PPG signals generated using green light resulted in a significantly higher SNR compared to red light. This result is expected as the reflectance pulsation spectrum of blood is greatest around 575nm and smallest around 650nm. Red and infrared wavelengths of light are also known to have deeper tissue penetration, thus picks up more physiological noise such as the movement of blood or tissue beneath the sensor which are the predominant contribution to experimental noise in PPG measurements [13]. As the sensors should be able to function as part of an exercise-focused wearable device, PPG measurements using green light should be used for HR estimation.
While it has been reported that skin pigmentation affects the intensity of the reflected light in PPG measurements [13] (due to green light being more strongly absorbed by melanin compared to red light thus resulting in relatively shallower penetration into tissue [14, 15]), this was not investigated in this study.
While the AFE4403EVM shows better performance compared to MAX30101ACCEVKIT in terms of SNR, the SNR of the MAX30101ACCEVKIT was sufficiently high enough to be amenable to HR estimation. From the spectrograms and frequency spectra of the PPG obtained using the AFE4403EVM and MAX30101ACCEVKIT (Fig. 8), the fundamental frequency of the HR and its harmonics can be clearly identified from the peaks occurring between 0-5Hz and is easily distinguishable from the background noise. The periodic HR component can easily be obtained with appropriate filtering and thresholding.
Currently, it was easier to implement the MAX30101ACCEVKIT into the proposed device due to the smaller hardware size and flexible 10-pin FFC ribbon cable (compared to the DB9 to 8 pin header sensor cable for the AFE4403EVM). The MAX30101ACCEVKIT also contained an integrated accelerometer for 3-axis motion tracking, which could be used for motion artefact removal. As such, the proposed device was constructed using MAX30101ACCEVKIT as the PPG sensor.
The SNR for the MAX30101ACCEVKIT system housed within the case was found to be approximately 30% higher than without the case. This indicates that the set-up does not appear to impair signal collection and has a SNR comparable to that of the AFE4403EVM system. This increase in SNR may be due to the differences in contact pressure between the sensor and skin when securing the sensor to the wrist with and without the case. However, due to the small number of samples, it was not possible to determine the significance of this difference in SNR within this report. The effect of contact pressure on SNR could be explored in future to optimise the device design.
Voluntary motion artefacts were qualitatively observed to introduce large changes in the pulse shape, amplitude, and frequency for the ranges of periodic and non-periodic motions tested. Frequency analysis of the PPG signal obtained showed that motion artefacts resulted in noise that lied within the range of physiological frequencies (0-5Hz) and obscured the frequency peaks indicating the fundamental HR frequency. This decreased the confidence of HR estimation.
It was also observed that movements involving the wrist had a more pronounced effect on the PPG pulse waveforms than those involving the arm. Wrist motions were likely to cause greater movement of tissues beneath the wrist-worn sensor compared to arm motions, and thus introduced more physiological noise than arm movements. Alternative sensor placements could be explored in the future, such as on the chest or forehead, which are prone to less ranges of voluntary movements. The thinner layers of skin and the high impedance of the skull may also result in a high SNR for reflectance-based PPG measurements taken from the forehead.
The HR estimation algorithm described in this study closely predicted the user’s actual heart rate. A strong correlation between the estimated HR and the reference HR was observed, with a Pearson coefficient of r = 0.97 and a reasonable limit of agreement of [-5.7 5.8] (Fig. 11). The estimated HR showed a slight negative bias of 0.05 bpm. The current HR estimation algorithm provides a good approximation of the actual HR when the subject remains stationary, however, the accuracy of the HR estimation is dependent on the quality (SNR, presence of MA) of the PPG signals obtained.
Furthermore, the HR estimation algorithm described in this study was comparable to smart watches. Comparison between the estimated HR using the current proposed algorithm and commercially available wrist-worn smartwatches showed that the estimated HR from the proposed algorithm was able to follow the trend of the estimated HR from the smartwatches with a small margin of error indicated by the MAPE (Table 9). The estimated HR by the Apple smartwatch appeared to be more robust against sudden large changes in HR compared to the Garmin smartwatch (Fig. 12).
HR estimated from PPG measurements taken from the chest using the AFE4403EVM were shown to have a higher percentage error against both smartwatches, while measurements taken from the chest using the MAX30101ACCEVKIT did not show large changes in percentage error compared to measurements taken from the wrist.
The HR estimation algorithm described was not robust to motion. Comparison between the estimated HR from PPG measurements against reference HR from ECG measurements in the walk and run datasets showed poor correlation and agreement, with Pearson coefficients of 0.05 and 0.13, and broad limits of agreement of [-16 47] and [-42 24] for running and walking respectively (Fig. 13 and Fig. 14). Additionally, results of the motion tests showed that the HR estimation during the motion displayed significantly larger variance compared to the stationary segments, caused by large fluctuations in the estimated HR due to motion artefacts (Table 10).
The bandpass filter implemented in the proposed algorithm can remove high frequency noise and baseline drifts from the raw PPG signal but is unable to correct motion-induced artefacts as these tend to occur within the bandpass threshold. Numerous methods can be explored for the removal of motion artefacts from a range of movements, some of which requiring additional data input such as acceleration or multichannel PPG signals (Table 11).
Table 11
Current methods for motion artefact detection, correction and/or removal.
Method | Reference |
Statistical Measures |
Kurtosis, Shannon Entropy | (Selvaraj et al., 2011) |
Skewness, Kurtosis | (Krishnan, Natarajan & Warren, 2010) |
Time-Frequency Analysis |
Independent Component Analysis | (Krishnan, Natarajan & Warren, 2010) (Kim & Yoo, 2006) |
Empirical Mode Decomposition | (Khan et al., 2016) |
Sparse Signal Reconstruction | (Zhang, Pi & Liu, 2015) (Zhang, 2015) |
Spectral Subtraction | (Zhang, 2015) (Zhang et al., 2019) |
Wigner-Ville | (Yan, Poon & Zhang, 2005) |
Wavelet Denoising | (Lee & Zhang, 2003) (Fu, Liu & Tang, 2008) |
Variable Frequency Complex Demodulation | (Dao et al., 2017) |
Adaptive Filtering |
Kalman Filter | (Seyedtabaii & Seyedtabaii, 2008) |
Least Mean Squares Filtering | (Khan et al., 2016) (Tanweer, Hasan & Kamboh, 2017) |
Recursive Mean Squares Filtering | (Wu, Chen & Fang, 2018) |
The current HR estimation algorithm is not optimised in terms of processing speed or complexity. The current algorithm recalculates the peak intervals for all peaks in each window, including redundant overlapping datapoints between adjacent windows. This could be modified in future such that the calculation of peak intervals is only performed on new data points. A different approach using spectral analysis (such as periodogram, short-time Fourier transform, continuous wavelet transform, sparse-signal reconstruction based spectral estimation) could be performed given that PPG waveforms are quasi-periodic. The current algorithm is also written for post signal acquisition analysis and will need to be modified for real-time signal analysis.