1. Capture Layer
Camera and microphone inputs collected locally for low-latency, privacy-preserving processing.
Technology
How A3CP turns multimodal signals into adaptive communication.
The Ability-Adaptive Augmentative Communication Platform (A3CP) translates complex research into a practical, open, and ethical communication system. It connects signal capture, classification, reasoning, and feedback in a modular way so that each part can be understood, audited, and improved over time.
Overview
A3CP combines gesture and sound recognition with human feedback to create an ability-adaptive communication system. Unlike traditional AAC tools that depend on symbol or text selection, A3CP learns directly from each person’s expressive movements, sounds, and context. Its open-source design ensures transparency, ethical oversight, and adaptability across care, education, and research environments.
Modular Architecture
A3CP is built as a modular framework: each component operates independently and communicates through shared data standards. This makes the system easier to audit, extend, and deploy across care, therapy, and educational environments.
2. Feature Extraction
Landmarks, movement features, and audio features converted into compact numeric vectors.
3. Classification
User-specific models generate intent predictions with calibrated confidence scores.
4. CARE Engine
Fuses predictions, checks uncertainty, and triggers caregiver clarification when needed.
5. Memory & Learning
Stores caregiver-confirmed examples so the system gradually adapts to the user’s patterns.
6. Interface Layer
Drives speech, text, or symbol output and can explain uncertainty or request confirmation.
The CARE Engine
The CARE Engine — Clarification, Adaptation, Reasoning, and Explanation — is the decision core that turns raw classifier outputs into safe, interpretable communication.
It fuses information from gesture, sound, and contextual modules, computes confidence, and decides when to ask for clarification. When confidence is low, it surfaces the most plausible interpretations for caregivers instead of committing to a single guess.
Each clarification becomes a structured training example, allowing the system to adapt to the individual user over time while keeping humans in control of final decisions.
Ethical and Edge-Capable Design
A3CP is engineered to run locally, preserve privacy, and stay open to inspection.
The platform is designed to run fully offline on affordable devices such as Raspberry Pi or Jetson Nano. This makes it viable for families, schools, and care homes without relying on commercial cloud services or permanent internet access.
Only derived feature data are stored — never raw video or audio — so that learning processes remain transparent and explainable. All code and documentation are open source, allowing others to review, replicate, or improve the platform.
Development Path
A3CP has progressed through successive prototypes toward a stable, deployable system.
Phase 1
Streamlit demonstrator validated feasibility of gesture capture, landmark visualization, and personalized training.
Phase 2
Modular FastAPI architecture established a scalable foundation for real-world deployment.
Phase 3 (2026)
Integration of gesture and sound classifiers, caregiver-in-the-loop training, and early pilot studies.
Future Possibilities
The adaptive architecture of A3CP creates new opportunities across creative, therapeutic, and research domains.
Because every interaction is logged as structured, interpretable data, A3CP can support new forms of learning, creativity, and clinical insight. The same modular pipeline that enables communication support can also power creative tools or research systems.
Visual concepts
These are suggested illustrations for this page and the broader GestureLabs visual language:
- Architecture diagram: A clean left-to-right flow showing Input → Feature Extraction → Classifiers → CARE Engine → Output → Feedback loop.
- Clarification loop: A small cycle diagram showing the system proposing an interpretation, caregiver confirmation/correction, and the model updating.
- Edge deployment graphic: A Raspberry Pi or tablet icon with arrows indicating on-device processing and a subtle “no cloud” implication (no cloud icon, no upload arrows).