Abstract
This paper proposes an approach todetermining an occupant’s indoor location through the use of machine vision techniques combined with wearable computing. Based on “off-the-shelf” machine vision tools a system is introduced to obtain a user’s indoor location through the detection of “reference” objects in their immediate environment. This information is subsequently cross-referenced with a knowledge base containing details ofwhich rooms referencemarkers are located in.Details of the architecture required to realize the solution are presented which also accommodates for the fusion of information sources overcoming the heterogeneous nature of data gathered frommultiple sourceswithin the environment.The solution can be used to provide context aware assistance with Activities of Daily Living to those who may normally require assistance in their day-today life hence allowing them to live independently at home for longer.
| Original language | English |
|---|---|
| Title of host publication | International Workshop on Ambient Assisted Living |
| Pages | 195-202 |
| Volume | 8868 |
| ISBN (Electronic) | 978-3-319-13105-4 |
| DOIs | |
| Publication status | Published (in print/issue) - 2014 |
Publication series
| Name | Lecture Notes in Computer Science |
|---|---|
| Publisher | Springer |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Fingerprint
Dive into the research topics of 'Wearable computing to support activities of daily living'. Together they form a unique fingerprint.Student theses
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Leveraging machine-vision for activity recognition utilising indoor localisation to support aging-in-place
Shewell, C. (Author), Nugent, C. (Supervisor), Wang, H. (Supervisor) & Donnelly, M. (Supervisor), Oct 2023Student thesis: Doctoral Thesis
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