Brining it all together: wearable data fusion

Yunus Celik, Alan Godfrey*

*Corresponding author for this work

Research output: Contribution to journalEditorialpeer-review

5 Citations (Scopus)
15 Downloads (Pure)

Abstract

Contemporary wearables like smartwatches are often equipped with advanced sensors and have associated algorithms to aid researchers monitor physiological outcomes like physical activity levels, sleep patterns or heart rate in free living environments. But here's the catch: all that valuable data is often collected separately because the sensors don't always play nice with each other, and it's a real challenge to put all the data together. To get the full picture, we may often need to combine different data streams. It's like putting together a puzzle of our health, instead of just looking at individual pieces. This way, we can gather more useful info and better understand health (it's called digital twinning). Yet, to do so requires robust sensor/data fusion methods at the signal, feature, and decision levels. Selecting the appropriate techniques based on the desired outcome is crucial for successful implementation. An effective data fusion framework along with the right sensor selection could contribute to a more holistic approach to health monitoring that extends beyond clinical settings.
Original languageEnglish
Article number149
Number of pages3
Journalnpj Digital Medicine
Volume6
Issue number1
DOIs
Publication statusPublished - 17 Aug 2023

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