Sensemitter is now Emhance.

For user researchers

For user researchers

Emotion intelligence,

built

for real

research

Bring lab-grade emotion and attention signals into at-home playtests on players’ own devices — then turn that data into clear outputs your team can act on.

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Trusted by player-obsessed studios

Our methodology

Our methodology

Biometric data

has value

We make it

actionable

Decades of research shows that physiological data can reveal moments missed by surveys and observation. Until now, applying these methods in games meant lab setups, specialist hardware, and slow analysis. Emhance AI makes these signals usable in real playtests, without labs or complex setups.

Lab-grade signals, natural play

Players test at home on their own devices, reducing observation effects and keeping context intact.

Lab-grade signals, natural play

Players test at home on their own devices, reducing observation effects and keeping context intact.

Lab-grade signals, natural play

Players test at home on their own devices, reducing observation effects and keeping context intact.

Lab-grade signals, natural play

Players test at home on their own devices, reducing observation effects and keeping context intact.

Camera-based access

Capture facial and attention signals without electrodes, head-mounted rigs, or calibration-heavy setups.

Camera-based access

Capture facial and attention signals without electrodes, head-mounted rigs, or calibration-heavy setups.

Camera-based access

Capture facial and attention signals without electrodes, head-mounted rigs, or calibration-heavy setups.

Camera-based access

Capture facial and attention signals without electrodes, head-mounted rigs, or calibration-heavy setups.

From signals to research outputs

Our neural net highlights peaks, drop-offs, and confusion zones, linked to game events and UI states.

From signals to research outputs

Our neural net highlights peaks, drop-offs, and confusion zones, linked to game events and UI states.

From signals to research outputs

Our neural net highlights peaks, drop-offs, and confusion zones, linked to game events and UI states.

From signals to research outputs

Our neural net highlights peaks, drop-offs, and confusion zones, linked to game events and UI states.

Method-aware framing

Signals are treated as diagnostic inputs, not literal emotional truth, with transparency about what is inferred.

Method-aware framing

Signals are treated as diagnostic inputs, not literal emotional truth, with transparency about what is inferred.

Method-aware framing

Signals are treated as diagnostic inputs, not literal emotional truth, with transparency about what is inferred.

Method-aware framing

Signals are treated as diagnostic inputs, not literal emotional truth, with transparency about what is inferred.

Built for triangulation

The Emhance AI methodology combines cutting edge emotion recognition and neural nets with traditional observation, interviews and surverys, providing a 360-degree view into player behavior.

Built for triangulation

The Emhance AI methodology combines cutting edge emotion recognition and neural nets with traditional observation, interviews and surverys, providing a 360-degree view into player behavior.

Built for triangulation

The Emhance AI methodology combines cutting edge emotion recognition and neural nets with traditional observation, interviews and surverys, providing a 360-degree view into player behavior.

Built for triangulation

The Emhance AI methodology combines cutting edge emotion recognition and neural nets with traditional observation, interviews and surverys, providing a 360-degree view into player behavior.

Repeatable research

Faster setup supports ongoing studies and consistent protocols so findings accumulate into a usable evidence base.

Repeatable research

Faster setup supports ongoing studies and consistent protocols so findings accumulate into a usable evidence base.

Repeatable research

Faster setup supports ongoing studies and consistent protocols so findings accumulate into a usable evidence base.

Repeatable research

Faster setup supports ongoing studies and consistent protocols so findings accumulate into a usable evidence base.

Research use cases

Research use cases

Where it fits in your workflow

Locate experience breaks, prioritise probes, and align stakeholders on what evidence supports a change.

Usability diagnostics

Pinpoint the exact seconds where comprehension breaks, attention drops, or control confidence collapses.

Usability diagnostics

Pinpoint the exact seconds where comprehension breaks, attention drops, or control confidence collapses.

Usability diagnostics

Pinpoint the exact seconds where comprehension breaks, attention drops, or control confidence collapses.

Hypothesis refinement

Use peaks and drop-offs to refine what to ask next and what variants to test, before running broader studies.

Hypothesis refinement

Use peaks and drop-offs to refine what to ask next and what variants to test, before running broader studies.

Hypothesis refinement

Use peaks and drop-offs to refine what to ask next and what variants to test, before running broader studies.

Experience mapping

Build a timeline of attention and activation across tasks, levels, or journeys, tied to events and UI states.

Experience mapping

Build a timeline of attention and activation across tasks, levels, or journeys, tied to events and UI states.

Experience mapping

Build a timeline of attention and activation across tasks, levels, or journeys, tied to events and UI states.

Comparative studies

Apply the same protocol to builds or competitors to compare confusion zones, pacing, and attention allocation.

Comparative studies

Apply the same protocol to builds or competitors to compare confusion zones, pacing, and attention allocation.

Comparative studies

Apply the same protocol to builds or competitors to compare confusion zones, pacing, and attention allocation.

Memory-weighted moments

Highlight likely recall drivers by weighting peak and ending moments, then connect them to design intent.

Memory-weighted moments

Highlight likely recall drivers by weighting peak and ending moments, then connect them to design intent.

Memory-weighted moments

Highlight likely recall drivers by weighting peak and ending moments, then connect them to design intent.

Stakeholder alignment

Share a single evidence timeline so decisions rely less on opinion and more on what happened, when, and why.

Stakeholder alignment

Share a single evidence timeline so decisions rely less on opinion and more on what happened, when, and why.

Stakeholder alignment

Share a single evidence timeline so decisions rely less on opinion and more on what happened, when, and why.

Why Emhance AI?

Regular Approaches Alone

Depends on recall and verbalisation after the moment

Hard to isolate the exact second where experience shifts

Lab-grade biometrics require rigs, cost, and specialist interpretation

Manual review cycles slow down production decisions

Second-by-second evidence tied to game events, with pre-cut clips to easily zoom in on specific patterns across players

Camera-based capture without lab rigs or intrusive setup

Automated decoding to surface peaks, drop-offs, and confusion zones

Outputs designed for triangulation and stakeholder alignment

How it works for researchers

Designed to fit existing research practice: define questions, run natural play, review moment-level evidence, then triangulate with your qualitative and behavioural data.

Define the research question

Frame hypotheses, tasks, and success criteria (usability, comprehension, pacing, or response to change).

Run natural playtests

Recruit from your audience or panels by GEO and genre. Players test on their own devices at home.

Capture activation & attention

Camera-based signals combined with interaction paths to preserve moment-level context.

Automated decoding

Identify peaks, drop-offs, and confusion zones, then align them to events and UI states.

Interpret and triangulate

Get access to all interviews, raw data and surveys, allowing you to check against telemetry and usability notes.

Build a research library

Store protocols and outcomes to compare builds, features, and audiences over time.

421+

Signals per second

Increase in average D1 retention

Faster iteration

Ready to level up your research toolkit?

See how Emhance AI can transform your user research by unlocking next-gen biometric testing.