Field reliability improved from about 70% in the initial Gen 1 home device to about 96% by the final Gen 3 clinical study.
Medical devices
FlowSense Home / Lynx
Home-use hydrocephalus wearable advanced across 3 design generations, 100+ units, 200+ participants, 2,800+ wear hours, and 70% → 96% reliability improvement.
Part of Senior R&D Engineer · Rhaeos, Inc. · May 2022 – Dec 2025
completed 2023–2025 WearablesHome monitoringThermal sensingReliability
Type product
Skills Wearable sensing, Home-use workflow, Reliability, Data organization
Selected facts
Quantitative details and source-backed proof points.
Clinical-study build lots were typically 20–30 devices.
Build-cycle timeline improved from about 6 weeks in the Gen 1 period to about 3 weeks by the Gen 3 period.
Home platform scale: 3 design generations, 100+ manufactured units, 200+ participants, and 2,800+ home-device wear hours.
Home platform integrated modular electronics/sensor/battery architecture, Qi charging, motion sensing, onboard memory, data encryption, and remote data collection.
Work included hardware/software designs, BOMs, assembly procedures, design-verification protocols, DMFEA, patient/caregiver training, shipping, and remote-support workflows.
Project summary
Why it exists, what I built, and what I learned.
Why I built it
FlowSense needed to move from in-clinic spot checks into long-duration home monitoring for patients with implanted CSF shunts.
What I built
Wearable builds, adhesive/sensor layout iterations, modular electronics/sensor/battery architecture, Qi charging, motion sensing, onboard memory, data encryption, remote collection, and thermal visualization scripts.
What worked
Reliability improved because patient, caregiver, clinician, software, and hardware feedback directly informed form factor, placement, charging, adhesive, and data-quality decisions.
What failed
Home-use wearables exposed edge cases around placement, motion, charging, adhesive wear, shipping, support, and data quality that were not visible in lab use.
What I learned
Home monitoring is an integrated system problem: sensor performance, usability, reliability, and remote workflows have to improve together.
Stack
Tools, systems, and technical areas involved.
Wearable sensingQi chargingMotion sensingOnboard memoryData encryptionThermal mapsDMFEARemote support
Links and direction
Public links and next steps.
Keep the public summary focused on reliability, build cycle, home-use data scale, and workflow translation.
Related projects
Other projects in the same neighborhood.
Medical devices
FlowSense Clinical / ACE
Clinical wearable and DSP/ML workflow for noninvasive CSF shunt-flow assessment; improved from a 74% baseline to 0.81 AUC / 82% blinded validation accuracy.
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