A wearable medical patch continuously captures patient vital signs and securely transmits physiological data for remote monitoring, giving care teams a continuous view of patient status rather than relying only on intermittent manual measurements.
Every project follows the same Ryvasys Split — AI drafts the mechanical work, a named engineer reviews and owns every decision that touches safety, cost, or a regulatory limit.
Additional detail on architecture, stack and implementation specifics for this engagement.
The patch runs on a low-power, Cortex-M-class microcontroller paired with an analog front-end for capturing physiological signals (ECG/PPG-class sensing), with firmware structured around an RTOS to manage sampling, buffering and radio scheduling within a tight power envelope. A BLE radio handles the device-to-phone hop; a companion mobile app relays data over standard REST/HTTPS to a cloud ingestion service.
Sensor data is sampled continuously, filtered and windowed on-device to reduce the volume that needs to cross the radio link, then streamed over BLE in periodic batches to tolerate connection drops. On the backend, a device-to-cloud pipeline validates, timestamps and persists readings, exposing them through APIs consumed by the care-team-facing application.
The core tension was continuous multi-day wear against a small battery budget: every sampling-rate, filtering and radio-duty-cycle decision traded signal fidelity against power draw. Motion artifact rejection and reliable skin-contact sensing under real-world conditions (sweat, movement, adhesive wear) were the other major axis of engineering effort, alongside designing for graceful reconnection when BLE links drop.
An RTOS-based firmware architecture with aggressive duty-cycling was chosen over a bare-metal loop to make power-state management and sensor scheduling explicit and testable, which mattered given the multi-day continuous-wear requirement and the safety expectations around a medical monitoring device.
