Heart Rate Estimation Error Research Articles

The Use of Wrist EMG Increases the PPG Heart Rate Accuracy in Smartwatches

The impact of tissue movements on the accuracy of heart rate (HR) estimates is a challenge in today’s wearable technology. Tissue movements are caused by muscle activity that modifies the optical path of the reflectance photoplethysmography (PPG), leading to motion artifacts (MAs) that mask the true HR. This kind of MA is not always detected using accelerometers (ACCs). In this study, we propose a method to increase the PPG HR accuracy of a wristwatch using wrist surface electromyogram (EMG) and ACC using spectrum subtraction algorithms. We collected the wrist EMG, wristwatch PPG, ACC data, and the electrocardiogram (ECG) from nine subjects. Data were recorded during four frequent hand movements and three activities (weightlifting and running/walking with and without holding a racket). The added value of the EMG was studied. Visual results indicate that wrist EMG correlates well with the MA seen in the PPG signal and provides additional information over the typically used ACC data. Our analysis showed that the proposed artifact removal method using EMG and ACC decreases the HR estimation error on average by 49% compared to only ACC. Our results showed that wrist EMG contains complementary information on the PPG artifacts and offers a novel signal modality for improving optical HR estimation accuracy in smartwatches.

Adaptive scheduling of acceleration and gyroscope for motion artifact cancelation in photoplethysmography

Background and objectiveRecently, various algorithms have been introduced using wrist-worn photoplethysmography (PPG) to provide high accuracy of instantaneous heart rate (HR) estimation, including during high-intensity exercise. Most studies focus on using acceleration and/or gyroscope signals for the motion artifact (MA) reference, which attenuates or cancels out noise from the MA-corrupted PPG signals. We aim to open and pave the path to find an appropriate MA reference selection for MA cancelation in PPG. MethodsWe investigated how the acceleration and gyroscope reference signals correlate with the MAs of the distorted PPG signals and derived both mathematically and experimentally an adaptive MA reference selection approach. We applied our algorithm to five state-of-the-art (SOTA) methods for the performance evaluation. In addition, we compared the four MA reference selection approaches, i.e. with acceleration signal only, with gyroscope signal only, with both signals, and using our proposed adaptive selection. ResultsWhen applied to 47 PPG recordings acquired during intensive physical exercise from two different datasets, our proposed adaptive MA reference selection method provided higher accuracy than the other MA selection approaches for all five SOTA methods. ConclusionOur proposed adaptive MA reference selection approach can be used in other MA cancelation methods and reduces the HR estimation error. SignificanceWe believe that this study helps researchers to address acceleration and gyroscope signals as accurate MA references, which eventually improves the overall performance for estimating HRs through the various algorithms developed by research groups.

An Effective Photoplethysmography Heart Rate Estimation Framework Integrating Two-Level Denoising Method and Heart Rate Tracking Algorithm Guided by Finite State Machine

In order to achieve accurate heart rate (HR) estimation in complex scenes, this paper presents an effective photoplethysmography (PPG) HR estimation framework integrating two-level denoising method and HR tracking algorithm guided by finite state machine (FSM). Aiming at solving the problems of low signal-to-noise ratio and co-frequency (the noise frequency is close to the HR frequency) caused by motion artifacts, the two-level denoising method consisting of the cascaded adaptive filtering and the differential denoising guided by FSM are designed to remove motion-related noises in PPG signals. In order to solve the problem of HR tracking error caused by poor wrist contact, the HR tracking algorithm guided by FSM is proposed to obtain the global optimization capability. The results of HR estimation experiments conducted on the IEEE Signal Processing Cup database and the WeData database created by ourselves show that the proposed framework can effectively cope with the problems of low signal-to-noise ratio and co-frequency. Even if tracking errors occur due to poor wristband contact, the proposed HR tracking algorithm guided by FSM can correct them in time when the HR component appears again. The average absolute error of HR estimation on the two databases are 1.76 BPM (beats per minute) and 2.77 BPM, respectively, which is more accurate compared to other algorithms.

