From physiological signals to product-ready monitoring

The challenge

Point-of-care monitoring has to turn noisy physiological signals into information that can support a usable product. ECG, PPG, temperature and oxygen-saturation streams differ in sampling, artefacts and interpretation, so a single modelling step cannot carry the whole system.

The work required an end-to-end view: how data was collected, how signals were filtered, what the models were evaluated against, and how outputs would move into mobile and cloud environments.

The approach

I led the applied R&D workflow from signal preparation through model development and evaluation. Reproducible experiments made it possible to compare decisions without losing the conditions under which a result had been produced.

Integration was treated as part of the research problem. Supporting Android and cloud delivery exposed practical constraints early and helped keep the analytical work connected to the intended monitoring experience.

• Defined repeatable collection and signal-processing workflows.
• Developed and evaluated time-series classification approaches.
• Documented assumptions and experiment conditions.
• Supported the path from model outputs to Android and cloud integration.

Evidence and limits

Project evaluation reached 94% ECG classification accuracy. That figure is useful evidence within its evaluation setting, not a substitute for describing the data, protocol and intended operating context.

The more durable outcome was a disciplined route from raw signal to evaluated component—one that made performance, integration and limitations visible together.