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      Review of Wearable Devices and Data Collection Considerations for Connected Health.

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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 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.

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          Author and article information

          Journal
          Sensors (Basel)
          Sensors (Basel, Switzerland)
          MDPI AG
          1424-8220
          1424-8220
          Aug 19 2021
          : 21
          : 16
          Affiliations
          [1 ] Computing Department, Letterkenny Institute of Technology, F92 FC93 Letterkenny, Ireland.
          [2 ] School of Computing, Engineering & Intelligent System, Ulster University Magee Campus, BT48 7JL Londonderry, Ireland.
          [3 ] Rheumatology Department, Altnagelvin Hospital, Glenshane Road, BT47 6SB Londonderry, Ireland.
          Article
          s21165589
          10.3390/s21165589
          8402237
          34451032
          90ae0a04-7450-46b7-9fad-e7807523910a
          History

          deep learning (DL),digital healthcare,neural network (NN),quantified self (QS),wearable technology

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