A new principle of pulse detection based on terahertz wave plethysmography

This study presents findings in the terahertz (THz) frequency spectrum for non-contact cardiac sensing applications. Cardiac pulse information is simultaneously extracted using THz waves based on the established principles in electronics and optics. The first fundamental principle is micro-Doppler motion effect. This motion based method, primarily using coherent phase information from the radar receiver, has been widely exploited in microwave frequency bands and has recently found popularity in millimeter waves (mmWave) for breathe rate and heart rate detection. The second fundamental principle is reflectance based optical measurement using infrared or visible light. The variation in the light reflection is proportional to the volumetric change of the heart, often referred as photoplethysmography (PPG). Herein, we introduce the concept of terahertz-wave-plethysmography (TPG), which detects blood volume changes in the upper dermis tissue layer by measuring the reflectance of THz waves, similar to the existing remote PPG (rPPG) principle. The TPG principle is justified by scientific deduction, electromagnetic wave simulations and carefully designed experimental demonstrations. Additionally, pulse measurements from various peripheral body parts of interest (BOI), palm, inner elbow, temple, fingertip and forehead, are demonstrated using a wideband THz sensing system developed by the Terahertz Electronics Lab at Arizona State University, Tempe. Among the BOIs under test, it is found that the measurements from forehead BOI gives the best accuracy with mean heart rate (HR) estimation error 1.51 beats per minute (BPM) and standard deviation 1.08 BPM. The results validate the feasibility of TPG for direct pulse monitoring. A comparative study on pulse sensitivity is conducted between TPG and rPPG. The results indicate that the TPG contains more pulsatile information from the forehead BOI than that in the rPPG signals in regular office lighting condition and thus generate better heart rate estimation statistic in the form of empirical cumulative distribution function of HR estimation error. Last but not least, TPG penetrability test for covered skin is demonstrated using two types of garment materials commonly used in daily life.

Comparative study on the effect of color spaces and color formats on heart rate measurement using the imaging photoplethysmography (IPPG) method.

BACKGROUND: The imaging photoplethysmography (IPPG) technology has been demonstrated to be an effective method for heart rate (HR) monitoring. However, some interference caused by the ambient illumination variation and facial motion severely influences the accuracy of the HR measurement. Some color spaces and color formats are assumed to reduce the interference, and enhance the accuracy of HR estimation.OBJECTIVE: The aim is to identify the optimal color space and format for IPPG based HR measurement.METHODS: Six color spaces and 3 color formats are compared in this study, based on an IPPG based HR measurement system. 424 pieces of videos captured by the system are used for the selection of the optimal color channel and color space; while 10 pieces of videos are for the identification of the optimal color format. RESULTS: The results shows that the green channel of RGB space is the optimal color channel, and RGB is the optimal color space, in respect of the mean squared error of HR estimation. BayerBG 8bit is found to be the optimal color format for video recording, which can significantly reduce the HR estimation error.CONCLUSIONS: BayerBG 8bit color format for video recording, and RGB color space for video analysis is suggested for the IPPG based HR measurement system. The suitable configuration of color space and format could enhance the accuracy of HR measurement.

Method of Selecting the Most Discriminatory Areas Based on Spectral Entropy in Remote Photoplethysmography

A person's heart rate can be determined from a video image of the facial skin. This is explained by the fact that variations in the optical characteristics of biological tissues are caused by changes in their blood volume due to the pulse waves. Remote photoplethysmography problem is stated which is directed to estimation of a person's heart rate by registering of changes in volume pulse remotely, using a camera. Common drawback of the existing methods of the remote photoplethysmography is caused by facial image noise due to human mimic activity, head turns during video recording, non-stationarity of illumination and other similar factors. The main ways to solve such problems are to detect face in each frame of the video sequence, and to select the most informative regions of interest. A new method for selecting facial areas in a video is proposed, which leads to improve the accuracy of a solution of the remote photoplethysmography. The method consists in the application of criterion based on the Shannon spectral entropy to the color signals obtained from different facial areas on the video sequence in order to select the least noisy area. The correctness of the proposed method was confirmed in the course of experiments on an open database collected from 42 volunteers. The developed method reduced the relative error of heart rate estimation to 7 %.

STSR: Spectro-Temporal Super-Resolution Analysis of a Reference Signal Less Photoplethysmogram for Heart Rate Estimation During Physical Activity

