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• W2942061427 abstract "Continuous heart rate variability (HRV) monitoring can monitor a user’s health and help them make adjustments and treatments. The current methods of using ECG or externally mounted sensors are accurate, but inconvenient for the user. Other methods of using computer vision rely on ambient lighting conditions, and there are issues that may violate user privacy. Mobile devices such as smart watches and smartphones hold the promise of providing a more convenient, practical, and non-invasive method of detection. In this paper, we propose S-HRVM, a smart watch-based heart rate variability monitoring system. The basic idea behind S-HRVM is to combine the physiological representation and motion state of user. In physiological representation, we propose SP-HR method to process heart rate data and extract HRV features. The motion state, which can be derived by using accelerometer data of smart watch, is used to reduce the power consumption. In addition, the HRV features are used as calculation parameters of G-MSPC (the general health monitoring model based on MSPC), which can be used to detect mental states and diseases associated with autonomic function. Extensive experimental results demonstrate the effectiveness of the proposed methods." @default.
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• W2942061427 date "2019-03-15" @default.
• W2942061427 modified "2023-09-23" @default.
• W2942061427 title "S-HRVM: Smart Watch-based Heart Rate Variability Monitoring System" @default.
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• W2942061427 doi "https://doi.org/10.5555/3324320.3324342" @default.
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