An Adaptive Heart Rate Monitoring Algorithm for Wearable Healthcare Devices

This paper focuses on developing an adaptive heart rate monitoring algorithm for wrist-based rehabilitation systems. Due to the characteristics of the wrist, the heartbeat measurements are unstable. To improve the preprocessing efficiency and perform measurement calibration, a novel joint algorithm incorporating automatic multiscale-based peak detection and fuzzy logic control (AMPD-Fuzzy) is proposed. The monitoring approach consists of two phases: (1) Preprocessing and (2) Detection and Calibration. Phase 1 explores the parameter settings, threshold, and decision rules. Phase 2 applies fuzzy logic control and the Laplacian model to provide signal reshaping. Experimental results show that the proposed algorithm can effectively achieve heart rate monitoring for wearable healthcare devices.

[1]  Maury A. Nussbaum,et al.  Preferred Placement and Usability of a Smart Textile System vs. Inertial Measurement Units for Activity Monitoring , 2018, Sensors.

[2]  B. Carlin,et al.  Pulmonary rehabilitation and chronic lung disease: opportunities for the respiratory therapist. , 2009, Respiratory care.

[3]  Cinna Soltanpur,et al.  A review on wearable photoplethysmography sensors and their potential future applications in health care , 2018, International journal of biosensors & bioelectronics.

[4]  Nicholas B Allen,et al.  Accuracy of Consumer Wearable Heart Rate Measurement During an Ecologically Valid 24-Hour Period: Intraindividual Validation Study , 2019, JMIR mHealth and uHealth.

[5]  Andriy Temko,et al.  Accurate Heart Rate Monitoring During Physical Exercises Using PPG , 2017, IEEE Transactions on Biomedical Engineering.

[6]  M. Usman Akram,et al.  Heart rate tracking in photoplethysmography signals affected by motion artifacts: a review , 2021, EURASIP J. Adv. Signal Process..

[7]  Ahmad Alwosheel,et al.  Heart rate variability estimation in photoplethysmography signals using Bayesian learning approach , 2016, Healthcare technology letters.

[8]  Ramesh R. Rao,et al.  A Bayesian model of heart rate to reveal real-time physiological information , 2012, 2012 IEEE 14th International Conference on e-Health Networking, Applications and Services (Healthcom).

[9]  Mario Konijnenburg,et al.  CorNET: Deep Learning Framework for PPG-Based Heart Rate Estimation and Biometric Identification in Ambulant Environment , 2019, IEEE Transactions on Biomedical Circuits and Systems.

[10]  Yun-Kyung Lee,et al.  Development of a wristwatch-type PPG array sensor module , 2011, 2011 IEEE International Conference on Consumer Electronics -Berlin (ICCE-Berlin).

[11]  Martin Wolf,et al.  An Efficient Algorithm for Automatic Peak Detection in Noisy Periodic and Quasi-Periodic Signals , 2012, Algorithms.

[12]  S. Haykin,et al.  Adaptive Filter Theory , 1986 .

[13]  Guoliang Xing,et al.  DeepHeart , 2021, ACM Trans. Sens. Networks.

[14]  Roozbeh Jafari,et al.  Robust heart rate estimation using wrist-based PPG signals in the presence of intense physical activities , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[15]  Yonghong Peng,et al.  A Novel Adaptive Spectrum Noise Cancellation Approach for Enhancing Heartbeat Rate Monitoring in a Wearable Device , 2018, IEEE Access.

[16]  Tapio Taipalus,et al.  Comparison of photoplethysmogram measured from wrist and finger and the effect of measurement location on pulse arrival time , 2018, Physiological measurement.

[17]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[18]  James V. Stone.  Independent Component Analysis: A Tutorial Introduction , 2007 .

[19]  David Atienza,et al.  SPARE: A Spectral Peak Recovery Algorithm for PPG Signals Pulsewave Reconstruction in Multimodal Wearable Devices , 2021, Sensors.

[20]  Cecilia Surace,et al.  A Comparative Analysis of Signal Decomposition Techniques for Structural Health Monitoring on an Experimental Benchmark , 2021, Sensors.

[21]  Antonio Cicone,et al.  How Nonlinear-Type Time-Frequency Analysis Can Help in Sensing Instantaneous Heart Rate and Instantaneous Respiratory Rate from Photoplethysmography in a Reliable Way , 2017, Front. Physiol..

[22]  Ming-Feng Wu,et al.  Design of Pervasive Rehabilitation Monitoring for Chronic Obstructive Pulmonary Disease , 2013, IEEE Sensors Journal.

[23]  Richard Casaburi,et al.  A brief history of pulmonary rehabilitation. , 2008, Respiratory care.

[24]  Roozbeh Jafari,et al.  BioWatch: A Noninvasive Wrist-Based Blood Pressure Monitor That Incorporates Training Techniques for Posture and Subject Variability , 2016, IEEE Journal of Biomedical and Health Informatics.

[25]  Andres J. Rodriguez,et al.  Sources of Inaccuracy in Photoplethysmography for Continuous Cardiovascular Monitoring , 2021, Biosensors.

[26]  Kristof Van Laerhoven,et al.  Deep PPG: Large-Scale Heart Rate Estimation with Convolutional Neural Networks , 2019, Sensors.

[27]  N. Fang,et al.  Skin-electrode iontronic interface for mechanosensing , 2021, Nature Communications.

[28]  W. Kibbe,et al.  Investigating sources of inaccuracy in wearable optical heart rate sensors. , 2020, NPJ digital medicine.