臺灣博碩士論文加值系統

感知科技和無線網路的進步，促成了「無線慣性感測網路」的發展。其可藉由在人體上，穿戴無線傳輸的慣性感測器，捕捉肢體的動作，並可應用在包含醫療照顧、電子遊戲和情緒運算上。對於建置高無線傳輸效率、高動作捕捉精度的感測平台所需要的技術，我們在此進行了三項基礎性的研究。

第一項研究，探討利用資料壓縮，來克服無線慣性感測網路中的感測資料收集議題。我們觀察到，雖然相鄰的感測節點可能激烈地競爭頻寬，但肢體移動時，其感測資料通常含有些許重覆，甚至是強烈的時、空相關性。我們為無線慣性感測網路，特別設計壓縮演算法，以適用於其感測節點可監聽彼此傳輸的特性。為了有效利用監聽的機制，我們將無線慣性感測網路上的資料壓縮問題，建模為在監聽圖上的組合最佳化問題，證明其計算復雜度，並展示有效的計算方法。我們亦探討如何設計支援此壓縮模型的無線媒體存取層協定。實驗回報了以皮拉提斯的醫療復建動作進行的案例分析。結果顯示，我們的解決方法，可比先前研究節省百分之七十以上的傳輸資料量。

不同於第一項研究中，每個節點僅可容許監聽至多$\kappa = 1$個其它節點的傳輸，在第二項研究裡，我們進一步考慮「複數空間相關性」，延伸$\kappa = 1$到$\kappa > 1$，並建構部分排序性的「有向無環圖」來表示感測節點間的壓縮相依性。相較於$\kappa = 1$時，可在多項式時間內找到最小成本生成樹，
我們證明即使$\kappa = 2$，尋找最小成本的有向無環圖也是具NP完備性的。
之後我們提出有效率的經驗性演算法，並用真實感測資料驗證其效能。

除了資料收集，在第三項研究中，我們亦感興於在人體上佈置多個加速度計，以追蹤人體的動作。其中一個重要議題是如何計算重力的方向。這是很有挑戰的問題，尤其是當肢體在持續移動時。假設已將多個加速度計擺置於人體的一個剛性肢節上，一篇近期的論文提出一個資料融合的方法，可量測此剛體座標上重力的方向。然而，它並未探討，如何找到最佳的擺放位置，以達到最小的量測誤差。
在此，我們定義此擺置最佳化問題，並提出兩個經驗性演算法，名之為「基於梅式取樣之擺置法」與「最大間距法」。模擬與真實試驗的結果，亦顯示我們的方法，在多種幾何形狀的剛體上，都能有效地找出近似解。

The advance of sensing technology and wireless communication has boosted body-area inertial sensor networks (BISNs), in which wireless wearable inertial sensor nodes are deployed on a human body to monitor its motion. Applications include medical care, pervasive video games, and affective computing. We conduct fundamental research into the technologies required to create an efficient wireless communication BISN that maximizes motion tracking accuracy and data collection efficiency.

The first work addresses data collection issues in BISNs by data compression.
We observe that, when body parts move, although sensor nodes in vicinity may compete strongly with each other, the transmitted data usually exists some levels of redundancy and even strong temporal and spatial correlations. Our scheme is specifically designed for BISNs, where nodes are likely fully connected and overhearing among sensor nodes is possible. We model the data compression problem for BISNs, where overhearing should be efficiently utilized, as a combinatorial optimization problem on overhearing graphs. We show its computational complexity and present efficient algorithms.
We also discuss the design of the underlying MAC protocol to support our compression model. An experimental case study in Pilates exercises for patient rehabilitation is reported. The results show that our schemes reduce more than 70% of overall transmitted data compared with existing approaches.

Based on the first work, where a node is allowed to overhear at most $\kappa = 1$ node's transmission, in the second work, we consider multi-spatial correlations by extending $\kappa = 1$ to $\kappa > 1$ and constructing a partial-ordering directed acyclic graph (DAG) to represent the compression dependencies among sensor nodes. While a minimum-cost tree for $\kappa = 1$ can be found in polynomial time, we show that finding a minimum-cost DAG is NP-hard even for $\kappa = 2$. We then propose an efficient heuristic and verify its performance by real sensing data.

