  Emotion is well-recognized as a distinguished symbol of human beings, and it
plays a crucial role in our daily lives. Existing vision-based or sensor-based
solutions are either obstructive to use or rely on specialized hardware,
hindering their applicability. This paper introduces EmoSense, a
first-of-its-kind wireless emotion sensing system driven by computational
intelligence. The basic methodology is to explore the physical expression of
emotions from wireless channel response via data mining. The design and
implementation of EmoSense {face} two major challenges: extracting physical
expression from wireless channel data and recovering emotion from the
corresponding physical expression. For the former, we present a Fresnel zone
based theoretical model depicting the fingerprint of the physical expression on
channel response. For the latter, we design an efficient computational
intelligence driven mechanism to recognize emotion from the corresponding
fingerprints. We prototyped EmoSense on the commodity WiFi infrastructure and
compared it with main-stream sensor-based and vision-based approaches in the
real-world scenario. The numerical study over $3360$ cases confirms that
EmoSense achieves a comparable performance to the vision-based and sensor-based
rivals under different scenarios. EmoSense only leverages the low-cost and
prevalent WiFi infrastructures and thus constitutes a tempting solution for
emotion sensing.
