IEEE Access (Jan 2020)
Anti-Motion Interference Wearable Device for Monitoring Blood Oxygen Saturation Based on Sliding Window Algorithm
Abstract
Wearable multi-physiological parameter monitoring technology can realize non-intrusive, non-invasive daily health monitoring of the human body. It has the characteristics of convenient operation, long-term continuous work, display of results, abnormal physiological condition alarm and wireless data transmission. First, an improved sliding window confidence propagation algorithm is proposed, which reduces the inefficient iterative process in the decoding process by correlation coefficients and reduces the running time of the algorithm by more than 1.5 times; designing a channel noise estimation method based on sliding window confidence propagation algorithm, and The source coding is similar. In order to estimate the local statistical parameters of the channel noise, the confidence propagation, the decoder uses the local partial probability of the adjacent accompanying child nodes to estimate the noise parameters of the channel noise. Secondly, the design and research of the blood oxygen saturation monitor, including filtering out high-frequency glitch noise, low-frequency baseline drift noise, and mixing and sudden motion interference noise, also achieved rapid and accurate extraction of pulse wave feature points, and calculated the blood oxygen value and pulse rate value. Compared with the traditional oximeter, the filtering algorithm and pulse wave feature point extraction algorithm on the oximeter software have less calculation amount and higher real-time performance. Finally, the relevant test experiment of the blood oxygen saturation monitor proves that the blood oximeter has good stability, accuracy and sensitivity. The practical use of anti-motion interference wearable devices not only shows that the wearable PPG sensor in this paper can stably obtain high-quality PPG signals, but also reflects its many applications in the field of real-time blood.
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