Inference of Mood State Indices by Using a Multimodal High-Level Information Fusion Technique

Mood state assessment (MSA) is increasingly important for diagnosis and treatment of depression. Recent years, many approaches have been proposed for the process of MSA. When using a single approach for MSA, the user often has to deal with possible noisy data and unacceptable error rates. Novelty: In order to improve the accuracy of MSA, in this paper, we propose a novel high-level information fusion method for determining the MS of users by fusing physiological data, such as heart rate and brainwave information collected through a wearable device, and psychological data collected through a monthly mood chart. The multifaceted information must be collected and analyzed simultaneously. Contribution: In the inference process of proposed framework, we adopted a Bayesian Network (BN) to perform high-level information fusion. We exploited various evaluation approaches to evaluate the performance of the proposed approach. Result: We have conducted experiments using two datasets and evaluated the performance using various factors. The results show that the proposed method (7-M Bayesian Fusion) is superior to other methods averagely 9.48 % improvement in most evaluation factors. It reveals that the proposed approach is efficient in fusing the MS information required for accurate diagnosis of depression compared with those approaches without fusion approach or with few information fusing.