Five synchronized signals.
One expert. Ten thousand cases.
A Humynex cognitive-motor surgical record is a time-indexed bundle of five signal streams captured during expert demonstration sessions. Phase 1 uses porcine tissue for MVP validation; cadaveric sessions under willed body program tissue use agreements follow as the dataset scales. No IRB required. No patient consent burden.
Video (RGB-D)
Egocentric + field view of the operative site and instrument at 30fps
Force / Pressure
Inline cannula pressure telemetry — the tactile signal driving tissue-plane decisions
Motion (IMU)
3-axis accelerometer & gyroscope on cannula handle; trajectory and velocity
Intent Narration
Expert surgeon describing decisions in real time — “reducing suction; deep fascia resistance”
Event Tags
Discrete skill-boundary labels: tissue_plane_transition, cannula_swap, abort_pass
Unfair advantages
that cannot be replicated.
“Ten thousand liposuction procedures performed — that depth of expertise is what makes me the expert operator, and what Humynex is now making machine-readable.”
Dr. Matlock is the primary data asset of Humynex. A board-certified physician and entrepreneur with an MBA from UC Irvine Paul Merage, he has performed over 10,000 liposuction procedures across four decades of practice and trained 435 surgeons in 46 countries in his pioneered techniques.
During Humynex cadaver capture sessions, Dr. Matlock performs expert demonstrations while narrating every operative decision in real time — tissue plane identification, suction pressure adjustment, cannula selection, abort criteria. That narration, synchronized to force and motion telemetry, is the supervisory signal that current surgical robotics AI programs cannot generate from simulation or clinical video.
He is also the President & CEO of his surgical practice at 433 N. Camden Dr., Suite 610, Beverly Hills, CA 90210 (1994–Present) and the Laser Vaginal Rejuvenation Institute of America (2003–Present) — two medical businesses he built and continues to operate.
“Thirty-five years building surgical infrastructure — now applying that experience to the AI frontier.”
Dr. Reich brings the operational architecture that transforms clinical expertise into scalable enterprise. A serial healthcare entrepreneur, he was the first in the United States to achieve Joint Commission accreditation for an Ambulatory Surgical Center — a milestone that set the national standard for outpatient surgical quality.
His career spans an extraordinary range of roles: medical consultant to James Burke, CEO of Johnson & Johnson in the 1970s; originating medical team of ABC’s Extreme Makeover (2001–2003); and principal investigator on original prostaglandin research conducted at Mulago Hospital and Makerere University in Uganda — one of only two centers worldwide selected for that work.
At Humynex, Dr. Reich oversees operations, partner relationships, and the business development infrastructure that will take the dataset from first capture to first commercial licensing agreement.
Rhea Huang is an ML scientist and engineer specializing in multimodal data infrastructure, real-time sensor pipelines, and production machine learning systems. Her work spans clinical data engineering — including published research in oncology EHR interoperability and medical image analysis — and applied reinforcement learning and imitation learning for high-throughput decision systems.
She brings direct experience with the synchronization, annotation, and schema design challenges central to expert-demonstrated robotics datasets. At Humynex, she leads ML architecture and the technical publication strategy for the cognitive-motor surgical dataset program.
From expert demonstration
to ML-ready training record.
The Humynex capture platform converts simultaneous sensor streams into structured, machine-learning-ready records through four sequential stages.
Expert Cadaver Demonstration
10,000-case surgeon performs the procedure with real-time think-aloud narration. Willed body program tissue use agreement. No IRB pathway required.
Multimodal Signal Capture
RGB-D camera, IMU on cannula handle, inline pressure transducer, directional microphone. Sub-$3,000 MVP hardware stack. Setup under 30 minutes.
Synchronization Engine
Hardware-level timestamp alignment across all five modalities. Automated quality checks flag drift events before data enters the training pipeline.
Structured Dataset Export
Time-indexed records: video frame + force + motion + narration + event tag, all aligned to the same timestamp. Direct ingestion by standard ML frameworks.
How existing sources compare
| Data Source | Expert Level | Intent / Decision Layer | Tissue Validity | Humynex Advantage |
|---|---|---|---|---|
| Public OR video recordings | Variable — includes trainees | ✕ None | ✕ Live patient; consent-limited | ✓ on all three |
| Dry-lab phantom studies | Mixed | ✕ None | ✕ Synthetic tissue; poor fidelity | ✓ on all three |
| da Vinci kinematic logs | ~ Surgeon-level | ✕ No narration or decision tags | ✕ Clinical; no cadaver control | ✓ adds cognitive layer |
| Simulation only | N/A | ✕ None | ✕ Sim-to-real gap unvalidated | ✓ real tissue + real expert |
| Humynex Dataset | ✓ 10,000-case expert | ✓ Full think-aloud + event tags | ✓ Willed body cadaver — anatomically valid | Unique in market |
A $97B market with
no independent data layer.
The AI in robot-assisted surgery segment is growing at 44% CAGR. Intuitive Surgical’s proprietary dataset is platform-locked and not commercially available. No independent company has published an expert-annotated, cadaver-validated surgical cognitive-motor dataset.
From $5.5B in 2024, growing at 44.3% CAGR — driven by AI-guided autonomy, intraoperative decision support, and training data platforms. Source: Fortune Business Insights, 2024.
Intuitive Surgical performed 2.68M procedures in 2024 — 17M total. 2,000+ U.S. hospitals operate robotic surgical systems. The hardware infrastructure is already deployed at scale.
The data infrastructure layer is unoccupied. No independent company has built an open, expert-annotated, cadaver-validated surgical cognitive-motor dataset. That is the gap Humynex fills.
Revenue pathways
Dataset Licensing — OEMs
Intuitive Surgical, Medtronic, Stryker, J&J (Ottava) — all developing next-generation AI-guided platforms that require expert decision-layer training data.
Academic Research Partnerships
Stanford CHARM, JHU LCSR, CMU Biorobotics — established surgical robotics labs with active need for annotated training data and limited access to expert operators.
Government Grants (Non-dilutive)
NIH SBIR Phase I (~$300K), DARPA RSTAS — directly aligned program areas with active funding cycles for surgical AI training data infrastructure.
Strategic Acquisition
A proprietary expert surgical dataset becomes a strategic acquisition target as OEMs compete on AI differentiation. Intuitive’s da Vinci 5 force feedback introduction signals this competitive vector.
The capture infrastructure is domain-agnostic.
Any field where expert human judgment must be transferred to an autonomous system faces the same fundamental problem: tacit knowledge that has never been structured as training signal. Surgical robotics is the Humynex beachhead — the most credible, most tissue-valid, most fundable entry point. But the same synchronized multimodal capture methodology — expert demonstration, real-time intent narration, force and motion telemetry — transfers directly to adjacent domains.
Surgical robotics first. The infrastructure scales.
Capture Protocol & Facility Access
Define ML schema · refine event tag vocabulary · execute willed body program tissue use agreement · procure MVP hardware (<$3K)
Cadaver Sessions
5–10 expert liposuction demonstration passes across anatomical variants · real-time think-aloud narration · synchronization validation
Dataset & Publication
Process recordings · write 6-page technical note · submit to ISMR 2026 or MICCAI workshop · post to arXiv simultaneously
Let’s talk
before the arXiv drops.
Humynex is raising $500K pre-seed. SAFE note, $4M cap, 20% discount. Target close Q2 2026. Seed trigger: published dataset paper + one OEM pilot LOI.