EmPath:
The Emotional Response Layer For AI
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EmPath combines multimodal emotional-response signals with structured interaction context so AI systems can adapt around attention, friction, stress, confusion, engagement, and emotional impact.
From games and training systems to agents and adaptive software, EmPath helps AI understand not just what users do — but how experiences affect them emotionally.
Adaptive Experiences
Interactive systems can adapt pacing, difficulty, feedback, coaching, narrative, and challenge based on attention, stress, overload, confidence, frustration, or flow.
Agentic Product Optimization
As AI agents generate and modify applications, EmPath can help them identify where users lose attention, encounter friction, become confused, or disengage — then adapt interfaces, onboarding, content, or workflows accordingly.
Emotional Response Datasets
EmPath can generate structured datasets connecting scenarios, multimodal signals, probable emotional response, user behavior, and outcomes.
Emotionally Aligned AI
EmPath helps AI systems estimate emotional impact and choose responses that reduce harm, respect context, and behave with greater emotional intelligence.
AI needs a richer understanding of emotional response.
AI systems already learn from what users say and what users do: prompts, corrections, ratings, clicks, drop-off, error logs, and interaction patterns.
EmPath adds what those channels miss: multimodal signals about how users are feeling during the experience itself.
By combining direct input, behavior, physiology, environment, and structured context, EmPath helps AI systems estimate attention, friction, stress, confusion, overload, flow, disengagement, and emotional response.
How EmPath works
A user interacts with a game, app, training system, AI assistant, or scenario.
EmPath captures multimodal emotional-response signals, aligns them with what was happening, and estimates how an experience may be affecting the user.
AI systems can then use that feedback to adapt behavior, pacing, interface, content, support, or future design.
Multimodal Signals
Video, audio, facial expression, voice, posture, motion, sEMG, ECG (HR, HRV), EEG, PPG, GSR/EDA, respiration, temperature, interaction telemetry, error states, hesitation, and other signals where appropriate.
Structured Context
Signals are interpreted alongside the task, prompt, environment, interface, expected outcome, user behavior, and scenario history.
Emotional Response
EmPath estimates probable emotional response from multimodal evidence and context.
EmPath can be deployed wearables-free.
Case Study: Emotionally Aligned AI
Quantified Empathy™ is EmPath’s alignment layer: a framework for helping AI systems understand emotional impact and respond with better timing, tone, and care.
The video below shows how multimodal signals, structured scenarios, and probable emotional-state modeling can help future AI systems understand how their behavior affects people.
The next generation of AI won’t just respond to what we say.
It will understand how we respond emotionally.