A wearable continuously analyzes motion data locally to identify movement patterns associated with potential falls, enabling rapid detection without continuously transmitting raw sensor data to the cloud.
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 wearable pairs an IMU (accelerometer/gyroscope) with a Cortex-M-class MCU capable of running a lightweight inference model directly on-device, avoiding a dependency on continuous cloud connectivity for the time-critical detection path. A BLE link and companion mobile app handle configuration, event upload and caregiver alerting.
Raw IMU samples are processed locally through a feature-extraction and classification pipeline running entirely on-device; only detected events (not raw streams) are pushed over BLE to the phone and onward to the cloud, keeping the safety-critical detection loop independent of network conditions.
The central challenge was tuning the detection model for recall (catching real falls) while keeping false positives low enough that the device stays trustworthy for daily wear — a tradeoff validated against a labeled motion dataset covering both falls and fall-like everyday movements (sitting quickly, dropping the device, etc). Doing this inference within the power and compute budget of a small wearable, while keeping detection latency sub-second, shaped the model and firmware architecture.
On-device inference was chosen deliberately over a cloud-inference model so that detection latency and reliability don't depend on network availability — a hard requirement for a safety use case where the device may be relied upon in areas with poor connectivity.
