Abstract
With the development of smart wearable devices, daily health care becomes a popular topic among mobile applications. As one of human body vital signs, respiratory rate monitoring is discussed in substantial number of recent work based on smart watch. Inertial Measurement Unit (IMU) and Photoplethysmograph (PPG) sensor on smart watch can obtain respiratory rate respectively through various algorithms. However, existing works is designed with complicated signal processing methods which are poor of efficiency and accuracy. Therefore, we propose eRRGe, a smartwatch-based respiratory rate monitoring system, which can balance efficiency and accuracy by using peak detection and regression model together in order to generate respiratory rate. The mean absolute error (MAE) of our system is 2.25 breaths/minute, which shows a better performance than other systems. Moreover, the memory usage of regression model and processing time of our system are lower than other similar systems. Make it compatible for actually deployment on smart watch.
The work is supported by National Key R&D Program of China (2019YFB2102202), NSFC (61772084, 61832010), the Fundamental Research Funds for the Central Universities (2019XD-A13).
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Zhang, F., Zhao, L., Zhou, A., Ma, H. (2021). eRRGe: Balancing Accuracy and Efficiency of Respiratory Monitoring Using Smart Watch by Combining Peak Detection and Regression Model. In: Cui, L., Xie, X. (eds) Wireless Sensor Networks. CWSN 2021. Communications in Computer and Information Science, vol 1509. Springer, Singapore. https://doi.org/10.1007/978-981-16-8174-5_9
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