- How to Use EMG Muscle SensorPosted 5 hours ago
- WiFi Christmas TreePosted 1 day ago
- James Bruton’s 3D-printed, Arduino-powered Nerf blaster fires 10 darts at oncePosted 1 day ago
- Shimmering Snowflake PCB OrnamentPosted 1 day ago
- Arduino Buzzer/Light Temperature Alert Hat PrototypePosted 2 days ago
- LED-based Christmas ornamentsPosted 3 days ago
- Stream & Visualize MATRIX Creator Sensor Data with Swim.aiPosted 4 days ago
- Improve human-robot collaboration with GhostARPosted 4 days ago
- ISS Tracking LampPosted 5 days ago
- An Arduino-powered fishing pole with automatic casting and reelingPosted 5 days ago
Remote Patient Monitoring? Now It’s Possible!
The Connected Health project aims to bring vital sign monitoring to the masses with a simple, inexpensive unit built around commodity hardware. This monitoring system is connected to the Internet, which enables remote patient monitoring.
They developed the HealthyPi HAT for the Raspberry Pi as a way of opening up the healthcare and open source medical to anyone.
HealthyPi is a HAT for the Raspberry Pi that turns it into a full-featured human vital signs monitor.
“Our objective when we began developing the HealthyPi was to make a simple vital sign monitoring system which is simple, affordable, open-source (important !) and accessible. For the sake of reproducibility, the entire PCB design is only 2-layers and can be opened/edited in the free version of Eagle. The BOM count also has been intentionally kept low”
For this application the makers used a Raspberry Pi 3, because it is affordable, easy-to-use and accessible: you can get a Raspberry Pi easily from any corner of the globe and a wide variety of support is available.
The most important part of the whole development is that HealthyPi is completely open-source, both Hardware and Software alike. All our code and hardware schematics are available on GitHub.
Another project’s focal point is the cloud connectivity. By collecting data for high-risk patients inside or outside a healthcare, eventualities can be prevented. This is possible by analyzing a large amount of historical data and use machine learning to learn from the data for predictive analysis. Is also possible the diagnosis of patient health from a healthcare professional in a remote location.
