Review of Wearable Devices and Data Collection Considerations for Connected Health

Vini Vijayan, James P. Connolly, Joan Condell, Nigel McKelvey, Philip Gardiner

Research output: Contribution to journalReview articlepeer-review

106 Citations (Scopus)
166 Downloads (Pure)


Wearable sensor technology has gradually extended its usability into a wide range of well-known applications. Wearable sensors can typically assess and quantify the wearer’s physiology and are commonly employed for human activity detection and quantified self-assessment. Wearable sensors are increasingly utilised to monitor patient health, rapidly assist with disease diagnosis, and help predict and often improve patient outcomes. Clinicians use various self-report questionnaires and well-known tests to report patient symptoms and assess their functional ability. These assessments are time consuming and costly and depend on subjective patient recall. Moreover, measurements may not accurately demonstrate the patient’s functional ability whilst at home. Wearable sensors can be used to detect and quantify specific movements in different applications. The volume of data collected by wearable sensors during long-term assessment of ambulatory movement can become immense in tuple size. This paper discusses current techniques used to track and record various human body movements, as well as techniques used to measure activity and sleep from long-term data collected by wearable technology devices.
Original languageEnglish
Article number5589
Pages (from-to)1-31
Number of pages31
Issue number16
Early online date19 Aug 2021
Publication statusPublished online - 19 Aug 2021

Bibliographical note

Funding Information:
Funding: This research was funded by Letterkenny Institute of Technology (LYIT), Co., Donegal, Ireland.

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.


  • wearable technology
  • digital healthcare
  • quantified self (QS)
  • deep learning (DL)
  • neural network (NN)
  • Neural network (NN)
  • Quantified self (QS)
  • Deep learning (DL)
  • Digital healthcare
  • Wearable technology


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