Real-time Health Dashboard with Wearable Sensors

We built a real-time health monitoring dashboard powered by our proprietary wearable sensor platform and AI algorithms, designed for continuous patient observation in hospitals, clinics and at home. Multiple body-worn sensors stream data such as movement, temperature and heart-related metrics to a gateway or mobile device, where our system detects falls, monitors activity patterns (walking, resting, sleeping) and raises alerts when something looks wrong.

Clinicians and caregivers see each patient’s status in a clear, unified view: current state, trends over time and notifications about potential risks. The solution is flexible enough to run fully connected with hospital systems or in offline mode at the edge, which is critical for home care, rural environments or situations where connectivity is unreliable.

This health dashboard forms the foundation for remote patient monitoring, early detection of deterioration and safer rehabilitation, while also reducing the need for constant one-to-one observation.

What this solves

Healthcare teams are under constant pressure to monitor more patients with limited staff and time. Traditional observation methods rely on occasional manual checks, basic alarms or patient self-reporting, which can miss early warning signs and create a high risk of unnoticed falls, wandering or sudden changes in condition. Remote care is even more challenging when internet connectivity is unstable, but safety must still be maintained.

Our platform tackles these challenges by turning continuous sensor streams into actionable insights. The wearable devices capture movement and physiological signals; our algorithms classify activities such as walking, sitting, lying and sleeping, detect falls and unusual inactivity, and track metrics such as temperature and overall activity level. The dashboard summarises this into simple traffic-light statuses, trend graphs and alert lists, helping medical staff and caregivers focus attention where it’s needed most and supporting safer, more scalable care.

How we did it

We designed the solution around a proprietary wearable sensor app that communicates with a mobile device or local gateway, which can operate fully offline if required. A data processing pipeline on the device or edge gateway cleans and aggregates sensor data in real time, runs fall detection and activity recognition models, and manages local alerting even without a cloud connection. When connectivity is available, data is synchronised with a central backend and visualised in a web-based dashboard.

The dashboard provides individual and ward-level views: current posture and activity state, fall events, temperature trends, sleep patterns and configurable KPIs for each patient group. Role-based access control and audit trails support clinical workflows, while the flexible architecture allows integration with hospital information systems or home-care platforms. The same core technology can be extended to additional health metrics and disease-specific monitoring scenarios, making it a robust building block for digital health ecosystems.