Spire Health Tag

Spire Health's technology offers a passive sensing solution for long-term patient monitoring. The Health Tags are designed to adhere to a patient's existing clothing, eliminating the need for charging or transferring between garments. These tags are durable, capable of withstanding washing and drying, and have a lifespan of up to 12 months.

The system includes a cellular-based Home Hub that automatically syncs data via Bluetooth, ensuring seamless data collection without requiring patient interaction. This setup prevents data loss and facilitates continuous monitoring. The data collected is shared with healthcare professionals through a dedicated dashboard, which triggers notifications if there are changes in a patient's health status.

The Health Tags capture signals with medical-grade accuracy, validated against clinical benchmarks for respiratory effort, activity, steps, and pulse rate. Advanced algorithms analyze these signals to learn individual health patterns, detecting deviations from clinical baselines. This allows for early detection of health changes, potentially identifying issues days before symptoms appear or physiological decline occurs.

Spire Health's technology offers a passive sensing solution for long-term patient monitoring. The Health Tags are designed to adhere to a patient's existing clothing, eliminating the need for charging or transferring between garments. These tags are durable, capable of withstanding washing and drying, and have a lifespan of up to 12 months.

The system includes a cellular-based Home Hub that automatically syncs data via Bluetooth, ensuring seamless data collection without requiring patient interaction. This setup prevents data loss and facilitates continuous monitoring. The data collected is shared with healthcare professionals through a dedicated dashboard, which triggers notifications if there are changes in a patient's health status.

The Health Tags capture signals with medical-grade accuracy, validated against clinical benchmarks for respiratory effort, activity, steps, and pulse rate. Advanced algorithms analyze these signals to learn individual health patterns, detecting deviations from clinical baselines. This allows for early detection of health changes, potentially identifying issues days before symptoms appear or physiological decline occurs.