本文提出了運用一個時變機率模型估測人類非語言行為意向。此推論模型我們結合了心理學模型(計畫行為理論)建構一個符合人類行為意向統計與分析的機率模型。目前計畫行為理論也廣泛應用於決策、統計與分析。然而，目前的行為意向研究採用的是可穿戴式傳感器、圖像或上下文模型限於固定的目標與內容，而缺乏時變性和可擴展性，同時，不具備在類似動作下估測人類的行為意向。本研究中我們基於心理學模型與統計問卷收集有關人類行為意向的數據。從數據中清楚地表明，人的行為意向與肢體動作有關並且會隨環境與時間變化。有鑑於此，本研究提出了一個行為意向推理模型對於人類的行為意向進行估測。此推理模型具有四個優勢：一、可以提高同一數據庫中行為意向的準確性；二、可以強健的近似人類的真實行為意圖；三、運用人機介面顯示行為意向機率；四、整合心理學模型來推斷人類的行為意向。研究中，我們藉由姿態動作的變化預估人類的行為意向機率。過程中，我們運用問卷瞭解人們對於四個姿態動作的認知再透過統計分析的方式歸納出姿態動作隱含的行為意向。此外，本研究中運用這些統計數據與心理學模型建構一個推論模型。從模擬和實驗結果中，我們都驗證了所提出的方法具有隨人類的姿態、環境與時間變化估測人類的行為意向機率並進一步近似人類真實的行為意向。

We propose a method using time-varying fuzzy hidden Markov models (HMMs) probabilities to estimate nonverbal human behavior intention. This inference model combines the theory of psychology (Theory of Planned Behavior, TPB) to modeling the inference model. Traditional behavior intention models typically depend on the theory of reasoned action and the theory of planned behavior, which are often applied to decision making, analysis, and estimation. However, current identification systems and behavior intention estimation systems that use wearable sensors, images, and context models are limited to a fixed target, so they lack temporal variation and extensibility. Furthermore, these systems have low estimated accuracy for similar actions. To understand the impact of time on human behavior intention, we design and apply a questionnaire based on psychology, social customs, and expert experience to gather statistical data on human behavior intentions. The results clearly show that human intention varies with time. We therefore propose an inference model to predict the implied behavior intention states. The four key advantages of the inference model are as follows: i) improved accuracy in estimating behavior intentions from the same database, ii) robust approximations of real human behavior intentions, iii) used the human machine interface showing the human behavior intention probabilities, and iv) the model incorporates psychological concepts to infer human behavior intention. In this study, we predict the human behavior intention probabilities via the postures. In the process, we used questionnaires to understand people's cognition of the four postures and then used statistical analysis to generalize the implicit behavioral intentions. In addition, used the statistics data to build HMMs. Through both simulation and experimental results, we proved time-varying fuzzy hidden Markov models can flowing the postures and environments to approximate the real behavior intentions.

Abstract in Chinese.......I
Abstract in English.......II
Contents............III
List of Figures......V
List of Tables......VII
Chapter 1 Introduction.....1
1.1 Problem Statement......2
1.2 Research Motivation and Objective......2
1.3 Background......4
1.4 Thesis Outline......7
Chapter 2 Human behavior intention inference model.....8
2.1 Psychology model......9
2.2 Statistical Questionnaire......12
2.3 Markov Probability method......20
2.4 Time-varying Fuzzy Hidden Markov Models......24
Chapter 3 Experimental and Simulation......33
3.1 Simulate the Behavior Intention Probabilities for Time-varying Fuzzy Hidden Markov Models......33
3.2 Experiment the Human Behavior Intention Probabilities using Time-varying Fuzzy Hidden Markov Models.....41
Chapter 4 Conclusions......46
References........47
List of Figures
2.1	Traditional the theory of planned behavior .....10
2.2	Behavior intention inference model....12
2.3	Box plot of the posture risk index.....17
2.4	Box plot of the risk index for behavior intention states in p1 and p2	.....19
2.5	Box plot of the risk index for behavior intention states in p3....19
2.6	Box plot of the risk index for behavior intention states in p4....20
2.7	The Markov probabilities between the physiological states in outdoor 	....21
2.8	The Markov probabilities between the physiological states in living room....22
2.9	The Markov probabilities between the physiological states in office	.....23
2.10	The Markov probabilities between the physiological states in special time.....24
2.11	The fuzzy membership functions....31
3.1	The intention inference method schematic diagram.....34
3.2	Simulate the behavior intention probabilities of p1 and p2 in the outdoor environment; a) fixed the time and b) fixed the hands distance....35
3.3	Simulate the probabilities of p3 in the outdoor environment; a) fixed the time and b) fixed the hands distance.....36
3.4	Simulate the probabilities of p4 in the outdoor environment; a) fixed the time and b) fixed the hands distance.....36
3.5 Simulate the behavior intention probabilities of p_1 at different environments; a, b) outdoor environment and c, d) living room environment....37
3.6 Simulation the probabilities of p_3 at living room; a) fixed the time and b) fixed the hands distance....38
3.7 	Simulation the probabilities of p_4 at living room; a) fixed the time and b) fixed the hands distance....38
3.8 	Simulation the probabilities of p_3 at office; a) fixed the time and b) fixed the hands distance....39
3.9 	Simulation the probabilities of p_4 at office; a) fixed the time and b) fixed the hands distance....39
3.10 	Simulation the probabilities of p_3 at special time; a) fixed the time and b) fixed the hands distance....40
3.11 	Simulation the probabilities of p_4 at special time; a) fixed the time and b) fixed the hands distance....40
3.12	Human machine interface....41
3.13 Experiment the behavior intention probabilities in different postures via the human machine interface: a) the p_1 in the outdoor; b) the p_2 in the outdoor....43
3.14 Experiment the behavior intention probabilities of p_1 in time via human machine interface: a) time is 5sec in the outdoor; b) time is 10sec in the outdoor; c) time is 15sec in the outdoor; d) time is 25sec in the outdoor	....44
3.15 	Experiment the behavior intention probabilities of p_1 in different environments via human machine interface: a) outdoor; b) living room; c) office; d) special time....45
List of Tables
1.1	Example of a Markov model: Probabilities of tomorrow’s weather based on today’s weather....5
1.2	Example of a hidden Markov model: Probability of someone carrying an umbrella based on the weather....5
2.1	The math symbols....8
2.2	Statistic results for human behavior intention probabilities: a hand touching the head (p1) and two hands touching the head (p2)....14
2.3	Statistic results for human behavior intention probabilities: hands touching the chest (p3)....14
2.4	Statistic results for human behavior intention probabilities: hands touching the belly (p4)....15
2.5	The physiological states of the coefficient of variation (CV) in different environments and times.....17
2.6	Fuzzy rule table.....32

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