For Full-Text PDF, please login, if you are a member of IEICE,|
or go to Pay Per View on menu list, if you are a nonmember of IEICE.
Combining Parallel Adaptive Filtering and Wavelet Threshold Denoising for Photoplethysmography-Based Pulse Rate Monitoring during Intensive Physical Exercise
Chunting WAN Dongyi CHEN Juan YANG Miao HUANG
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2020/03/01
Online ISSN: 1745-1361
Type of Manuscript: PAPER
Category: Human-computer Interaction
photoplethysmography (PPG), pulse rate (PR), motion artifacts (MA), recursive least squares (RLS), wavelet threshold denoising,
Full Text: PDF(925.6KB)>>
Real-time pulse rate (PR) monitoring based on photoplethysmography (PPG) has been drawn much attention in recent years. However, PPG signal detected under movement is easily affected by random noises, especially motion artifacts (MA), affecting the accuracy of PR estimation. In this paper, a parallel method structure is proposed, which effectively combines wavelet threshold denoising with recursive least squares (RLS) adaptive filtering to remove interference signals, and uses spectral peak tracking algorithm to estimate real-time PR. Furthermore, we propose a parallel structure RLS adaptive filtering to increase the amplitude of spectral peak associated with PR for PR estimation. This method is evaluated by using the PPG datasets of the 2015 IEEE Signal Processing Cup. Experimental results on the 12 training datasets during subjects' walking or running show that the average absolute error (AAE) is 1.08 beats per minute (BPM) and standard deviation (SD) is 1.45 BPM. In addition, the AAE of PR on the 10 testing datasets during subjects' fast running accompanied with wrist movements can reach 2.90 BPM. Furthermore, the results indicate that the proposed approach keeps high estimation accuracy of PPG signal even with strong MA.