standard deviation of the beat length over a window of
time. We use a window of 2min.
Baseline: We obtain the ground truth for the above metrics using a commercial ECG monitor. We use the AD8232
evaluation board with a 3-lead ECG monitor to get the ECG
signal. The ECG device and the FMCW radio are connected
to a shared clock to keep them synchronized.
Accuracy in comparison to ECG. We run experiments
with 30 participants, collecting over 130,000 heartbeats.
Each subject is simultaneously monitored with EQ-Radio
and the ECG device. We process the data to extract the above
We first compare the IBIs estimated by EQ-Radio to the
IBIs obtained from the ECG monitor. Figure 5(a) shows
a scatter plot where the x and y coordinates are the IBIs
derived from EQ-Radio and the ECG respectively. The color
indicates the density of points in a specific region. Points on
the diagonal have identical IBIs in EQ-Radio and ECG, while
the distance to the diagonal is proportional to the error. It
can be visually observed that all points are clustered around
the diagonal, and hence EQ-Radio can estimate IBIs accurately irrespective of the their lengths.
We quantitatively evaluate the errors in Figure 5(b),
which shows a Cumulative Distribution Function (CDF)
of the difference between EQ-Radio’s IBI estimate and the
ECG-based IBI estimate for each beat. The CDF has jumps
at 4ms intervals because each FMCW sweep takes 4ms. The
CDF shows that the 97th percentile error is 8ms. Our results
further show that EQ-Radio’s mean IBI estimation error is
3.2ms. Since the average IBI in our experiments is 740ms, on
average, EQ-Radio estimates a beat length to within 0.43% of
its correct value.
In Figure 5(c), we report results for beat variation metrics that are typically used in emotion recognition. The
figure shows the CDF of errors in recovering the SDNN
Section 7. 2.
All experiments in this section were approved by our IRB.
7. 1. Evaluation of heartbeat extraction
First, we assess the accuracy of EQ-Radio’s segmentation
algorithm in extracting heartbeats from RF signals.
Experimental setup. Participants: We recruited 30 partici-
pants ( 10 females). The subjects are between 19 and 77 year
old. The subjects had no restrictions on their clothing.
Experimental Environment: We perform our experiments
in five different rooms in a standard office building. The
evaluation environment contains office furniture including
desks, chairs, couches, and computers. The experiments
are performed while other users are present in the room.
The change in the experimental environment and the presence of other users had a negligible impact on the results
because the FMCW radio described in Section 4 eliminates
reflections from static objects (e.g., furniture) and isolates
reflections from different humans. 6
Metrics: To evaluate EQ-Radio’s heartbeat extraction, we
use metrics that are common in emotion recognition:
• Inter-Beat-Interval (IBI): The IBI measures the accuracy
in identifying the boundaries of each individual beat.
• Root Mean Square of Successive Differences (RMSSD):
This metric focuses on differences between successive
beats. RMSSD is typically used as a measure of the parasympathetic nervous activity that controls the heart. 44
We calculate RMSSD for IBI sequences in a 2min
• Standard Deviation of NN Intervals (SDNN): The term
NN-interval refers to the IBI. Thus, SDNN measures the
IBI from ECG (ms)
(a) Scatterplot of IBI estimates for EQ-Radio vs. ECG
Error of IBI (ms)
(b) CDF of error in IBI
400 600 800 10001200 0 10 20 30 40
0 5 10 15
(c) Error in emotion-related
Figure 5. Comparison of IBI Estimates Using EQ-Radio and a Commercial ECG Monitor. The figure shows various metrics for evaluating
EQ-Radio’s heartbeat segmentation accuracy in comparison with an FDA-approved ECG monitor. Note that the CDF in (b) jumps at 4ms
intervals because the RF signal was sampled every 4ms.