In addition to data collection, in the third work, we are interested in tracking human postures by deploying accelerometers on a human body. One fundamental issue in such scenarios is how to calculate the gravity. This is very challenging especially when the human body parts keep on moving. Assuming multiple accelerometers being deployed on a rigid part of a human body, a recent work proposes a data fusion method to estimate the gravity vector on that rigid part. However, how to find the optimal deployment of sensors that minimizes the estimation error of the gravity vector is not addressed. In this work, we formulate the deployment optimization problem and propose two heuristics, called Metropolis-based method and largest-inter-distance-based (LID-based) method. Simulation and real experimental results show that our schemes are quite effective in finding near-optimal solutions for a variety of rigid body geometries.

Abstract (Chinese) i
Abstract ii
Acknowledgement iv
Contents v
List of Tables vii
List of Figures viii
1 Introduction 1
1.1 Backgrounds and Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1.1 Prototyping Experience: Home Rehabilitation . . . . . . . . . . . . . . 3
1.1.2 Prototyping Experience: Multi-Screen Cyber-Physical Video Game . . 5
1.2 Issues Addressed in this Dissertation . . . . . . . . . . . . . . . . . . . . . . . 10
1.3 Organization of this Dissertation . . . . . . . . . . . . . . . . . . . . . . . . . 12
2 Data Compression by Temporal and Spatial Correlations in a BISN 13
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2 Related Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.3 BISN System Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.4 Design of Data Compression by Overhearing . . . . . . . . . . . . . . . . . . 18
2.4.1 Basic Idea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.4.2 Data Compression Model . . . . . . . . . . . . . . . . . . . . . . . . 21
2.4.3 Algorithms for Constructing the TX Order Tree T . . . . . . . . . . . 25
2.4.4 Design Issues of the Underlying MAC Protocol . . . . . . . . . . . . . 27
2.4.5 Retraining the System . . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.5 Experiment Results: A Case Study in Pilates Exercises . . . . . . . . . . . . . 29
2.5.1 Experiment Setup and Data Collection . . . . . . . . . . . . . . . . . . 29
2.5.2 Effects of γ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
2.5.3 Compression Ratio under Ideal Channel Condition . . . . . . . . . . . 32
2.5.4 Compression Ratio under Non-Ideal Channel Condition . . . . . . . . 32
2.5.5 Energy Cost . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
2.5.6 Scalability Issue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
2.5.7 Effect of System Retraining . . . . . . . . . . . . . . . . . . . . . . . 39
2.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3 Exploiting Multi-Spatial Correlations of Motion Data for Data Compression 42
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
3.2 Multi-Spatial Data Compression . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.2.1 Problem Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.2.2 Complexity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.2.3 A Greedy Cycle Breaker Heuristic . . . . . . . . . . . . . . . . . . . . 46
3.3 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
3.3.1 Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3.3.2 Compression under Ideal Channels . . . . . . . . . . . . . . . . . . . 47
3.3.3 Compression under Lossy Channels . . . . . . . . . . . . . . . . . . . 48
3.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4 Inertial Sensor Deployment Methods for Gravity Measurement 51
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
4.2 Related Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
4.3 Gravity Measurement and Deployment Optimization Problems . . . . . . . . . 54
4.4 Optimization Heuristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
4.4.1 Metropolis-Based Method . . . . . . . . . . . . . . . . . . . . . . . . 56
4.4.2 Largest-Inter-Distance-Based Method . . . . . . . . . . . . . . . . . . 57
4.5 Simulation and Experimental Results . . . . . . . . . . . . . . . . . . . . . . . 61
4.5.1 Simulation Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
4.5.2 Comparison of Accuracy and Execution Time . . . . . . . . . . . . . . 63
4.5.3 Effects of the Number of Sensors . . . . . . . . . . . . . . . . . . . . 64
4.5.4 Real Gravity Measurement . . . . . . . . . . . . . . . . . . . . . . . . 64
4.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
4.7 Appendices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
4.7.1 Proof of Theorem 4.4.2 . . . . . . . . . . . . . . . . . . . . . . . . . . 65
4.7.2 Proof of Theorem 4.4.3 . . . . . . . . . . . . . . . . . . . . . . . . . . 68
5 Conclusions 77
Bibliography 78
Curriculum Vitae 83

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