人類的生理呼吸與心理情緒狀態之間已驗證存有明顯的相關性。然而傳統生理傳感器量測的呼吸信號不僅含有情緒變化和情緒強度的資訊，而且常混雜著移動雜訊（MAs）。呼吸信號中混雜的資訊，限制了呼吸信號應用於情緒狀態評估的效能。本研究提出從呼吸信號中提取代表性情緒引發片段（representative Emotion Elicited Segments, EESs）作為情緒識別的法則，使個體的情緒狀態識別更為可靠。 EES提取程序包含下列兩個主要的法則：（1）基於動態時間規劃距離（Dynamic Time Warping）及互信息(Mutual Information)的情緒特徵挑選法則(MIDTW); （2）情緒特徵聚類密度分析法則（Constraint-based Elicited Segment Density, CESD）。由於呼吸為一週期性且受人體自律的生理信號，特定情緒狀態下的呼吸行為表現應具有類同質性。基於此性質，本研究提出一種適用於呼吸信號的無參數之半同質性切斷演算法（parameter-free Respiration quasi-Homogeneity Segmentation, RHS），將呼吸信號依半同質性進行分段，然後從分段資料中提取出EES。從五個人類基本情緒（即“愛”，“悲傷”，“喜悅”，“憤怒”和“恐懼”）誘發實驗的驗證結果顯示，本研究提出的切斷與EES提取法則，有效提升情緒的識別準確度。
然而朝向移動式情緒偵測的技術發展，需倚賴便攜式生理傳感器。最近的研究指出，光電容積描記圖（PPG）可用於監測呼吸活動，尤其PPG與呼吸率（respiratory rate, RR）之間的相關性已被廣泛研究與證實。然而，PPG傳感器（脈搏血氧儀）對MAs非常敏感，導致四個受呼吸活動調變的PPG特徵（PPG波的強度，頻率，幅度和脈衝寬度）明顯不一致。針對此議題，本研究提出一種基於卡曼濾波器（Kalman Filter, KF）的自適應融合法則，用於融合PPG信號中的4種RR特徵。該模型應用特徵間差異性和特徵內部的變異量統計，建構卡曼濾波器的測量程序與RR狀態程序。由於不須依賴外部實際呼吸量測信號(參考信號)，為使卡曼濾波器能穩定運作，須忽略信號品質低的資料片段。
然而，考量長時且連續RR估計的應用需求，本研究進一步提出一種自適應融合與預測的法則（Adaptive Fusion with Prediction model, AFP模型）。 AFP模型利用PPG信號的品質，及四個受呼吸調變特徵間的一致性，於時序中動態地調整AFP模型該進入學習模式亦或預測模式。為了提高RR估計過程的強健性，該模型導入一個動態類神經網路模型（Recurrent Neural Network, RNN），RNN在PPG信號品質好時，進行RR預測學習，在受移動雜訊干擾時進行RR預測。此外，AFP模型適應性的特徵融合策略，使RR估計過程能穩健地不受移動雜訊干擾的影響。並且，通過RNN的實時學習能力，個人PPG信號特徵會被導入RR估計程序的學習中，從而促進個別的RR估計，為長期的實時情緒偵測提供一種具潛力的法則。

The human respiration has been reported having great relevance with the emotional state. However, the respiratory signal obtained using conventional physiological sensors not only reflects changing emotions and emotion intensities, but also contains motion artifacts (MAs). The resulting information ambiguity limits the practical application of the respiration signal as a means of reliable affective state appraisal. Thus, this dissertation proposes a method for extracting representative Emotion Elicited Segments (EESs) from the respiratory signal such that the affective state of the individual can be more reliably determined. The EES extraction process involves the combination of the following procedures, namely (1) Mutual Information Based Emotion Relevance Feature Ranking based on the Dynamic Time Warping Distance (MIDTW); and (2) Emotion clustering analysis based on Constraint-based Elicited Segment Density (CESD). Due to the innate regularity of the respiration signal, the respiration pattern under a particular emotion state would be relatively quasi-homogeneous over time. Accordingly, a parameter-free Respiration Quasi-Homogeneity Segmentation (RHS) algorithm is proposed for partitioning the respiration signal into quasi-homogenous segments, from which the EESs can then be extracted. The experimental results obtained for five prototypical emotions (i.e., “love”, “sadness”, “joy”, “anger” and “fear”) show that the proposed segmentation / extraction methodology enables the EESs to be reliably identified.
However, a portable respiratory sensor is demanded when performing ambulatory emotion detection. Recent studies have suggested that photoplethysmogram (PPG) can be used for respiration activity monitoring, where the relationship between PPG and respiratory rate (RR) has been widely investigated. However PPG sensor (pulse oximetry) is very sensitive to MAs, resulting that the RR features derived from the four respiratory-induced variations (intensity, frequency, amplitude and pulse width) of PPG may present significantly inconsistent values. To address the issue, an adaptive fusion approach based on Kalman Filter (KF) is proposed to adaptively fuse the RR features in the PPG signals. The KF model applies the relationship of inter-feature coherence and intra-feature statistical changes to identify the measurement process and the four RR state processes of the KF for the four variations related to intensity, frequency, amplitude and pulse width, respectively.
The use of KF model must neglect low signal quality intervals which may pose an application limitation. Accordingly, an Adaptive Fusion with Prediction (AFP) model is further proposed for long-term continuous RR estimation based on PPG signals. The AFP model incorporates a Recurrent Neural Network (RNN), which performs online prediction learning when the PPG signal has good quality and RR prediction when MA interference. Moreover, the AFP model dynamically adapts the feature fusion strategy, and therefore renders the RR estimation process more robust toward the effects of MA interference. Notably, through the real-time learning capability of the RNN, the personal PPG characteristics of the individual can be incorporated into the RR estimation process, thereby facilitating individualized RR estimation, which provides a potential technique for long-term real-time affective detection.

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