Online and offline anger detection via electromyography analysis

Emotional states involving anger, hostility, anxiety and stress have been associated with an increased risk of cardiovascular disease. Online emotion recognition has achieved little attention in the literature in comparison to offline approaches. We present both online and offline methods to identify anger based on EMG data. In the offline method, the Hilbert-Huang transform is used to extract energy features from different time-frequency blocks. This approach achieves an overall classification accuracy of 87.5%. We also develop a novel online method combining machine learning with the tracking of a single parameter for anger detection. Here, band energy is calculated within time windows, and is continuously adjusted based on classified peak amplitudes. Although this technique has a lower classification accuracy than the offline method, it is quite promising as it is well-suited for wearable monitoring and long-term stress management.

[1]  T. Lewis,et al.  Psychosocial factors and cardiovascular diseases. , 2005, Annual review of public health.

[2]  M. Murugappan,et al.  Human emotional stress analysis through time domain electromyogram features , 2013, 2013 IEEE Symposium on Industrial Electronics & Applications.

[3]  Iman Mohammad Rezazadeh,et al.  Discriminating affective states in music induction environment using forehead bioelectric signals , 2011, 2011 1st Middle East Conference on Biomedical Engineering.

[4]  Jennifer Healey,et al.  Digital processing of affective signals , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[5]  Bo Cheng,et al.  Emotion Recognition from Surface EMG Signal Using Wavelet Transform and Neural Network , 2008 .

[6]  Gabriel Rilling,et al.  Bivariate Empirical Mode Decomposition , 2007, IEEE Signal Processing Letters.

[7]  Hang Li,et al.  On the use of instantaneous mean frequency estimated from the Hilbert spectrum of facial electromyography for differentiating core affects , 2012, 2012 5th International Conference on BioMedical Engineering and Informatics.

[8]  Fethi Bereksi-Reguig,et al.  Negative emotion detection using EMG signal , 2014, 2014 International Conference on Control, Decision and Information Technologies (CoDIT).

[9]  Emery N. Brown,et al.  Characterization of fear conditioning and fear extinction by analysis of electrodermal activity , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[10]  Mohamed Chetouani,et al.  Hilbert-Huang transform based physiological signals analysis for emotion recognition , 2009, 2009 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT).

[11]  Stefanie Rukavina,et al.  Recognition of Intensive Valence and Arousal Affective States via Facial Electromyographic Activity in Young and Senior Adults , 2016, PloS one.

[12]  Hong Qiu,et al.  Affective recognition from EMG signal: An approach based on correlation analysis and adaptive Tabu search , 2011, 2011 International Conference on Mechatronic Science, Electric Engineering and Computer (MEC).

[13]  Tamás D. Gedeon,et al.  Objective measures, sensors and computational techniques for stress recognition and classification: A survey , 2012, Comput. Methods Programs Biomed..

[14]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.