Creation of a system that offers real-time human activity recognition within the clinical systems to provide better diagnosis & treatment for critical care patients
The ML-based healthcare monitoring system offers actionable insights to help collate the recorded data (historical & current health data sets) from various connected devices along with the patient’s vitals.
Readings are collected from various sources, including historical records, prescription, wearable patient sensors, and even equipment such as arterial blood gas (ABS) analyzer, echocardiography, and more
The simplified process helps convert varied data sets across different device protocols in a unified format for further analysis, reporting, diagnosis and, sometimes, treatment
The system classifies the activities performed & recorded by different patients in a continuous sequence – while they are sleeping, sitting, being fed, walking, and more
The AI-powered patient monitoring system offers real-time insights and alerts about health & performance, ensuring instant data interaction with actionable outputs
We created ML-based data models for the connected devices to enable activity recognition through a secure & reliable patient health monitoring system. The model enables the medical care provider to record & analyze large data sets of patient health in real-time to make informed decisions.
The data set was divided into observation cycle windows of one second with a 50% overlap and further deployed into the AI system at scale on users’ devices
The model extracts the data for the mapping & analysis of the patient’s health status through connected devices, including a heart rate sensor, VO2, ECG, and more
The customized interface helps incorporate metrics beyond traditional activity identification models, and creates dashboard based on various parameters such as gender, age, illness, treatment group, and more
It enables doctors to easily track & identify patterns of multiple patient activities and define an accurate, pre-emptive diagnosis of ailments