Using Wireless Signals
By Mingmin Zhao, Fadel Adib, and Dina Katabi
This paper demonstrates a new technology that can infer a
person’s emotions from RF signals reflected off his body.
EQ-Radio transmits an RF signal and analyzes its reflections
off a person’s body to recognize his emotional state (happy,
sad, etc.). The key enabler underlying EQ-Radio is a new
algorithm for extracting the individual heartbeats from the
wireless signal at an accuracy comparable to on-body ECG
monitors. The resulting beats are then used to compute
emotion-dependent features which feed a machine-learning
emotion classifier. We describe the design and implementation of EQ-Radio, and demonstrate through a user study that
its emotion recognition accuracy is on par with state-of-the-art emotion recognition systems that require a person to be
hooked to an ECG monitor.
Emotion recognition is an emerging field that has attracted
much interest from both the industry and the research community. 13, 18, 22, 35, 40 It is motivated by a simple vision: Can we
build machines that sense our emotions? If we can, such
machines would enable smart homes that react to our
moods and adjust the lighting or music accordingly. Movie
makers would have better tools to evaluate user experience.
Advertisers would learn customer reaction immediately.
Computers would automatically detect symptoms of depression, anxiety, and bipolar disorder, allowing early response
to such conditions. More broadly, machines would no longer be limited to explicit commands, and could interact with
people in a manner more similar to how we interact with
Existing approaches for inferring a person’s emotions
either rely on audiovisual cues, such as images and audio
clips, 22, 42, 48 or require the person to wear physiological sen-
sors like an Electrocardiogram (ECG) monitor. 7, 21, 26, 36 Both
approaches have their limitations. Audiovisual techniques
leverage the outward expression of emotions, but cannot
measure inner feelings. 12, 16, 36 For example, a person may be
happy even if she is not smiling. Also, people differ widely
in how expressive they are in showing their inner emo-
tions, which further complicates this problem. 25 The second
approach recognizes emotions by monitoring the physiolog-
ical signals that change with our emotional state. Intuitively,
a person’s heart rate increases with anger or excitement;
there are also more complex changes that appear as vari-
ability in the duration of a heart beat. 12, 39 This approach uses
on-body sensors – For example, ECG monitors – to measure
these signals and correlate their changes with joy, anger,
etc. This approach is more correlated with the person’s
inner feelings since it taps into the interaction between
The original version of this paper appeared in Proceedings
of the 22nd Annual International Conference on Mobile
Computing and Networking. ACM, 2016.
the autonomic nervous system and the heart rhythm. 27,
39 However, the use of body sensors is cumbersome and
can interfere with user activity and emotions, making this
approach unsuitable for regular usage.
In this paper, we introduce a new method for emotion
recognition that achieves the best of both worlds – that is,
it directly measures the interaction of emotions and physiological signals, but does not require the user to carry sensors on his body. Our design uses Radio Frequency (RF)
signals to sense emotions. Specifically, RF signals reflect off
the human body and get modulated with bodily movements.
Recent research has shown that such RF reflections can be
used to measure a person’s breathing and average heart rate
without body contact. 6, 15, 19, 23, 33 However, the periodicity of
the heart signal (i.e., its running average) is not sufficient
for emotion recognition. To recognize emotions, we need
to measure the minute variations in each individual beat
length. 12, 29, 39
Yet, extracting individual heartbeats from RF signals
incurs multiple challenges, which can be seen in Figure 1.
First, RF signals reflected off a person’s body are modulated
by both breathing and heartbeats. The impact of breathing is
typically orders of magnitude larger than that of heartbeats,
and tends to mask the individual beats (see the top graph
in Figure 1); to separate breathing from heart rate, past systems operate over multiple seconds (e.g., 30sec in Ref. Adib
et al. 6) in the frequency domain, forgoing the ability to measure the beat-to-beat variability. Second, heartbeats in the
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Figure 1. Comparison of RF signal with ECG signal. The top graph plots
the RF signal reflected off a person’s body. The envelope of the RF signal
follows the inhale-exhale motion. The small dents in the signal are due
to heartbeats. The bottom graph plots the ECG of the subject measured
concurrently with the RF signal. Individual beats are marked by grey
and white shades. The numbers report the beat-length in seconds.
Note the small variations in consecutive beat lengths.