Prevention starts when you know the riskiest moments.
The ability to interpret how a patient intends to moves—not just whether motion occurred.
Data from wearables is continuosly captured.
Real time analysis identify patient safety compromise.
Early insights support intervention upstream of risk.
Deliver care in existing workflows.
Fall detection is too late. Through motion intelligence, earlier timing to fall risk events is possible. Identifying sit-up intents within 6 seconds gives care teams the best chance to deliver proactive care
Identifies sit-up intent within approximately 6 seconds, giving care teams earlier movement insight.
Delivers movement-intent notifications within existing workflows, helping care teams prioritize care.
Provides movement-related information that helps teams understand activity patterns during fall-prevention workflows.
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OK2Predict is designed to sit between wearable sensors and clinical workflows. The platform analyzes patient motion data, identifies meaningful movement patterns, and delivers real-time signals to the systems care teams already use.
44 high-risk patients across skilled nursing facilities.
~6 seconds from detected sit-up intent to signal delivery.
~0.1 false alerts per bed per day.x
*Based on evaluation conducted in five skilled nursing facilities. Results reflect observed system performance under defined conditions and depend on trained staff response, facility protocols, and clinical workflows.
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