The increasing demand for intelligent, wearable health monitoring devices with a Photoplethysmogram (PPG) sensor to monitor real-time heart rate (HR) in a subject attracts great attention from researchers. However, the PPG-enabled wearable devices are sensitive to motion artifacts, which results in inaccurate HR estimation. The accuracy of HR estimation is possible only by eliminating motion artifacts. A method based on multi-resolution Spectro-Temporal Super Resolution (STSR) and superlet transform (SLT) is proposed to improve the estimation accuracy of a motion artifact corrupted PPG signal. This proposed methodology does not require a reference accelerometer signal to suppress motion artifacts to estimate HR in real-time during physical activities. Hence, the proposed method is a computationally efficient algorithm. In the first step, a multi-order SLT separates the motion artifacts from the acquired PPG signal in the proposed method. In the next step, a high-intensity lobe in the spectro-temporal representation of the SLT spectrogram is detected to compute the HR. In the final step, an HR smoothing algorithm that uses HR from the preceding windows is proposed to accurately estimate HR in a motion artifact effected signal. The performance of the SLT-based HR estimation algorithm is evaluated using the publicly available IEEE SPC dataset. The average HR estimation error is 1.01 beats per minute, and the Pearson correlation is 0.997. Low estimation error, short computational time, and fast HR tracking make the proposed method an ideal choice to implement in wearable devices for real-time HR estimation.

A Wearable Ballistocardiography Device for Estimating Heart Rate During Positive Airway Pressure Therapy: Investigational Study Among the General Population.

BackgroundObstructive sleep apnea (OSA) is a condition in which a person’s airway is obstructed during sleep, thus disturbing their sleep. People with OSA are at a higher risk of developing heart problems. OSA is commonly treated with a positive airway pressure (PAP) therapy device, which is used during sleep. The PAP therapy setup provides a good opportunity to monitor the heart health of people with OSA, but no simple, low-cost method is available for the PAP therapy device to monitor heart rate (HR).ObjectiveThis study aims to develop a simple, low-cost device to monitor the HR of people with OSA during PAP therapy. This device was then tested on a small group of participants to investigate the feasibility of the device.MethodsA low-cost and simple device to monitor HR was created by attaching a gyroscope to a PAP mask, thus integrating HR monitoring into PAP therapy. The gyroscope signals were then analyzed to detect heartbeats, and a Kalman filter was used to produce a more accurate and consistent HR signal. In this study, 19 participants wore the modified PAP mask while the mask was connected to a PAP device. Participants lay in 3 common sleeping positions and then underwent 2 different PAP therapy modes to determine if these affected the accuracy of the HR estimation.ResultsBefore the PAP device was turned on, the median HR error was <5 beats per minute, although the HR estimation error increased when participants lay on their side compared with when participants lay on their back. Using the different PAP therapy modes did not significantly increase the HR error.ConclusionsThese results show that monitoring HR from gyroscope signals in a PAP mask is possible during PAP therapy for different sleeping positions and PAP therapy modes, suggesting that long-term HR monitoring of OSA during PAP therapy may be possible.

Accurate Ballistocardiogram Based Heart Rate Estimation Using an Array of Load Cells in a Hospital Bed.

The ballistocardiogram (BCG), a cardiac vibration signal, has been widely investigated for continuous monitoring of heart rate (HR). Among BCG sensing modalities, a hospital bed with multi-channel load-cells could provide robust HR estimation in hospital setups. In this work, we present a novel array processing technique to improve the existing HR estimation algorithm by optimizing the fusion of information from multiple channels. The array processing includes a Gaussian curve to weight the joint probability according to the reference value obtained from the previous inter-beat-interval (IBI) estimations. Additionally, the probability density functions were selected and combined according to their reliability measured by q-values. We demonstrate that this array processing significantly reduces the HR estimation error compared to state-of-the-art multi-channel heartbeat detection algorithms in the existing literature. In the best case, the average mean absolute error (MAE) of 1.76 bpm in the supine position was achieved compared to 2.68 bpm and 1.91 bpm for two state-of-the-art methods from the existing literature. Moreover, the lowest error was found in the supine posture (1.76 bpm) and the highest in the lateral posture (3.03 bpm), thus elucidating the postural effects on HR estimation. The IBI estimation capability was also evaluated, with a MAE of 16.66 ms and confidence interval (95%) of 38.98 ms. The results demonstrate that improved HR estimation can be obtained for a bed-based BCG system with the multi-channel data acquisition and processing approach described in this work.

Your Smart Speaker Can "Hear" Your Heartbeat!

