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研究生： 徐卓朗
Chui, Cheuk-Long
論文名稱： 穿戴式經絡儀
Acusense - long-term multi-meridian monitoring using E-tattoo
指導教授： 藍崑展
Lan, Kun-Chan
學位類別： 碩士
Master
系所名稱： 電機資訊學院 - 資訊工程學系
Department of Computer Science and Information Engineering
論文出版年： 2024
畢業學年度： 112
語文別： 英文
論文頁數： 103
中文關鍵詞： 穴道 、生物阻抗 、穿戴式裝置 、石墨烯電極紋身貼紙(GETs) 、皮膚電反應(GSR) 感測器
外文關鍵詞： Acupuncture points, Bioimpedance,, Wearable devices, Graphene Electrode Tattoo (GETS), Galvanic skin response (GSR) sensors
相關次數： 點閱：36  下載：1 
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    • 在傳統中醫領域（TCM）中，脈診是診斷疾病和健康狀況的重要方法之一。在先前穴道相關的 研究中，不同學者已發表過基於生物阻抗的經絡監測裝置並對穴道進行高精度之測量。然而， 這些裝置通常體積龐大、重量沉重且不可穿戴。即使是可穿戴之原型機，大多數裝置僅能進行 單點測量，缺乏多點測量的機能。

    本研究提出了「Acusense」穿戴式經絡儀原型機。此原型機是一種創新的可穿戴式多通道經絡 監測裝置。與先前研究中所提出的經絡儀相比，Acusense 具有輕量化和使用電池驅動等設計 ，並使用石墨烯電極紋身貼片（GETs）作為感測電極，對六個穴道進行同時生物阻抗測量。此 外，此研究中亦提出了一種概念驗證的雲端連接遠程監測系統，實現數據即時傳輸到後端服 務器，並通過手機應用程式進行存取及連接等之機能驗證。

    為了驗證Acusense 的可用性和精確性，本研究亦有對所提出的裝置原型機進行電路和醫療 級設備比較驗證測試。測試結果顯示，Acusense 在電路驗證中達到了少於1% 的測量誤差。 而在設備比較驗證中，在與醫療級的測量結論比較下亦達到了平均4.9% 的通道測量誤差， 此結果與先前研究中使用類近取樣方式之測量設備準確率相約。

    最後，本研究對Acusense 在血糖監測中的潛在應用方向進行了一項可行性研究。可行性研 究實驗中共涉及六名受試者，初步結果顯示，Acusense 的生物阻抗測量與血糖趨勢之間存在 一定的相關性，相關係數範圍為0.73 到0.95。這些研究結果證實了AcuSense 作為準確的 可穿戴經絡監測工具的能力，並表明其作為基於非侵入性穴道生物阻抗血糖監測工具的潛力 ，為將來的患者和醫療保健提供者等提供顯著的好處。

    In traditional Chinese medicine (TCM), pulse diagnosis is one of the most important methods for diagnosing illnesses and health conditions. Previous research efforts have introduced bioimpedance-based devices for meridian monitoring, demonstrating high measurement accuracy. Nonetheless, these devices have typically large size, heavy weight and are non-wearable. For wearable prototypes, most devices only take single point measurements and lack the ability for multi-point measurements.

    This study introduces "Acusense," an innovative wearable multi-channel meridian monitoring device. Compared with prototypes proposed by prior works, Acusense featured a lightweight and battery-operated design, and capacity to measure bioimpedance across six channels using graphene electrode tattoos (GETs) as sensing electrodes. Additionally, a proof-of-concept cloud-connected remote monitoring system has been proposed, enabling real-time data transmission to a backend server accessible via a user-friendly mobile application.

    To validate the usability and precision of Acusense, electrical and known device validation tests were conducted on the prototype. Results from these tests revealed Acusense achieving less than 1% measurement error in electrical validation and an average channel measurement error of 4.9% in known device validation when compared against a medical-grade meridian measuring device.

