Medical IoT and connected medical device development — sensor to cloud, one team.
Healthcare IoT and remote patient monitoring device companies have long paid a hardware house, a firmware contractor and a cloud team separately — and paid again for the gaps between them. Rivix is the team that closes those gaps, with AI doing the heavy lifting that used to eat months, and a named senior engineer accountable end-to-end.
One team across the stack for connected health devices.
Hardware, firmware, cloud and mobile get built by one accountable team, with regulatory process and PHI handling designed in from the architecture stage, not bolted on after.
- Embedded Hardware
- Firmware
- Cloud & Device Platforms
- Mobile & Web Apps
- AI & Intelligence
Where AI accelerates
- Component selection
- Driver scaffolding
- Schema drafts
- Model integration & inference pipeline code
Where it stays human
- DFM review
- Field-failure triage
- Data-retention & compliance decisions
- Bias & fairness review
The full stack, scoped for a connected health product.
Embedded Hardware
Sensing, analog front-ends and low-power MCU selection for wearable and patch form factors.
Explore hardware →Firmware
RTOS-managed sampling, buffering and radio scheduling within a tight power envelope; secure BLE.
Explore firmware →Cloud & Device Platforms
Data pipelines from device to storage, designed with compliance considered at every hop.
Explore cloud →Mobile & Web Apps
Companion apps for patients and care teams, talking directly to the device underneath.
Explore mobile & web →AI & Intelligence
On-device inference for time-critical detection paths, avoiding a dependency on continuous cloud connectivity where it matters most.
Explore AI & Intelligence →Medical Device Firmware Development
IEC 62304-aligned process, ISO 14971 risk management and fail-safe firmware patterns, in one combined overview.
Explore medical device firmware →Wearable Medical Device Development
Form factor, power budget and on-body sensing, specific to wearable medical and health devices.
Explore wearable medical devices →What actually shapes a connected health device architecture.
Regulatory process, not just paperwork
Two standards govern the build, not one checklist.
- IEC 62304 — software lifecycle rigor scaled to safety class, full traceability from requirements to verification evidence
- ISO 14971 — risk management: hazard ID, risk estimation, controls, extended to connectivity loss, cloud outages and cybersecurity threats
HIPAA-relevant data handling
PHI gets protected at every hop, not just at rest.
- Business Associate Agreement required with any vendor touching PHI
- TLS in transit; encryption at rest on device, in transit queues, and in cloud databases
- Role-based access scoped to minimum need, with audit logging of PHI access
Fail-safe firmware patterns
The device has to fail safely, not just work.
- Hardware watchdog timer resets the device on a firmware hang
- Periodic self-tests: sensor plausibility, battery/storage range, wireless-stack liveness
- Critical state written to non-volatile memory often enough to survive power loss, structured against mid-write corruption
What the hardware and firmware look like in practice
Two real builds, two different tradeoffs.
- Fall-detection wearable — IMU + Cortex-M-class MCU, on-device inference, sub-second latency, only classified events sent over BLE
- Vitals patch — Cortex-M-class MCU + analog front-end for ECG/PPG sensing, RTOS scheduler separating sampling/radio/power, BLE 5.x to the app, REST/HTTPS to the cloud
Real builds in health and medtech.
Pulled from our case-study library — same AI/human split shown in full, industry context included.
On-Device Early Fall-Detection Wearable
US-based aging-in-place / digital-health wearable startupA wearable continuously analyzes motion data locally to identify movement patterns associated with potential falls, without continuously transmitting raw sensor data to the cloud.
Remote Patient Vitals Patch
US-based digital-health / remote-patient-monitoring startupA wearable medical patch continuously captures patient vital signs and securely transmits physiological data for remote monitoring.
AI-Personalized Wearable Sleep Coach
US-based consumer wellness / wearable startupA wearable captures sleep and physiological signals and uses AI to transform longitudinal data into personalized sleep insights and recommendations.
What we've written about regulatory process and fail-safe firmware.
HIPAA-Compliant Data Handling for Health Devices
Every hop a device's data takes is a place compliance can be designed in or quietly left out.
Read the article →Regulatory Basics for Connected Medical Devices
Regulatory strategy determines architecture and process from the first design decision, not paperwork layered on after.
Read the article →Reliability and Fail-Safe Design Principles for Medical Wearables
In a medical wearable, the failure case has to be designed first, not handled as an afterthought.
Read the article →What people ask before scoping a health device.
Do you design for HIPAA-relevant data handling?
Every hop a health device's data takes — device, transmission, storage — is a place compliance can be designed in or left out. We design it in from the architecture stage.
What's the AI/human split for a medical-grade product?
AI accelerates model-integration code and data pipelines; a senior engineer owns model selection, accuracy/recall tradeoffs and false-positive management — the calls that actually determine whether the feature works in the field.
Can firmware failure modes be designed for, not just handled after the fact?
Yes — in a medical wearable, a firmware bug can mean a missed alert or lost readings at the moment they mattered most, so the failure case is designed first, not handled as an afterthought.
Do you handle device certification too?
We design for pre-compliance from the first revision — EMC-aware layout, component margins and test-point planning — and coordinate with your certification lab through submission.