Optimizing a PPG-based heart rate estimation model using neural architecture search reduced parameters by 75 times and improved the mean absolute error from 7.65 BPM to 6.02 BPM.
Heart rate estimation during exercise
Neural architecture search and hyperparameter optimization for PPG-based HR estimation vs Previous models
Mean absolute error (MAE) in heart rate estimation
Absolute Event Rate: 6.02% vs 7.65%
It is common for people to use wristband-type electronic devices such as smartwatches for routine healthcare services. Among the healthcare services provided by smartwatches, the method of measuring the heart rate (HR) during exercise is non-invasive and uses a photoplethysmogram (PPG); however, the disadvantage is that it is vulnerable to the motion artifacts (MAs) of the user. A technique for removing an MA from a PPG by using an accelerometer was studied and recently many studies were conducted based on deep learning-based algorithms. In this study, various preprocessing techniques were compared, and optimal preprocessing parameters were determined, and an improvement in the performance was achieved by using a model tuning technique. In addition, the model was optimized with hyperparameter search and neural architecture search using Neural Network Intelligence developed by Microsoft. As a result, the parameter was reduced by 75 times as compared to previous works, and the mean absolute error (MAE) was improved by 26%, from 7.65 BPM to 6.02 BPM.
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Seok Bin Song
Jungwoo Nam
Jin Heon Kim
IEEE Sensors Journal
Seokyeong University
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Song et al. (Tue,) conducted a other in Heart rate estimation during exercise. Neural architecture search and hyperparameter optimization for PPG-based HR estimation vs. Previous models was evaluated on Mean absolute error (MAE) in heart rate estimation. Optimizing a PPG-based heart rate estimation model using neural architecture search reduced parameters by 75 times and improved the mean absolute error from 7.65 BPM to 6.02 BPM.
synapsesocial.com/papers/6a05146c6c3d07813971bdaf — DOI: https://doi.org/10.1109/jsen.2021.3073047