    Furthermore, a feasibility study was conducted to explore Acusense's potential application in blood glucose monitoring. Initial findings from this study involving six subjects demonstrated strong correlations between Acusense's bioimpedance measurements and blood glucose trends, with correlation coefficients ranging from 0.73 to 0.95. These findings affirm the capability of AcuSense as an accurate wearable meridian monitoring tool and suggest its potential as a non-invasive acupoint bioimpedance based blood glucose monitoring tool, offering significant benefits to patients and healthcare providers alike.

    摘要 3 Abstract 4 致謝 5 List of Tables 8 List of Figures 9 Chapter 1. Introduction 13 1.1 Skin bioimpedance in western medicine 13 1.2 Acupoint impedance association with diseases 14 1.3 Problem with the current acupoint bioimpedance monitoring devices 15 1.4 Usefulness of E-tattoo as a replacement to traditional electrodes 16 1.5 Estimating blood glucose with acupoint bioimpedance 18 1.6 Contributions & novelty of this research 19 Chapter 2. Related Work 22 2.1 Prior work in studying bioimpedance signals measured at the acupoints 22 2.2 Prior work in designing a device for measuring bioimpedance at the acupoints 22 2.3 Prior work in studying how diseases are associated with the acupoints bioimpedance 27 2.4 Prior work in using E-tattoo for wearable device 28 2.5 Prior work in using bio-signals to infer blood sugar 28 2.6 Novelty of the proposed design 32 Chapter 3. Methodology 34 3.1 Wearable Sensor Prototyping 37 3.1.1 Design of the Sensor Array Module 37 3.1.2 Power & Power Isolation 39 3.1.3 System Integration & Portability 42 3.2 GET Electrodes and Preparations 44 3.2.1 Properties of GET Electrodes 44 3.2.2 Method of Preparing GET Electrodes 44 3.2.3 Method of Attaching GET Electrodes to Acupoint of Interests 45 3.3 Smartphone App 48 3.4 Data Collection Backend 51 Chapter 4. Experiment & Design Validation 52 4.1 Validation experiment design 52 4.1.1 Electrical / Circuitry validation using fixed value resistors 52 4.1.2 Known device validation using ARDK 53 4.1.3 Feasibility study of blood glucose trend estimation using Acusense 54 4.1.3.1 Subject Screening and Pilot Experiment 55 4.1.3.2 Properties of using acupuncture point in blood glucose correlation 58 4.1.3.3 Benchmarking Device 58 4.1.3.4 Choice of experiment food 59 4.1.3.5 GETs electrode preparation & attachment in experiment 60 4.1.3.6 Gel electrode application method 61 4.2 Sensor data alignments 61 4.3 Blood glucose trend trend feature extraction 63 4.4 Experiment results 64 4.4.1 Acusense vs Fixed Resistor Validation Result 64 4.4.2 Acusense vs ARDK Accuracy Validation Results 64 4.5 Feasibility of using Acusense for blood glucose trend estimation 65 4.5.1 Correlation between blood glucose trend and acupoint bio-impedance 65 4.5.2 Correlation between predictions correlation and subject base values 69 4.5.3 Bio-impedance reading drop when blood-glucose peaked 71 Chapter 5. Discussion 73 5.1 Special Cases Study 73 5.1.1 Data on subject with diabetes family history 73 5.1.2 Data on low correlation case 75 5.2 All Channel Results Overview 76 5.3 Potential Method for picking correct channel for regression analysis 76 Chapter 6. Limitation 78 Chapter 7. Future Works 81 Chapter 8. Conclusion 83 References 84 Appendix 88 PCB Layouts 88 Software and Codes 91 Sensor Module Sampling Algorithm(ATmega328, Arduino IDE) 91 Acusense Mobile App 94 App data type constructor 94 Bluetooth data receival 94 Data sync function between mobile app and server 96 Data Recording Server 98 File Write Queue and Consumer Producer writer 99 Correlation Algorithm 101

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