Patient tracking with Raspberry Pi

Image Credit kevinpartner @ pixabay (Image altered and text added)

Mental health facilities face some of the greatest security challenges of any type of healthcare organization. Monitoring location of inmates is important to mental health and seniors care. An application that can track the location may be useful in non-intrusive security, measuring physical activity and in identifying subtle signs of depression. I always wanted to experiment with Raspberry Pi, and I feel location tracking would be an ideal project. Blue-tooth beacons are gaining in popularity with a decline in their prices. I have not yet gotten hold of both, but I thought I would create the plan here before I place my order. Let me name it piNEAPPLE and it would probably fit into the HL10 framework for behaviour intervention technologies.

First, let us get our requirements right. Let us call the person to be tracked, a ‘patient’ though the term may not be politically and technically correct. The patient is typically in a long term care facility, a mental health facility or home care. The caregivers need to keep track of the patient’s location over a period of time. The system should be able to track several patients. Essentially patient would be wearing a device that relays the identity and strategically placed receivers would record their presence. Though the opposite is also possible, we won’t be considering that option for now.

The patient will be wearing a wearable blue-tooth beacon. A beacon will relay the UUID to any blue tooth aware application. iBeacon is an Apple implementation of this, but we have cheaper options. We would use the raspberry pi as receivers. Each receiver will have a unique ID and will be placed in strategic locations. The receiver will be connected to the network. Every time the receiver detects a beacon, it will record the event by calling a RESTful application on a central server. The event notification shall include the UUID of the detected beacon, the ID of the receiver and the timestamp. The receiver will periodically rescan to see if the beacon is still within the range. The application will have a UI that can be viewed by an authenticated user.

Now on to the tools:

This article sums up the tools required and where to get them. We could use the general guidelines sans the proximity detection component. More details on Model B+ here. We will be using loopback to create the REST API for the backend. The Yeoman generator ‘generator-loopback’ will be used to scaffold the backend application. Install and run using:

npm install -g generator-loopback
yo loopback
node .
Cite this article as: Eapen BR. (July 30, 2015). - Tracking patients in mental health facilities using Raspberry Pi. Retrieved July 18, 2024, from

Do post your comments below.

Bell Eapen
Follow Me