Why the study?
Wearable devices face problems of excessive error when detecting human anxiety, requiring improved algorithms for accurate ECG measurement and heart rate variability analysis.
Does a DBSCAN-based algorithm improve the accuracy of anxiety detection and ECG measurement in wearable devices compared to traditional algorithms?
Does a DBSCAN-based algorithm improve the accuracy of anxiety detection and ECG measurement in wearable devices compared to traditional algorithms?
A novel DBSCAN-based algorithm improves the accuracy of anxiety detection and ECG measurement in civilian wearable devices.
Improved wearable accuracy for anxiety and ECG does not yet warrant clinical adoption; leaves open impact on MI hospitalization risk.
This paper proposes an algorithm based on DBSCAN, which can solve the problem of excessive error when wearable devices detect human anxiety. It is mainly used for more accurate ECG measurement and display of portable civilian wearable devices, and can better analyze the changes in the user's anxiety based on the heart rate variability. The traditional algorithm is improved. Compared with the anxiety analysis of wearable devices using traditional algorithms, the measurement accuracy of devices equipped with this algorithm is improved by 16.2%. At the same time, the current ECG display function of civilian wearable devices can use this algorithm to correct the ECG. Compared with the direct measurement of the ECG on wearable devices without this algorithm, the accuracy of this algorithm is improved by 12.74%. The error after using this algorithm is significantly reduced, which can better meet user needs and improve the accuracy of civilian wearable devices.
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Tang et al. (2021) studied this question.
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