Key result
A robust de-noising technique using adaptive filters achieved a heart rate estimation error of 1.89 BPM, which was lower than other existing mechanisms reporting errors of 1.97 and 2.09 BPM.
Why the study?
Heart rate estimation using photoplethysmography during intense physical exercise is difficult due to noise components like motion artifacts.
Does the proposed de-noising technique improve heart rate estimation accuracy from wrist-type PPG signals during physical exercise compared to existing mechanisms?
Does the proposed de-noising technique improve heart rate estimation accuracy from wrist-type PPG signals during physical exercise compared to existing mechanisms?
Absolute Event Rate: 1.89% vs 1.97%
The proposed de-noising technique using RLS and NLMS adaptive filters improves the accuracy of heart rate estimation from wrist-type PPG signals during intense physical exercise.
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May enhance wearable PPG HR monitoring during exercise; leaves open prospective clinical validation.
Arunkumar et al. (2020) studied Heart rate estimation during intense physical exercise. Robust de-noising technique using RLS and NLMS adaptive filters vs. Other existing HR estimation mechanisms was evaluated on Heart rate estimation error (BPM). A robust de-noising technique using adaptive filters achieved a heart rate estimation error of 1.89 BPM, which was lower than other existing mechanisms reporting errors of 1.97 and 2.09 BPM.
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