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We present RFexpress! the first-ever network-edge based system to recognizeemotion from movement, gesture and pose via Device-Free Activity Recognition(DFAR). With the proliferation of the IoT, also wireless access points aredeployed at increasingly dense scale. in particular, this includes vehicularnodes (in-car WiFi or Bluetooth), office (Wlan APs, WiFi printer or projector)and private indoor domains (home WiFi mesh, Wireless media access), as well aspublic spaces (City/open WiFi, Cafes, shopping spaces). ProcessingRF-fluctuation at such edge-devices, enables environmental perception. In thispaper, we focus on the distinction between neutral and agitated emotionalstates of humans from RF-fluctuation at the wireless network edge in realisticenvironments. In particular, the system is able to detect risky drivingbehaviour in a vehicular setting as well as spotting angry conversations in anindoor environment. We also study the effectiveness of edge-based DFAR emotionand activity recognition systems in real environments such as cafes, malls,outdoor and office spaces. We measure radio characteristics in theseenvironments at different days and times and analyse the impact of variationsin the Signal to Noise Ratio (SNR) on the accuracy of DFAR emotion and activityrecognition. In a case study with 5 subjects, we then exploit the limits ofedge-based DFAR by deriving critical SNR values under which activity andemotion recognition results are no longer reliable. In case studies with 8 and5 subjects the system further could achieve recognition accuracies of 82.9\%and 64\% for vehicular and stationary wireless network edge in the wild(non-laboratory noisy environments and non-scripted, natural individualbehaviour patterns).

RFexpress! - Exploiting the wireless network edge for RF-based emotion sensing