Every output is linked to
- Observable signals
- Confidence estimates
- Contributing factors
- Historical trends
Ethics & Transparency Executive Brief
The Human-State Layer
EmoPulse is a patented, on-device human-state layer that derives behavioural and physiological state indicators from camera, voice and text signals.
Systems already see a lot. What they miss is the person, and that is the layer EmoPulse adds. A layer about people needs rules that hold even when nobody is watching.
Our design philosophy is simple:
Responsible behaviour should be enforced by architecture, not promised by policy.
Core Principle
The Person Remains In Control
- Explicit user consent required
- Processing runs locally by default
- No cloud dependency required
- Air-gapped operation supported
- User can terminate a session at any time
Privacy By Architecture
The default EmoPulse deployment model keeps computation entirely on the device.
On-device configuration:
- No video uploads
- No audio uploads
- No server-side biometric processing
- No cloud storage required
- No network connection required
Explainability
EmoPulse does not rely on opaque scores.
Examples include
- Cognitive workload indicators
- Stress-related indicators
- Behavioural consistency metrics
- Camera-derived physiological measurements
The objective is traceable state estimation, not black-box prediction.
Responsible Use
What EmoPulse Does
- Derives human-state signals
- Estimates behavioural and physiological indicators
- Measures multimodal coherence across face, voice and text
- Provides decision-support information
What EmoPulse Does Not Do
- Read minds
- Detect lies
- Determine intent
- Diagnose medical conditions
- Provide certainty about internal mental states
Outputs represent probabilistic estimates derived from observable signals. EmoPulse is not a medical or diagnostic device: readings come from camera-based signal estimation and are informational indicators, not clinical measurements.
Known Limitations
Performance may vary due to:
- Lighting conditions
- Camera quality
- Occlusion and movement
- Physiological variability
- Environmental conditions
Uncertainty is exposed through confidence estimates rather than hidden.
Bias mitigation and generalisation remain active areas of validation.
Regulatory Alignment
The architecture has been designed with:
- GDPR principles
- EU AI Act requirements
- Healthcare privacy considerations
- Enterprise auditability requirements
These represent architectural objectives and design choices, not completed external certifications.
Governance Commitments
EmoPulse is committed to:
- Ongoing validation and bias testing
- Transparent methodology
- Independent expert engagement
- Expanded user controls
- Deployment-specific safeguards
Our Position
Human-state systems should be:
Explainable, auditable and on-device by default.
The strongest safeguard is an architecture that minimizes data movement and maximizes user control.
The Human-State Layer
Privacy by Architecture
On-Device by Default
Protected by Patented Intellectual Property