# Real-Time Webcam Heart-Rate and Variability Estimation with Clean Ground Truth for Evaluation +
## 5 Experiments and Results
### In-Depth Analysis
#### 5.3.3 Influence of Subject Movement  
The physical movements of the subjects themselves can also introduce noise in rPPG extraction. This is essentially caused by the interaction of light on the observed skin pixels as it moves. The performance of our methods while subjects perform different kinds of movements are shown in Figure 8 on datasets containing labelled movement conditions: VIPL-HR, MoLi-PPG, ECG-Fitness, PURE, and VicarPPG 2. As per expectations, we observe that the rPPG method is most accurate when no movement is happening or when just the eyes are moving. All other kinds of movements degrade the performance somewhat, although the accuracy stay acceptable for some of them. The worst performance is observed when subjects perform large/sudden movements in multiple axis, as well as 'freestyle' head bobbing/dancing motion. In both MoLi-PPG and VicarPPG 2, an interesting observation is that vertical angular head movement (nodding) results in poorer accuracy while horizontal motion (head shaking) is handled quite well by the method. This could be due to the face modelling step being able to model side faces better than top/bottom looking faces.  
Figure 7: rPPG performance (MAE; error bars represent standard deviation) under different lighting conditions. Uneven illumination, oversaturation from by bright frontal lighting, yellowish glow of halogen lights, and the fluctuating monitor reflection, all somewhat degrade the performance of the rPPG method in comparison with standard ceiling lighting. However, the method performs well under dim/dark lighting.  
The impact of explicitly detecting and reducing the head movement noise in the signal via the rhythmic motion noise suppression component (Section 3.3) can be gauged with the help of an ablation study. The results of such a study on VicarPPG 2 and MoLi-PPG are shown in Figure 9. It can be seen that the addition of the motion noise suppression component in the rPPG pipeline reduces heart rate estimation errors significantly under most movement conditions. While the reduction in error is negligible when subject movement is low (conditions steady, stable, eye movement; \(\sim\)0.05 bpm), this gap in performance becomes large when subjects perform large intense head movements (\(\sim\)1 to \(\sim\)3 bpm). The exception to this is the relatively small error difference for the left/right (MoLi-PPG) and tilting (VicarPPG 2) conditions, which can largely be attributed to poor AAM face fitting during movement resulting in incorrect head orientation measurement.  
A shortcoming of the rhythmic motion noise suppression method manifests itself when the primary frequency of head movement coincides with the heart rate of the subject. In such cases, while the suppression effect from low intensity head movements do not have a dominating effect against the heart rate frequency component, high intensity head movements can. They can significantly attenuate the heart rate component in the signal causing incorrect selection of the passband range of the narrow-bandpass filter, which can increase estimation errors. This phenomenon is observed in one of the movement conditions videos from VicarPPG 2 (subject #5).  
Figure 8: rPPG performance (MAE; error bars represent standard deviation) for different types of subject movements. Large/sudden multi-axis movement have the largest impact on accuracy. Interestingly, vertical head nodding motion seems to produce much higher error than horizontal head shaking motion of the same intensity.