Review of Wearable Devices and Data Collection Considerations for Connected Health
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 increas...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/16/5589 |
| _version_ | 1797522057060155392 |
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| author | Vini Vijayan James P. Connolly Joan Condell Nigel McKelvey Philip Gardiner |
| author_facet | Vini Vijayan James P. Connolly Joan Condell Nigel McKelvey Philip Gardiner |
| author_sort | Vini Vijayan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T08:23:12Z |
| format | Article |
| id | doaj.art-28a1b2f7dd0f49988caad71daaefd208 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T08:23:12Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-28a1b2f7dd0f49988caad71daaefd2082023-11-22T09:42:24ZengMDPI AGSensors1424-82202021-08-012116558910.3390/s21165589Review of Wearable Devices and Data Collection Considerations for Connected HealthVini Vijayan0James P. Connolly1Joan Condell2Nigel McKelvey3Philip Gardiner4Computing Department, Letterkenny Institute of Technology, F92 FC93 Letterkenny, IrelandComputing Department, Letterkenny Institute of Technology, F92 FC93 Letterkenny, IrelandSchool of Computing, Engineering & Intelligent System, Ulster University Magee Campus, BT48 7JL Londonderry, IrelandComputing Department, Letterkenny Institute of Technology, F92 FC93 Letterkenny, IrelandRheumatology Department, Altnagelvin Hospital, Glenshane Road, BT47 6SB Londonderry, IrelandWearable 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.https://www.mdpi.com/1424-8220/21/16/5589wearable technologydigital healthcarequantified self (QS)deep learning (DL)neural network (NN) |
| spellingShingle | Vini Vijayan James P. Connolly Joan Condell Nigel McKelvey Philip Gardiner Review of Wearable Devices and Data Collection Considerations for Connected Health Sensors wearable technology digital healthcare quantified self (QS) deep learning (DL) neural network (NN) |
| title | Review of Wearable Devices and Data Collection Considerations for Connected Health |
| title_full | Review of Wearable Devices and Data Collection Considerations for Connected Health |
| title_fullStr | Review of Wearable Devices and Data Collection Considerations for Connected Health |
| title_full_unstemmed | Review of Wearable Devices and Data Collection Considerations for Connected Health |
| title_short | Review of Wearable Devices and Data Collection Considerations for Connected Health |
| title_sort | review of wearable devices and data collection considerations for connected health |
| topic | wearable technology digital healthcare quantified self (QS) deep learning (DL) neural network (NN) |
| url | https://www.mdpi.com/1424-8220/21/16/5589 |
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