高階融合生理及心理資料進行多重模型的情緒狀態分析

Analysis of Mood State using Multimodal High-Level Information Fusion of Psychological and Physiological Data

許峰銘(FENG-MING SHU)

指導教授 ： 張玉山

國立臺北大學/電機資訊學院/多媒體與網路科技產業碩士專班/碩士(2016年)

摘要

情緒狀態異常的判定是需要多方面的資訊予以協助判斷的，包含了生理及心理所提供的資訊，由於社會經濟壓力與生活步調快速，導致有情緒狀態不穩定及憂鬱傾向的人越來越多。而行動裝置的問世，也替人們帶來了更便利的生活，行動裝置的種類很多，包括了手機、手環、腦波感測器等，將上述行動裝置所產生的資料進行融合後，可以針對心理的情緒狀態狀做出判定，所以藉由這些儀器所搜集的資訊來增加判斷心理症狀的準確性是相當重要的。
我們設計了一套結合智慧型手機、智能手環及腦波感測器的應用程式系統，它對使用者搜集不同型態的生理及心理資料，這些資料經過本體論的方式進行推論，並以貝葉斯網路架構下做高階資訊融合，運用這些多種類別的模型推測出可能的情緒狀態異常程度指標，提供給使用者及醫生做參考。

關鍵字

高階融合 ； 生理 ； 心理 ； 情緒狀態 ； 本體論 ； 貝氏網路

並列摘要

Mood state define is required multifaceted information to assist judgment, it contains information provided by psychological and psychological. Since the socio-economic pressures and pace of life fast, lead to mood state and depression tends people more and more. The publish of mobile devices, bring a more convenient life for people, many different types of mobile devices, including smart phones, wisdom bracelet and electroencephalography, etc. After fusion the data generated by these mobile devices, psychological symptoms of depression can be made for the determination, so increase the accuracy of judgment psychological symptoms is very important by these instruments gathered information.
We designed a combination of smart phones, wisdom bracelet and electroencephalography application system, it collected different types of psychological and psychological data by user, these data through ontology way to inference, to do high-level information fusion by Bayesian Network, using variety models of categories speculate the possible indicators of the degree of depression, it can be provide to user and doctor with reference.

並列關鍵字

High-Level Information Fusion ； Psychological ； Physiological ； Mood State ； Ontology ； Bayesian Network

參考文獻

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