Smartwatch based Respiratory Rate and Breathing Pattern Recognition in an End-consumer Environment
| Autor: | Gerald Bieber, Hannes Schenk, John Trimpop, Friedrich Lämmel, Paul Burggraf |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Respiratory rate
business.industry Computer science 010401 analytical chemistry Wearable computer 020206 networking & telecommunications 02 engineering and technology Accelerometer 01 natural sciences 0104 chemical sciences Health data Smartwatch Breathing pattern Human–computer interaction 0202 electrical engineering electronic engineering information engineering Applied research business Wearable technology |
| Zdroj: | iWOAR |
| DOI: | 10.1145/3134230.3134235 |
| Popis: | Smartwatches as wearables became part of social life and practically and technically offer the possibility to collect medical body parameters next to usual fitness data. In this paper, we present an evaluation of the respiratory rate detection of the &gesund system. &gesund is a health assistance system, which automatically records detailed long-term health data with end-consumer smartwatches. The &gesund core is based on technology exclusively licensed from the Fraunhofer Institute of applied research. In our study, we compare the &gesund algorithms for respiration parameter detection in low-amplitude activities against data recorded from actual sleep laboratory patients. The results show accuracies of up to 89%. We are confident that wearable technologies will be used for medical health assistance in the near future. |
| Databáze: | OpenAIRE |
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