RPEngineeringTech, Inc
Human Motion Intelligence Platform (HMIP)
RPEngineeringTech, Inc. is developing a patent-pending personalized human -motion intelligence platform that integrates wearable sensing, full-body motion capture, launch-monitor outcomes, synchronized video, biomechanical engineering, and AI inference to identify motion corridors, failure signatures, and recovery actions.
Engineering & AI Development Laboratory
Our laboratory integrates Rokoko full-body motion capture, wearable IMUs, GCQuad outcome measurements, Sony high-speed video, synchronization data acquisition, and biomechanical engineering analysis to develop and validate personalized AI inference engines.
HMIP Technology & Intelligence Architecture
HMIP combines synchronized sensor fusion, biomechanical engineering, personalized motion corridors, failure-signature detection, recovery intelligence, and AI inference. NVIDIA-based development will support model training, simulation, sensor-fusion research, and inference-engine development, while the wearable product is designed for personalized edge deployment.
Patent-Pending Technology & Intellectual Property
RPEngineeringTech, Inc. is developing patent-pending wearable and AI-enabled human-motion technology. The intellectual-property strategy covers personalized motion corridors, synchronized sensing, failure-signature detection, recovery intelligence, and edge-delivered feedback while protecting confidential implementation details.
Personalized AI Inference Engines
HMIP develops personalized inference engines from synchronized biomechanical and outcome data. The engines learn individual motion patterns, detect early divergence from the personalized GOOD corridor., classify failure signatures, and determine recovery actions. The architecture is designed to progress from laboratory training and validation to real-time wearable edge inference.
Prototype & Edge Deployment
HMIP is being developed toward a wearable prototype that can perform personalized motion-corridor monitoring, detect developing failure signatures, and provide timely recovery feedback.. Laboratory-trained inference models are intended for efficient edge deployment, enabling real-time operation while minimizing dependence on continuous cloud connectivity.