# Physiological Driver Monitoring Using Capacitively Coupled and Radar Sensors
## 3 Results
### Experimental Results from Capacitively-Coupled ECG and Bio-Impedance  
5 subjects were recorded. Each subject drove approximately 30 min in city traffic and 30 min in highway traffic (one subject only did the highway traffic driving). 2 subjects wore two layers of clothing, and 3 subjects wore a single layer of clothing. See Table 1 for details. Table 2 shows the performance for R peak detection and heart rate on each of the subjects together with the percentage of removed signal (as a result of the quality-based processing described in Section 2.2). The motion artefact algorithms improve the median R peak detection sensitivity from 92.74% to 98.28% and the median positive predictive value (PPV) from 92.34% to 99.03%, city and highway combined. This compares favorably to [30] (average sensitivity 91.75%, PPV 84.47%) and [31] (average sensitivity 88.02%, PPV 95.17%). The overall mean absolute error on the heart rate when compared to the reference system is 1.48 bpm, calculated over 60 s windows. The motion artefact algorithms removed 10.29% resp. 20.41% (medians) of the signal in highway resp. city traffic. For applications where information regarding heart rate variability is desired, it is necessary to evaluate the performance on the detection of individual beat-to-beat R-R intervals. Table 3 shows performance metrics for all subjects. The mean absolute error on the R-R intervals is 34.22 ms. This is skewed by subject 5 where the reference contact ECG was noisy due to a poorly connected ground lead. If subject 5 is excluded, the mean absolute error on the R-R intervals reduces to 15.7 ms. Figure 4 shows the distribution of the R-R interval absolute error across the entire dataset, which is highly non-Gaussian. The 95 th percentile of the absolute error is 27.3 ms. If subject 5 is excluded this reduces to 5.9 ms.  
Table 3 also shows the coverage defined as the percentage of time that the respiration rate as calculated using an FFT-based method on 60-s windows from the ccBioZ signal has an error of 3 breaths per minute or less. The median coverage is 61%. This is significantly lower than the coverage numbers obtained for the ECG (median 93.85% coverage with R-R intervals within 5 ms), which can be explained by the facts that no signal quality based motion artefact reduction algorithm was yet included for the ccBioZ signals, and that only one, fixed ccBioZ measurement location was used, compared to an array for the ccECG measurements. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Subject**} & \multicolumn{2}{c}{**Recording Length**} & \multirow{2}{*}{**Number of Clothing Layers Worn**} \\ \cline{2-3}  & **City** & **Highway** & \\ \hline
1 & 44 min & 35 min & 1 \\
2 & 16 min & 15 min & 2 \\
3 & n/a & 32 min & 1 \\
4 & 38 min & 46 min & 1 \\
5 & 39 min & 36 min & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of recorded subjects for the capacitive sensor system evaluation. Figure 4: **(a)** Histogram of the absolute difference between R-R intervals extracted from the capacitive ECG and from the reference ECG over all 5 subjects and all driving scenarios (n = 13,148 heartbeats). **(b)** Cumulative distribution of the absolute difference. Note the horizontal axes in both graphs are logarithmic for clarity.