Vital sign monitoring is a common practice amongst medical professionals, and plays a key role in patient care and clinical diagnosis. Traditionally, dedicated equipment is employed to monitor these vital signs. For example, electrocardiograms (ECG) with 3-12 electrodes are attached to the target chest for heartbeat monitoring. In the last few years, wireless sensing becomes a hot research topic and wireless signal itself is utilized for sensing purposes without requiring the target to wear any sensors. The contact-free nature of wireless sensing makes it particularly appealing in current COVID-19 pandemic. Recently, promising progress has been achieved and the sensing granularity has been pushed to millimeter level, fine enough to monitor respiration which causes a chest displacement of 5 mm. While a great success with respiration monitoring, it is still very challenging to monitor heartbeat due to the extremely subtle chest displacement (0.1 - 0.5 mm) - smaller than 10% of that caused by respiration. What makes it worse is that the tiny heartbeat-caused chest displacement is buried inside the respiration-caused displacement. In this paper, we show the feasibility of employing the popular smart speakers (e.g., Amazon Echo) to monitor an individual's heartbeats in a contact-free manner. To extract the submillimeter heartbeat motion in the presence of other interference movements, a series of novel signal processing schemes are employed. We successfully prototype the first real-time heartbeat monitoring system using a commodity smart speaker. Experiment results show that the proposed system can monitor a target's heartbeat accurately, achieving a median heart rate estimation error of 0.75 beat per minute (bpm), and a median heartbeat interval estimation error of 13.28 ms (less than 1.8%), outperforming even some popular commodity products available on the market.

Optical difference in the frequency domain to suppress disturbance for wearable electronics.

Measurements based on optics offer a wide range of unprecedented opportunities in the biological application due to the noninvasive or non-destructive detection. Wearable skin-like optoelectronic devices, capable of deforming with the human skin, play significant roles in future biomedical engineering such as clinical diagnostics or daily healthcare. However, the detected signals based on light intensity are very sensitive to the light path. The performance degradation of the wearable devices occurs due to device deformation or motion artifact. In this work, we propose the optical difference in the frequency domain of signals for suppressing the disturbance generated by wearable device deformation or motion artifact during the photoplethysmogram (PPG) monitoring. The signal processing is simulated with different input waveforms for analyzing the performance of this method. Then we design and fabricate a wearable optoelectronic device to monitor the PPG signal in the condition of motion artifact and use the optical difference in the frequency domain of signals to suppress irregular disturbance. The proposed method reduced the average error in heart rate estimation from 13.04 beats per minute (bpm) to 3.41 bpm in motion and deformation situations. These consequences open up a new prospect for improving the performance of the wearable optoelectronic devices and precise medical monitoring in the future.

Robust De-Noising Technique for Accurate Heart Rate Estimation Using Wrist-Type PPG Signals

Heart rate (HR) estimated using the photoplethysmography (PPG) signals during intense physical exercise is highly onerous owing to the existence of noise components like motion artifacts (MA’s) in the PPG signal. In this work, a robust de-noising technique for accurate HR estimation from the corrupted PPG signal is reported. The de-noising technique reported employs three pairs of Recursive Least Squares (RLS) as well as Normalized Least Mean Squares (NLMS) adaptive filters. The de-noised PPG signal obtained at the output of RLS and NLMS adaptive filters are combined using the sigmoid function. The three MA reduced PPG signals obtained from each adaptive filter pair are again combined using softmax activation function to form a MA reduced PPG signal from which the HR is estimated. Fast Fourier Transform (FFT) is used to estimate the HR and phase vocoder is used to refine the estimated HR. The proposed method is tested on the publically available 23 PPG datasets and it resulted in an HR estimation error of 1.89 beat per minute (BPM). The HR estimated using the proposed technique is found to be accurate and the HR estimation error is 1.89 BPM which is less when compared to other existing HR estimation mechanisms that reported an error of 1.97 BPM and 2.09 BPM.

CASINOR: Combination of adaptive filters using single noise reference signal for heart rate estimation from PPG signals

The accuracy of the heart rate (HR) estimated from the photoplethysmography (PPG) signal is always skeptical due to the ubiquity of the motion artifact (MA) in the signal. To ensure the HR estimated from the PPG signal is accurate, a new algorithm is proposed for the removal of MA component from the PPG signal using accelerometer signals. In this paper, the combination of adaptive filters using single noise reference signal referred to as CASINOR is proposed. The recursive least square (RLS) and normalized least mean square (NLMS) adaptive filters are used for denoising the PPG signals. The selection of single noise reference signal is based on the power value of the accelerometer signals. The MA-reduced PPG signals obtained from these RLS and NLMS filters are combined to a single PPG signal using sigmoid function, and the HR is estimated. The estimated HR is refined by using phase vocoder. The proposed method resulted in less HR estimation error of 1.92 beat per minute (BPM) on 23 PPG datasets (IEEE SP CUP 2015). Reduced error value, less computational time and improved accuracy augment this technique to be implemented in wearable devices.

