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Open Access

Peer-reviewed

Research Article

Segmenting accelerometer data from daily life with unsupervised machine learning

Dafne van Kuppevelt,

Roles Conceptualization, Methodology, Software, Writing – original draft

Affiliation Netherlands eScience Center, Amsterdam, The Netherlands

http://orcid.org/0000-0002-2662-1994

⨯
Joe Heywood,

Roles Data curation, Resources

Affiliation Centre for Longitudinal Studies, UCL Institute of Education, London, United Kingdom
⨯
Mark Hamer,

Roles Writing – review & editing

Affiliation School Sport, Exercise and Health Sciences, Loughborough University, Loughborough, United Kingdom
⨯
Séverine Sabia,

Roles Writing – review & editing

Affiliations INSERM, U1018, Centre for Research in Epidemiology and Population Health, Paris, France, Department of Epidemiology and Public Health, University College London, London, United Kingdom
⨯
Emla Fitzsimons ,

Roles Data curation, Funding acquisition, Supervision

* E-mail: e.fitzsimons@ucl.ac.uk

Affiliations Centre for Longitudinal Studies, UCL Institute of Education, London, United Kingdom, Institute for Fiscal Studies, London, United Kingdom
⨯
Vincent van Hees

Roles Conceptualization, Methodology, Supervision, Writing – original draft

Affiliation Netherlands eScience Center, Amsterdam, The Netherlands
⨯

Segmenting accelerometer data from daily life with unsupervised machine learning

Dafne van Kuppevelt,
Joe Heywood,
Mark Hamer,
Séverine Sabia,
Emla Fitzsimons,
Vincent van Hees

x

Published: January 9, 2019
https://doi.org/10.1371/journal.pone.0208692

Reader Comments

Post a new comment on this article

Software references

Posted by VvanHees on 16 Jan 2019 at 08:02 GMT

Literature references 32 and 33 are references to software on GitHub, but they miss the actual url:
Reference 32 should refer to: https://github.com/wadpac...
Reference 33 should refer to: https://github.com/wadpac...

No competing interests declared.

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Accelerometers
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Physical activity
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Statistical distributions
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Bioenergetics
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Hidden Markov models
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Unsupervised machine learning
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