Thesis Topic Details
Topic ID: |
3262 | |
Title: |
Unobtrusive fall detection at home using the Microsoft Kinect device | |
Supervisor: |
Stephen Redmond | |
Research Area: |
Image Processing, Algorithms | |
| Associated Staff | ||
|---|---|---|
Assessor: |
Tim Moors | |
| Topic Details | ||
Status: |
Active | |
Type: |
Research | |
Programs: |
CS CE BIOM BINF SE | |
Group Suitable: |
Yes | |
Industrial: |
No | |
Pre-requisites: |
-- | |
Description: |
Microsoft's Kinect device performs depth mapping using a 3D sensor, records RGB video and contains a microphone array for directional listening. This project proposes to test existing tracking algorithms written for this device, and to test novel methods, aimed at recognising when an elderly person has fallen in their home. Such an unobtrusive falls detection approach is preferable existing solutions which require the individual to wear a motion sensing device, or panic alarm, as such personal monitors are often not worn, either through forgetfulness or by choice. | |
Comments: |
-- | |
| Past Student Reports | ||
| Esther MOSAD in s2, 2012 Unobtrusive fall detection at home using the Microsoft Kinect device |
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Download report from the CSE Thesis Report Library NOTE: only current CSE students can login to view and select reports to download. | ||