Smartwatch PPG Peak Detection Method for Sinus Rhythm and Cardiac Arrhythmia.

The aim of our work herein was to design a photoplethysmographic (PPG) peak detection algorithm which automatically detect and discriminate various cardiac rhythms-normal sinus rhythms (NSR), premature atrial contraction (PAC), premature ventricle contraction (PVC), and atrial fibrillation (AF)-for PPG signals collected on smartwatch. Compared with peak detection algorithm designed for NSR, the novelty is that our proposed peak detection algorithm can accurately estimate heart rates (HR) among various arrhythmias, which enhances the accuracy of AF screening. Our peak detection method is composed of a sequential series of algorithms that are combined to discriminate various arrhythmias, as described above. Moreover, a novel Poincaré plot scheme is used to discriminate AF with Rapid Ventricular Response (RVR) from normal basal heart rate AF. Moreover, the method is also able to differentiate PAC/PVC from NSR and AF. Our results show that the proposed peak detection algorithm provides significantly lower average beat-to-beat estimation error (> 40% lower) and mean heart rate estimation error (> 50% lower) when compared to a traditional peak detection algorithm that is known to be accurate for NSR. Our new approach allows more accurate HR estimation as it can account for various arrhythmias which previous PPG peak detection algorithms were designed solely for NSR.

Robust Heart Rate Monitoring for Quasi-Periodic Motions by Wrist-Type PPG Signals.

Heart rate (HR) monitoring using photoplethysmography (PPG) is a promising feature in modern wearable devices. PPG is easily contaminated by motion artifacts (MA), hindering estimation of HR. For quasi-periodic motions, previous works generally focused on a few specific motions, such as walking and fast running. However, they may not work well for many different quasi-periodic motions where MA are very complex. In this paper, a robust HR monitoring scheme for different quasi-periodic motions using wrist-type PPG is proposed, which consists of dictionary learning for signal characteristics learning, human motion recognition for the current motion recognition and dictionary selection, sparse representation-based MA elimination for denoising, and spectral peak tracking for HR-related spectral peak tracking. The proposed scheme is robust to MA caused by different motions and has high accuracy. Experiments on six common quasi-periodic motions showed that the average absolute error of heart rate estimation was 2.40 beat per minute, and also showed that the proposed method is more robust than some state-of-the-art approaches for different motions.

Motion Artifact Reduction for Wrist-Worn Photoplethysmograph Sensors Based on Different Wavelengths

Long-term heart rate (HR) monitoring by wrist-worn photoplethysmograph (PPG) sensors enables the assessment of health conditions during daily life with high user comfort. However, PPG signals are vulnerable to motion artifacts (MAs), which significantly affect the accuracy of estimated physiological parameters such as HR. This paper proposes a novel modular algorithm framework for MA removal based on different wavelengths for wrist-worn PPG sensors. The framework uses a green PPG signal for HR monitoring and an infrared PPG signal as the motion reference. The proposed framework includes four main steps: motion detection, motion removal using continuous wavelet transform, approximate HR estimation and signal reconstruction. The proposed algorithm is evaluated against an electrocardiogram (ECG) in terms of HR error for a dataset of 6 healthy subjects performing 21 types of motion. The proposed MA removal method reduced the average error in HR estimation from 4.3, 3.0 and 3.8 bpm to 0.6, 1.0 and 2.1 bpm in periodic, random, and continuous non-periodic motion situations, respectively.

SPECMAR: fast heart rate estimation from PPG signal using a modified spectral subtraction scheme with composite motion artifacts reference generation.

The task of heart rate estimation using photoplethysmographic (PPG) signal is challenging due to the presence of various motion artifacts in the recorded signals. In this paper, a fast algorithm for heart rate estimation based on modified SPEctral subtraction scheme utilizing Composite Motion Artifacts Reference generation (SPECMAR) is proposed using two-channel PPG and three-axis accelerometer signals. First, the preliminary noise reduction is obtained by filtering unwanted frequency components from the recorded signals. Next, a composite motion artifacts reference generation method is developed to be employed in the proposed SPECMAR algorithm for motion artifacts reduction. The heart rate is then computed from the noise and motion artifacts reduced PPG signal. Finally, a heart rate tracking algorithm is proposed considering neighboring estimates. The performance of the SPECMAR algorithm has been tested on publicly available PPG database. The average heart rate estimation error is found to be 2.09 BPM on 23 recordings. The Pearson correlation is 0.9907. Due to low computational complexity, the method is faster than the comparing methods. The low estimation error, smooth and fast heart rate tracking makes SPECMAR an ideal choice to be implemented in wearable devices. Graphical Abstract Flow chart for the heart rate estimation using modified SPEctral subtraction scheme utilizing Composite Motion Artifacts Reference generation (SPECMAR) from photoplethysmographic (PPG) signals.

