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

TL;DR

RFexpress! leverages wireless network edge devices to recognize human emotions from RF signal fluctuations, enabling real-time environmental perception and activity recognition in diverse, noisy settings without intrusive sensors.

Contribution

This work introduces the first system for emotion recognition from RF signals at the network edge, demonstrating its effectiveness in real-world environments and various application scenarios.

Findings

Achieved up to 82.9% accuracy in vehicular settings.

Successfully distinguished neutral and agitated emotional states.

Identified critical SNR thresholds for reliable recognition.

Abstract

We present RFexpress! the first-ever network-edge based system to recognize emotion from movement, gesture and pose via Device-Free Activity Recognition (DFAR). With the proliferation of the IoT, also wireless access points are deployed at increasingly dense scale. in particular, this includes vehicular nodes (in-car WiFi or Bluetooth), office (Wlan APs, WiFi printer or projector) and private indoor domains (home WiFi mesh, Wireless media access), as well as public spaces (City/open WiFi, Cafes, shopping spaces). Processing RF-fluctuation at such edge-devices, enables environmental perception. In this paper, we focus on the distinction between neutral and agitated emotional states of humans from RF-fluctuation at the wireless network edge in realistic environments. In particular, the system is able to detect risky driving behaviour in a vehicular setting as well as spotting angry conversations in an indoor environment. We also study the effectiveness of edge-based DFAR emotion and activity recognition systems in real environments such as cafes, malls, outdoor and office spaces. We measure radio characteristics in these environments at different days and times and analyse the impact of variations in the Signal to Noise Ratio (SNR) on the accuracy of DFAR emotion and activity recognition. In a case study with 5 subjects, we then exploit the limits of edge-based DFAR by deriving critical SNR values under which activity and emotion recognition results are no longer reliable. In case studies with 8 and 5 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 individual behaviour patterns).