Abstract
Although wrist-type photoplethysmographic (hereafter referred to as WPPG) sensor signals can measure heart rate quite conveniently, the subjects’ hand movements can cause strong motion artifacts, and then the motion artifacts will heavily contaminate WPPG signals. Hence, it is challenging for us to accurately estimate heart rate from WPPG signals during intense physical activities. The WWPG method has attracted more attention thanks to the popularity of wrist-worn wearable devices. In this paper, a mixed approach called Mix-SVM is proposed, it can use multi-channel WPPG sensor signals and simultaneous acceleration signals to measurement heart rate. Firstly, we combine the principle component analysis and adaptive filter to remove a part of the motion artifacts. Due to the strong relativity between motion artifacts and acceleration signals, the further denoising problem is regarded as a sparse signals reconstruction problem. Then, we use a spectrum subtraction method to eliminate motion artifacts effectively. Finally, the spectral peak corresponding to heart rate is sought by an SVM-based spectral analysis method. Through the public PPG database in the 2015 IEEE Signal Processing Cup, we acquire the experimental results, i.e., the average absolute error was 1.01 beat per minute, and the Pearson correlation was 0.9972. These results also confirm that the proposed Mix-SVM approach has potential for multi-channel WPPG-based heart rate estimation in the presence of intense physical exercise.
Highlights
Heart rate estimation can provide useful information for users who are engaged in rehabilitation or in physical exercise and anyone who wants to routinely keep track of their cardiac status
We propose an accurate and efficient strategy, named Mix-support vector machine (SVM), which tracks subjects’ heart rate based on a mixed method composed of adaptive filter, sparse signal reconstruction model, spectrum subtraction, and SVM-based spectral analysis
The results show that the performance of
Summary
Heart rate estimation can provide useful information for users who are engaged in rehabilitation or in physical exercise and anyone who wants to routinely keep track of their cardiac status. Traditional heart rate estimation mainly relies on electrocardiogram (ECG), but ECG requires the presence of ground and reference sensors that must be attached to the body. Its application area was limited because of the high hardware complexity and low user comfort ability. Photoplethysmography (PPG) [1,2] was widely used for measuring blood volume changes in tissue due to its non-invasive nature and low cost. The quality of PPG sensor signals can be affected by motion artifacts during intensive physical exercise. The motion artifacts must be removed to measure heart rate accurately [3,4,5]
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