Project

Automatically monitoring home care patients with machine vision

Goal: Building a device for automatically monitor home care patients.

Methods: Computer Vision, Machine Vision, Python, OpenCV, Home Care, Raspberry Pi, Fall Detection

Date: 1 March 2016 - 2 December 2016

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Project log

Kim Salmi
added a research item
The world has a problem: hospitals are filling up. That is why elderly people need to live longer at home. But they need to do so safely. This is problematic because they are afraid of falling. They need automatic monitoring to feel safe at home. This thesis proposes an automatic monitoring system for formal and informal home care patients and care centers. The proposed system will provide security and a feeling of safety by detecting when a resident suffers from a fall. This feeling of safety can increase the persons ability to perform daily routines at home. After the detection the system will be able to alert professional personnel or family. The proposed system is affordable. This system uses computer vision to detect persons and their actions. The patient does not need to remember to put the detector on, charge it or anything else, which is convenient for the target group that suffers from dementia. This solution also does not need costly installations or costly hardware.
Kim Salmi
added an update
I made my thesis publicly available so that you can follow my writing progress here https://github.com/infr/falldetector-public
 
Kim Salmi
added an update
Here is an interview about the project (in Finnish) - Kimin konenäkö pelastaa kaatuneen vanhuksen http://www.haaga-helia.fi/fi/kimin-konenako-pelastaa-kaatuneen-vanhuksen-kim-salmi-agoedu-teknologiajohtaja
 
Kim Salmi
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Kim Salmi
added a project goal
Building a device for automatically monitor home care patients.