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Are You in Pain? Predicting Pain and Stiffness from Wearable Sensor Activity Data

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Artificial Intelligence XXXVI (SGAI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11927))

Abstract

Physical activity (PA) is a key component in the treatment of a range of chronic health conditions. It is therefore important for researchers and clinicians to accurately assess and monitor PA. Although advances in wearable technology have improved this, there is a need to investigate PA in greater depth than the sum of its total parts. Specifically, linking deep PA data to patient outcomes offers a valuable, and unexplored use for wearable devices. As a result, this paper extracts useful features from accelerometer data (Actigraph GT3X Link), and applies machine learning algorithms to predict daily pain and stiffness. This was applied to a population of 30 arthritis patients and 15 healthy volunteers. Participants were provided with an Actigraph and asked to wear it continuously for 28 days. Results demonstrate that it is possible to predict both pain and stiffness of patients using the extracted accelerometer features.

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Notes

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    Currently ClinicalInk ENGAGE: https://www.clinicalink.com/engage/.

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Author information

Authors and Affiliations

  1. Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland

    Niladri Sett, Brian Mac Namee, Brian Caulfield, John Costello, Alison Keogh & Killian McManus

  2. Digital Development, Novartis Pharma AG, Basel, Switzerland

    Francesc Calvo, Jonas F. Dorn, Emer O’Hare & Caroline G. M. Perraudin

  3. Tallaght Hospital, Trinity College Dublin, Tallaght, Ireland

    Seamas C. Donnelly & Ronan H. Mullan

  4. Telecom SudParis, Evry, France

    Louis Jeay

Authors
  1. Niladri Sett
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  2. Brian Mac Namee
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  3. Francesc Calvo
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  4. Brian Caulfield
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  5. John Costello
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  6. Seamas C. Donnelly
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  7. Jonas F. Dorn
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  8. Louis Jeay
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  9. Alison Keogh
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  10. Killian McManus
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  12. Emer O’Hare
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  13. Caroline G. M. Perraudin
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Corresponding author

Correspondence to Niladri Sett .

Editor information

Editors and Affiliations

  1. University of Portsmouth, Portsmouth, UK

    Max Bramer

  2. Middlesex University, London, UK

    Miltos Petridis

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Sett, N. et al. (2019). Are You in Pain? Predicting Pain and Stiffness from Wearable Sensor Activity Data. In: Bramer, M., Petridis, M. (eds) Artificial Intelligence XXXVI. SGAI 2019. Lecture Notes in Computer Science(), vol 11927. Springer, Cham. https://doi.org/10.1007/978-3-030-34885-4_15

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  • DOI: https://doi.org/10.1007/978-3-030-34885-4_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-34884-7

  • Online ISBN: 978-3-030-34885-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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