RFexpress! - RF Emotion Recognition in the Wild. Raja, M. & Sigg, S. In 2017 IEEE International Conference on Pervasive Computing and Communication (WiP), March, 2017.
abstract   bibtex   
We present RFexpress! the first-ever system to recognize emotion from body movements and gestures via DeviceFree Activity Recognition (DFAR). We focus on the distinction between neutral and agitated states 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. In case studies with 8 and 5 subjects the system could achieve recognition accuracies of 82.9% and 64%. We study the effectiveness of 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 find critical SNR values under which activity and emotion recognition results are no longer reliable.
@INPROCEEDINGS{MuneebaWiP_2017_PerCom,
author={Muneeba Raja and Stephan Sigg},
booktitle={2017 IEEE International Conference on Pervasive Computing and Communication (WiP)},
title={RFexpress! - RF Emotion Recognition in the Wild},
year={2017},
abstract={We present RFexpress! the first-ever system to recognize emotion from body movements and gestures via DeviceFree Activity Recognition (DFAR). We focus on the distinction between neutral and agitated states 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. In case studies with 8 and 5 subjects the system could achieve recognition accuracies of 82.9% and 64%. We study the effectiveness of 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 find critical SNR values under which activity and emotion recognition results are no longer reliable.
},
%doi={10.1109/PERCOMW.2016.7457119},
month={March},
group = {ambience}}
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