## 4 Measurement Results of HR and HRV with Optical Homodyne and Heterodyne Technique
### Recording of Vibrocardiogram and Electrocardiogram Signal  
It is equally important to compare a non-contact type optical detection method with existing contact type measurement methods, such as ECG. The commercially available AD8232 chip, designed for ultra-low power applications, is used as a contact type sensor for a results comparison with an accuracy of more than 96% [37; 38]. AD8232 is used in a three electrode configuration and makes use of right arm (RA), left arm (LA), and right leg (RL) electrodes to acquire bio-potential signal. It has the capability to acquire, amplify, and filter bio-potential signals. This chip is capable of eliminating the motion artifacts and the electrode half-cell potential. The output obtained from the AD8232 module is processed with a microcontroller. The standard PQRST waveform signal is obtained with it. The successive R-R interval (\(T_{RR}\)) in milliseconds is calculated for the _HR_ measurement. The  
Figure 14: Modulation frequency along with chest wall frequency detection.  
Figure 15: Modulation frequency along with chest wall frequency detection.  
detrending and denoising of the raw ECG signal is performed by wavelet analysis. The equation to calculate \(HR\) in beats per minute is given as,  
\[HR=\left(\frac{1}{T_{RR}}\times 60\right) \tag{24}\]  
The optical beat signal obtained from the coherent detection is called a vibrocardiogram (VCG), as the light signal is modulated by vibrations from the chest wall or wrist. On each individual measurement, the ECG and VCG traces were simultaneously recorded. Figures 16-22 shows the detection of the \(HR\) with optical sensing. Short-term recordings (5 min) were carried out on 20 human subjects (ten males aged from 20 to 40 years; ten females aged from 20 to 40 years). All the participants signed an informed consent agreement before taking part in the tests. The \(HR\) per minute is measured for a different set of test subjects, with varying physical activities, such as running, walking, speaking, and resting [39]. The ECG and VCG data are recorded for multiple human subjects and are mainly divided into three categories (resting, active, and exertion). Table 3 shows information about the subjects under test.  
Figure 16: ECG and VCG signal for 72 heart beats per minute.  
Figure 17: ECG and VCG signal for 78 heart beats per minute.  
Figure 19: ECG and VCG signal for 90 heart beats per minute.  
Figure 20: ECG and VCG signal for 96 heart beats per minute.  
Figure 21: ECG and VCG signal for 102 heart beats per minute.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Subject** & **Gender** & **Age** & **Weight** & **Height** & **Body Mass Index** \\
**Number** & & **(Years)** & **(kg)** & **(m)** & **(BMI) (kg\(\cdot\)m\({}^{-2}\))** \\ \hline
1 & M & 20 & 61 & 1.71 & 20.86 \\
2 & M &  
For the \(HR\) measurement of physical exertion, the test subjects were asked to go through excessive physical activities, such as climbing stairs or exercise, just before the measurement. Physical exertion leads to artifacts in the measurement on the test subject. These artifacts are basically associated with a high respiration rate, which causes rapid change in the chest wall motion. Frequent motion of the chest wall leads to shift in the baseline of the VCG signal. The motion artifacts associated with respiration are always present in the \(HR\) signal in the form of baseline wandering. The baseline drift is removed by denoising and detrending. Figure 23 shows the VCG signal for 72 bpm, 90 bpm, and 102 bpm with the same time axis. From Figure 23, the VCG signals with different \(HR\) can be identified easily.