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 each other.
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 sensors 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 emotions, which further complicates this problem.25 The second approach recognizes emotions by monitoring the physiological 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 variability 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 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.
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