Working in collaboration with:

Phase 3. Final validation studies.

With optimization studies completed, the FallCall Detect team spent months validating its fall detection algorithm. Thousands of fall events were recorded using mannequin and human subjects wearing Apple Watches on each wrist. Various hand/arm locations were tested from the sitting and standing positions. Fall detection was able to capture up to 90%+ of higher impact standing falls and up to 80% of lower impact sitting falls with up to 70% differentiation accuracy depending on fall mechanics. Additionally, human subjects wore Apple Watches daily to determine false activation rates based on normal activity and exercise activities. Using daily usage data combined with lab data, adjustable sensitivities were determined to maximize fall detection sensitivity and fall differentiation accuracy while minimizing false activations.