Medical Mobility Robotics

AI Robotics Research — Sovereign AI models for adaptive control of medical mobility devices, rehabilitation robotics, and assistive manipulator systems.

Research Focus

This research area investigates how locally-deployed AI models can enhance robotic mobility and rehabilitation systems. The core technical challenge: robotic assistive devices must make real-time control decisions (10-50ms loops) while adapting to individual patient biomechanics — and all processing must happen on the device or on a facility-local server, with zero patient data transmitted externally.

Key Research Questions

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Adaptive Gait Analysis

Can quantized local AI models (4-bit/8-bit) provide real-time gait analysis with accuracy comparable to cloud-based systems, running on embedded robotics compute hardware?

Model quantization for embedded deployment (target <4GB VRAM)
Real-time inference optimization (target 10-50ms per frame)
Individual gait pattern adaptation via local fine-tuning
Multi-sensor fusion (IMU, pressure, optical) with local processing

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Rehabilitation Progression AI

Can AI models track rehabilitation progress and adjust robotic assistance levels in real-time, using only locally-computed metrics and without cloud-based model updates?

Temporal progression modeling (recovery trajectory prediction)
Adaptive assistance adjustment (autonomous level tuning)
Clinician-facing explainable progress dashboards
Sovereign RAG integration with treatment protocols

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Federated Robotic Learning

Can multiple rehabilitation robots at different facilities collaboratively improve their AI models via federated learning, sharing only model updates (not patient data) across sites?

Differential privacy in model update sharing
Secure aggregation for multi-facility training
Heterogeneous hardware handling across sites
Bandwidth-efficient synchronization for rural facilities

Technical Infrastructure

Model training and evaluation uses our multi-GPU NVIDIA CUDA research infrastructure:

Training: LoRA/QLoRA fine-tuning on NVIDIA CUDA GPUs for domain-specific adaptation
Quantization: Model compression pipeline for deployment to embedded robotics compute
Evaluation: Local model evaluation with medical imaging and clinical documentation datasets
Deployment target: Embedded GPU compute or facility-local inference server
Data sovereignty: All training data, evaluation data, and model weights remain on local hardware

Alignment with Parent Division

This robotics research is a sub-section of the Medical Mobility & Rehabilitation AI division. It extends the division's research into the physical robotics domain — translating sovereign AI models into real-time control systems for assistive devices.

← Back to AI Robotics | ← R&D Division