- Abstract:
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Step counting from smart-phones allows a wide range of applications related to fitness and health. Estimating steps from phones' accelerometers is challenging because of the multitude of ways a smart-phone can be carried. We focus our work on the windowed peak detection algorithm, which has previously been shown to be accurate and efficient and thus suitable for mobile devices. We explore and optimise further the algorithm and its parameters making use of data collected by three volunteers ho...
Expand abstract - Publication status:
- Published
- Peer review status:
- Peer reviewed
- Version:
- Accepted Manuscript
- Files:
-
-
(pdf, 920.5KB)
-
- Publisher copy:
- 10.1109/EMBC.2018.8513319
-
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- Publisher:
- IEEE Publisher's website
- Pages:
- 4423-4427
- Publication date:
- 2018-10-29
- Acceptance date:
- 2018-04-18
- DOI:
- ISSN:
-
1558-4615
- Pubs id:
-
pubs:944600
- URN:
-
uri:b1b3cf44-8fa6-4866-9408-bb65129340a8
- UUID:
-
uuid:b1b3cf44-8fa6-4866-9408-bb65129340a8
- Local pid:
- pubs:944600
- ISBN:
- 978-1-5386-3646-6
- Copyright holder:
- IEEE
- Copyright date:
- 2018
- Notes:
-
Copyright © 2018 IEEE. This is the accepted manuscript version of the article. The final version is available online from IEEE at: https://doi.org/10.1109/EMBC.2018.8513319
Conference item
An optimised algorithm for accurate steps counting from smart-phone accelerometry
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Funding
NIHR Oxford Biomedical Research Centre
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