Review of techniques to automatically quantify movement using wearable sensor technology

Vini Vijayan , james connolly, Joan Condell, Nigel McKelvey, Philip Gardiner

Research output: Contribution to journalReview articlepeer-review

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Abstract

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 to 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. And 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.
Original languageEnglish
Pages (from-to)1
Number of pages26
JournalSensors
Publication statusAccepted/In press - 2 Aug 2021

Keywords

  • Digital Healthcare
  • Neural Network (NN).
  • Wearable technology
  • Deep Learning (DL)
  • Quantified Self (QS)

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