking, workload variability, and more. The algorithm can be used to track the effects of diverse cognitive loads on the ability to learn and provide feedback for user self-improvement.
The Familiarity algorithm tracks learning processes to measure the relative level of understanding, learning, or comfort with a task. It is a measure of the subconscious learning of procedural (motor) and mental tasks. In some cases, it reflects how well a user is doing with the task. By observing trends, users can better understand and assess their learning process. It can be applied to tasks that are physical in nature (e.g., drawing) or mental (e.g., recitation), and enable data-tracking assessments to gauge learning status.
The Appreciation algorithm provides real-time measurement of the level of enjoyment or appreciation a subject feels towards an external audiovisual stimulus. The algorithm allows moment-by-moment detection of emotions. In marketing applications, it can be used to track and understand a users level of appreciation, providing insights and strategic direction for marketing efforts.