Ways to integrate.
Call a model. Get a result.
Every plan includes all validated models and capabilities. Each returns a real-time measurement and, where applicable, a prediction of when the state will change, with a confidence interval.
Cognitive Load
Cognitive Load refers to the amount of effort a person allocates to a task. It is recognized as an important factor in determining a person’s level of performance in a task and during daily life activities (Devos et al., 2020).
Eye tracking: Cognitive Load can be measured through physiological tools. Eye tracking is a sensitive, quantifiable, accurate real-time measure of cognitive effort via eye responses such as changes in the pupil (Fehringer, 2021), blinking (Chen, Epps, Ruiz, Chen, 2011), and other eye movements (Irwin & Thomas, 2010; Kramer, 1991; Van Orden, Limbert et al., 2001; Greef et al., 2009; Klinger, Tversky et al., 2010).
Assumes the same activity continues without intervention.
Predicted future level: Low, Moderate, or High
Time to reach the future state: seconds
Probability (%): for the current level and other levels
Confidence interval (0–1, low–high): for each output
Attention
Attention refers to how a person directs and distributes their focus during a task or activity (Nideffer, 1976). The width of a person’s attentional focus, from narrow to broad, plays a critical role in task performance. The ability to match attentional width to the demands of a situation is a key driver of success across a wide range of activities (Nideffer, 1990; Nideffer & Sagal, 2006; Wulf, 2013).
Eye tracking: Attention can be measured through eye movements. Eye tracking provides a real-time, objective window into how attention is being deployed, whether a person is locked onto a specific target or actively scanning a wide field. The HarmonEyes Attention solution captures attentional width through fixation patterns, saccade frequency and duration, and gaze distribution across the visual field.
Optimal performance depends on matching attentional width to the task at hand. Mismatches (a narrow focus during a high-awareness task, or a broad focus during a precision task) are associated with reduced accuracy, slower reactions, and increased error rates.
Mental Readiness
Mental Readiness is a real-time, session-level metric derived from cumulative cognitive load and grounded in the mental fatigue literature, which defines mental fatigue as a decrease in mental performance resulting from cognitive overload, due to task duration and/or workload, independent of sleepiness.
It reflects how much cognitive capacity a person has left in reserve, as shaped by their cognitive load over time. People rarely operate at a single, steady level of mental effort, cognitive load naturally fluctuates, and periods of sustained high cognitive load gradually deplete available resources (Borragán et al., 2017; Hu & Lodewijks, 2020). When mental readiness runs low, the effects are tangible: attention narrows, decision-making slows, and errors become more likely. Tracking how much of a session is spent at high cognitive load is therefore a key indicator of sustained performance, resilience under pressure, and the likelihood of cognitive overload (Boksem & Tops, 2008; Chen et al., 2018).
A running percentage of time spent in each demand state, updated every second starting one minute into a session:
For example, a session showing 70% Low / 30% High indicates that most of the session was spent with meaningful cognitive reserve intact.
Fatigue
Fatigue is a state of increased situational sleepiness associated with reduced alertness, slower reactions, and a greater tendency to fall asleep (Hu and Lodewijks, 2020). It impairs focus and responsiveness, especially during prolonged or repetitive tasks. Fatigue is driven by a combination of sleep debt (how long it has been since a person last slept) and the body’s internal 24-hour clock. Sleep is the most effective way to relieve fatigue.
Eye tracking: Changes in blink behavior are especially indicative of increasing fatigue (Barbato et al., 1995; Caffier et al., 2003; Schleicher et al., 2008; Cori et al., 2019; Abe, 2023).
Predicted future level: Alert, Neither alert nor drowsy, Rather drowsy, or Drowsy
Time to reach the future state: seconds
Confidence interval (0–1, low–high): for each output
A ready-made front end for Theia output.
An optional display layer for teams who need a front end but don’t want to build one. Configurable widgets size to your canvas and render live capability output in real time, no UI work required.
Live display
Real-time Cognitive Load and Fatigue readouts, with discrete or continuous scales and configurable widget sizing on the canvas.
In-session controls
Thresholding, refining, baselining, annotation, and prompting, all available live during a session.
Replay & review
After a session, replay results with every interaction, add annotations, and track task type.
Export & resources
Save, upload, and download session data as CSV. Built-in access to tutorials, updates, and dashboard info.
Any camera, plus dedicated eye-tracking hardware.
Runs on the hardware you already have.
The Theia SDK turns standard eye-tracking hardware into human-state insight at a fixed per-frame compute cost. No GPU, no cloud round-trip, no calibration. Figures below reflect validated production targets.
Download the full spec sheet (PDF) →
The foundation model for human-state intelligence, measured from eye movement alone.