Wearable data,
ready for analysis.
From device SDK to analysis-ready dataset. Seigyonics designs and deploys wearable data pipelines for pharmaceutical sponsors and NIH-funded research teams — so your data meets protocol before your first interim analysis.
Your devices collect data. Nobody structures it.
Device SDK exports arrive in proprietary formats that don't match your analysis protocol
Multi-site studies produce the same measure four different ways — discovered at interim analysis, not ingestion
Compliance and adherence gaps surface too late because no automated QC watches the incoming stream
The grant budget has no line item for a data engineer, so the PI or a postdoc does it manually
Device → Ingestion → Validation → Analysis-Ready Dataset
- ( 01 )
Device & Protocol Alignment
We map your wearable devices, SDKs, and study protocol into a unified data model. Every variable, every time resolution, every site — aligned before data collection begins.
- ( 02 )
Automated Ingestion
Raw device exports are ingested, parsed, and transformed on arrival. No manual file-handling, no spreadsheet staging. Multi-site data flows into a single structured pipeline.
- ( 03 )
Validation & Quality Control
Deterministic QC algorithms check every record against protocol specifications: wear-time compliance, signal quality, physiological range checks, cross-device consistency. Flags surface in real time, not at study end.
- ( 04 )
Feature Extraction & Derived Measures
We compute analysis-ready variables — sleep metrics, activity counts, heart rate variability, digital biomarkers — using validated, reproducible algorithms. No black-box processing.
- ( 05 )
Analysis-Ready Dataset & Archive
Clean, documented, versioned datasets delivered in your analysis tool's native format. Accompanied by a data dictionary and full processing provenance for regulatory or publication audit.
Fixed scope. Written deliverables. Your data, your IP.
Every engagement has a written statement of work with defined deliverables and milestones. No open-ended hourly billing.
All data, code, and derived datasets belong to you. Seigyonics retains no rights to your study data.
Pipeline services can be written directly into NIH grant budgets under the 2026 Data Management & Sharing policy. We've done this on 7 R01 applications.
For multi-year studies, ongoing data operations retainers keep the pipeline running and adapting as the protocol evolves.
Start small. Scale on proof.
Fixed scope, fixed price, written deliverables. No open-ended consulting.
Protocol & Pipeline Design
A written technical plan mapping your devices, protocol, and data flow — before you write the first line of pipeline code.
- —Device SDK & data format audit
- —Unified data model aligned to protocol
- —Pipeline architecture & QC specification
Pipeline Build & Validation
The full pipeline: ingestion, QC, feature extraction, and analysis-ready dataset delivery — deployed and validated against your study data.
- —Automated ingestion from device SDKs
- —Deterministic QC & compliance checks
- —Analysis-ready dataset with documentation
Study-Duration Data Ops
Ongoing pipeline operations for the life of your study — so data quality never quietly degrades between enrollment and lock.
- —Pipeline monitoring & incident response
- —Protocol amendment adaptations
- —Interim dataset delivery & QC reporting
Research teams: data management services can be budgeted directly into NIH grant applications under the 2026 Data Management & Sharing policy. Ask us how.
Pin-Wei Chen, PhD
Healthcare data scientist and senior ML scientist focused on large-scale data architecture and production pipeline deployment. If your data can meet clinical research standards, it can power reliable AI for your business.
Why Trust Seigyonics With Your Data
Tell us about your study, we'll tell you what it takes.
Share your study design, devices, and timeline. You'll get an initial read within 48 hours — what the pipeline looks like, a rough scope, and an honest take on whether we're the right fit.