Kinetic Context Engine (KCE)

⚠️ ETHICAL DISCLAIMER & LIMITATIONS

This software is a Proof of Concept (PoC) for multi-modal sensor fusion and signal processing. It is designed for research and educational purposes only.

• Not a Truth Verifier: This system detects stress indicators (physiological and behavioral), which correlate with but do not prove deception. Innocent individuals may trigger high stress scores due to anxiety, and practiced liars may trigger low scores.
• No Legal/Medical Standing: The output of this software should never be used for legal, employment, medical, or interpersonal decision-making.
• Bias Warning: The underlying models (MediaPipe, Whisper) and heuristic rules may exhibit bias across different demographics, accents, or neurotypes.

Digitizing Human Non-Verbal Communication

Vision

The Kinetic Context Engine (KCE) is a distributed, event-driven system designed to bridge the gap between Sensation (raw video/telemetry) and Perception (semantic meaning).

Instead of treating video analysis as a black box, KCE decomposes streams into atomic signals (micro-expressions, body language) and reconstructs them into context-aware meanings using a transparent, configurable rules engine.

Why Open Source?

We believe that digitizing human behavior requires a global, collaborative effort. We have open-sourced the core engine to solve two fundamental challenges identified in our Strategic Analysis:

1. The "Dictionary Problem" (Crowdsourcing Perception)

Building a comprehensive library of human non-verbal communication is too complex for a single team.

• The Goal: We provide the structure (rules.json); we ask behavioral scientists, psychologists, and researchers to fill it.

• The Ask: Submit Pull Requests defining new gestures (e.g., "Eyebrow Flash", "Defensive Crossing") mapping signals to meaning.

2. Universal Sensor Integration (Crowdsourcing Sensation)

Computer Vision evolves rapidly. Today the standard is MediaPipe; tomorrow it might be something else.

• The Goal: A modular SensorInterface that abstracts the "How" (CV library) from the "What" (Signal).

• The Ask: CV Engineers can write simple adapters for new libraries (MMPose, OpenFace, YOLO) to make them compatible with the KCE ecosystem.

3. Ethical Transparency

In the field of Affective Computing, "Black Boxes" create distrust. By open-sourcing our interpretation logic, we ensure that the system's decisions—how it reads a "smile" or a "frown"—are auditable, verifiable, and free from hidden biases.

Architecture

The system follows a Microservices Architecture powered by Apache Kafka:

1. Ingestion Layer: API Gateway accepting video/telemetry.

2. Sensation Layer (AI Workers): Modular workers (currently MediaPipe) that extract atomic signals (e.g., eyebrow_raise, intensity: 0.8).

3. Perception Layer (Context Adapter): The "Brain" that subscribes to signals and applies the rules.json logic to infer meaning (e.g., "Friendly Greeting").

4. Infrastructure: Kafka (Streaming), Redis (State/Task Queue), and Celery (Orchestration).

Upcoming: We are currently migrating the orchestration layer from Docker Compose to HashiCorp Nomad for distributed resilience.

Getting Started

Prerequisites

• Docker & Docker Compose

• Python 3.9+

Installation

1. Clone the repository:

   git clone [https://github.com/your-org/kinetic-context-engine.git](https://github.com/your-org/kinetic-context-engine.git)
   cd kinetic-context-engine