Bipolar Disorder Recognition with Histogram Features of Arousal and Body Gestures
Host Publication: AVEC 2018 - Proceedings of the 2018 Audio/Visual Emotion Challenge and Workshop, co-located with MM 2018
Authors: L. Yang, Y. Li, H. Chen, D. Jiang, M. Oveneke and H. Sahli
UsePubPlace: New York, NY, USA
Publication Date: Oct. 2018
Number of Pages: 7
This paper targets the Bipolar Disorder Challenge (BDC) task of Audio Visual Emotion Challenge (AVEC) 2018. Firstly, two novel features are proposed: 1) a histogram based arousal feature, in which the continuous arousal values are estimated from the audio cues by a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) model 2) a Histogram of Displacement (HDR) based upper body posture feature, which characterizes the displacement and velocity of the key body points in the video segment. In addition, we propose a multi-stream bipolar disorder classification framework with Deep Neural Networks (DNNs) and a Random Forest, and adopt the ensemble learning strategy to alleviate the possible over-fitting problem due to the limited training data. Experimental results show that the proposed arousal feature and upper body posture feature are discriminative for different bipolar episodes, and our proposed framework achieves promising classification results on the development set, with the unweighted average recall (UAR) of 0.714, which is higher than the baseline result 0.635. On test set evaluation, our system obtains the same UAR (0.574) as the challenge baseline.