Unobtrusive Photoplethysmographic Monitoring Under the Foot Sole while in a Standing Posture.

Photoplethysmography (PPG) of the foot sole could provide additional health-related information compared with traditional PPG of the finger or wrist. Previously, foot PPG required the procedural binding of a light-emitting diode (LED)-photodetector (PD) pair. We achieved PPG of the foot sole without binding any sensors to the foot while the participant stood in a natural standing position on the testing device. Foot PPG was performed using multiple LED-PD pairs to overcome motion artefacts caused by stabilization. We identified regions of the sole suitable for reliable sensor positioning with optimal LED-PD pairs on the basis of the estimated heart rate (HR) and signal quality index derived by dynamic time warping (wSQI). The first experiment included four participants with direct skin-to-sensor contact, and the results showed a mean HR estimation error of 0.01 beats/min and a wSQI of 0.909. The extended experiment with 53 participants, which involved including a gap between the skin and sensors to consider real-life applications, yielded a mean HR estimation error of 0.638 beats/min and a wSQI of 0.751. Based on the selection ratio of optimal LED-PD pairs, the best region of the sole for PPG was the midfoot, except the medial longitudinal arch. In conclusion, we confirmed that foot PPG using multiple LED-PD pairs is appropriate for HR evaluation and further applications.

Performance of graphene ECG electrodes under varying conditions.

Smart garments for invisible health sensing have been available for a number of years, with heart sensing typically performed using silver loaded conductive threads integrated into the fabric to pick up the electrocardiogram. Recent work has investigated printed graphene textiles as an alternative to this, which are potentially more environmentally friendly, cost-effective, and can be performed after garment manufacturing. This paper presents an exploration of second order factors on the performance of graphene textile electrodes for electrocardiogram measurements. We prepare graphenebased textile electrodes using a simple and highly scalable continuous padding method. We then analyze two metrics: the change in heart rate estimation error, and the changes in signal-to-noise ratio; under two separate conditions: an extended record length, and varying temperatures; to recreate the some of the conditions the material would experience when being worn in real-life. We report that neither the heart rate estimation error or the signal-to-noise ratio are significantly affected after a long record or with varying temperature. These tests indicate that graphene electrodes are suitable for electrocardiogram measurements in a wearable that will be subjected to these conditions.

PREHEAT: Precision heart rate monitoring from intense motion artifact corrupted PPG signals using constrained RLS and wavelets

Abstract Goal Monitoring heart rate (HR) from intense motion artifact (MA) corrupted photoplethysmography (PPG) signals is a challenging problem. Accurate HR estimation from intense MA corrupted PPG signals can be very useful in HR monitoring programs during physical exercises. Method In this paper, we present a constrained recursive least-squares (cRLS) based denoising technique to effectively eliminate MAs from a composite PPG signal, and a coupled wavelet-Fourier based frequency estimation technique to estimate the HR more precisely. To separate the clean PPG, the MA corrupted composite PPG signal is decomposed using the ensemble empirical mode decomposition (EEMD) based signal separation technique. When EEMD fails to effectively separate the clean PPG from MAs, the new cRLS based denoising is used that can eliminate MAs even when there is an acceleration peak close to the HR peak. In HR estimation from the denoised signal, a novel wavelet based frequency estimation technique is incorporated along with the conventional Fourier based technique that improves the precision of HR tracking, especially from nonstationary PPG window frames. The autoregressive (AR) model based predictor and some heuristic decision based approaches are included for further reduction of the HR estimation error. Result The method is tested on 12 different subjects running on a treadmill with a peak velocity upto 15 km/h and we have obtained a mean absolute error of 0.81–0.84 beat per minute (BPM) with a standard deviation of ±0.82–0.96 BPM. Conclusion The proposed technique has achieved a benchmark result on these subjects which is much better as compared to the recently reported techniques in the literature. Significance The proposed method is expected to find a good application in wearable gadgets to monitor HRs accurately during fitness programs and other activities with intense physical motion.