隨著世界人口老化，居家照護及長期健康監測的需求也隨之增加。針對此種需求，無線人體局域網被提出並作為一種可行的方案。然而，此種長期監測的無線系統伴隨著可觀的功率消耗，其大部分來自於無線傳輸的部分。因此，本論文設計並評估了用以降低無線傳輸資料量的前端壓縮裝置。因其低複雜度和隨之而來的低系統實現功率，本論文選定有損壓縮的曲率偵測以及無損壓縮的霍夫曼編碼做為資料的前端壓縮裝置。應用於心電訊號壓縮的霍夫曼編碼系統使用了預先定義的編碼表。該架構透過極度簡化的編碼表提供2.16倍的壓縮倍率，相當接近於最佳編碼的2.53倍。本論文使用此預定義簡化編碼表在FPGA上實現霍夫曼編碼裝置並加以測試。此外本論文檢視了應用於心電訊號、肌電訊號以及步態訊號的曲率偵測並在微控制器上實現具有目標壓縮倍率追蹤機制的演算法且其目標倍率可隨使用者調整。曲率偵測系統應用於心電訊號時，在2.23倍的壓縮倍率下具有1.7%的均方根誤差百分比，在10倍的壓縮倍率下具有5.5%的均方根誤差百分比。最後，曲率偵測演算法也在無線傳收器上進行實現並驗證、展示其於人體局域網的適用性。依不同的處理器與傳收器裝置功率比，目標壓縮倍率為10的測試結果能節省3%到75%的功率。本論文提出的低複雜度演算法和其他演算法相比，能用更低的運算功率為系統帶來更優化的功率消耗。

With the aging of the world population, the importance of long-term medical monitoring has increased along with the need for home care services for the elderly. To serve these needs, a body area network (BAN) is proposed as a possible solution. A system for long-term monitoring with wireless data transmission consumes a considerable amount of power, a significant portion of which is dissipated by the wireless transmitter. Therefore, front-end compression circuits are designed and evaluated in this study to reduce the required data rate of wireless transmission. Compression circuits based on curvature detection (providing lossy compression) and Huffman coding (lossless compression) are selected due to their low complexity and resulting low-power implementation. Huffman coding for ECG compression is designed using a pre-defined coding table. It is shown that truncating the table at a very shallow depth yields a compression factor (CF) of 2.16, very close to the optimum result of 2.53 using the complete table. This truncated table yields a compact hardware, implemented and tested in this study using a FPGA. Furthermore, the method of curvature detection for ECG, EMG and gait pattern compression is examined and an algorithm including target CF tracking is implemented in software on a microcontroller. The target CF is user programmable. Measured results demonstrate a CF of 2.23 and PRD error of 1.7%, and CF of 10 with PRD of 5.5% for ECG compression. Finally, the curvature algorithm is implemented on a wireless transceiver to demonstrate its suitability for BAN application. It is concluded that a CF of 10 yields system power reduction between 3% and 75% depending on the relative power consumption of the data processor and the transmitter. This power advantage is achieved by the low complexity algorithms described in this thesis, yielding low computation energy per compressed sample as compared with many other algorithms for ECG/EMG compression.

致謝 i
摘要 ii
Abstract iii
Contents v
List of Figures viii
List of Tables xi
Chapter 1 INTRODUCTION 1
1.1 Background 1
1.2 Thesis organization 2
Chapter 2 WIRELESS FRONT-END COMPRESSION/RECONSTRUCTION SYSTEM 4
2.1 Motivation and background 4
2.2 Comparison of compression principles 5
2.2.1 Compressed sampling 5
2.2.2 Curvature detection 6
2.2.3 Other approaches: RLE, LZW, and Huffman 7
Chapter 3 CURVATURE DETECTION 9
3.1 Introduction 9
3.2 Metrics of the curvature compression system 10
3.3 Implementation 12
3.3.1 Compression 12
3.3.2 Reconstruction 14
3.4 Simulated results 15
Chapter 4 HUFFMAN COMPRESSION SYSTEM 20
4.1 Introduction 20
4.2 Implementation of Huffman compression system 22
4.2.1 Signal partitioning 22
4.2.2 Code book definition 23
4.3 Huffman encoder module hardware design 28
4.3.1 Differential stage 30
4.3.2 Huffman coding stage 30
4.3.3 FIFO stage 31
4.4 Huffman decoder module 32
4.4.1 FIFO stage 32
4.4.2 Huffman decoding stage and reconstruct stage 32
Chapter 5 IMPLEMENTATION AND MEASUREMENT RESULTS 34
5.1 Huffman compression system testing 34
5.1.1 Testing environment 34
5.1.2 Measurement with body-signal signal input 35
5.2 Curvature compression system testing 36
5.2.1 Testing environment 36
5.2.2 Measurement with body-signal signal input 38
5.2.3 Wireless transmission 46
5.3 Comparison 52
Chapter 6 CONCLUSION AND FUTURE WORK 56
References 58

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