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研究生:樸安杰
研究生(外文):ANDREY PUZANKOV
論文名稱:應用於健康監測的皮膚電阻建模與分析
論文名稱(外文):Modeling and Analysis of Electrodermal Resistance for Health StatusMonitoring
指導教授:祝國忠祝國忠引用關係
指導教授(外文):KUO-CHUNG CHU
口試委員:戴敏育陳彥宏祝國忠
口試委員(外文):MIN-YUH DAYYEN-HUNG CHENKUO-CHUNG CHU
口試日期:2020-07-06
學位類別:碩士
校院名稱:國立臺北護理健康大學
系所名稱:國際健康科技碩士學位學程
學門:生命科學學門
學類:生物科技學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:英文
論文頁數:208
中文關鍵詞:皮膚電位反應大數據分析穴位人體能量醫療保健監測中醫
外文關鍵詞:electrodermal responsebig data analysisacupointshuman body energyhealthcare monitoringTCM
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當今,高齡化和健康監測已成為熱門話題,到2050年,超過60歲的人口比例將從12%增加到22%。這群人通常受各種疾病影響的風險最高,他們需要適當的醫療保健,並進行定期檢查和健康監測,以維持高品質的生活。Automatic Reflective Diagnosis Komplex (ARDK)是一種複合系統,用於測量手腕和腳踝兩側24個穴位的皮膚電位反應。它可以一種健康監測的方法,此系統包含測量皮膚電導能力的設備以及收集、分析數據和產生報告的軟體。透過本研究可更佳理解穴位電阻與人類年齡間的關係,並且藉由分析皮膚的電阻來確認整體體健康狀況與生理年齡。本論文深入分析ARDK設備收集了八年以上的大型資料集,共有154922筆記錄。資料集依性別、年齡分組,以T檢定檢驗男女的平均ARDK值差異是否有統計顯著性;利用單因子變異數分析檢驗年齡分組間的平均ARDK值否存在顯著差異。

為了找出年齡與平均ARDK值之間的關係(模型1),以及男女性組與平均ARDK值之間的關係(模型2),則使用非線性最小平方回歸分析法。T檢定結果顯示,性別在平均ARDK值存在顯著差異,而單因子變異數分析和事後分析結果,亦顯示不同性別年齡分組之平均ARDK值存在顯著差異。回歸分析顯示,兩種模型的年齡與平均ARDK值之間均呈對數關係。ARDK平均值隨年齡增長而降低。我們建立的模型可以依據用邊界值作為標準差的一部分來確定健康與否。使用這些模型分類後的資料集,模型1結果顯示不健康(≈55.3%)比健康(≈44.7%)多。模型2結果顯示,不健康的約55%,健康的約45%;其中,男性的能量比例高於女性。

信賴區間和變異分析顯示,皮膚導電能力(生物能)在童年和年輕時較不穩定,20歲後變得較穩定。但是,老年後再次變得不穩定。
由於過往此領域的研究並不多,且使用資料量也相當少,本研究可補先前研究之不足。本研究有大量可用資料與較廣泛的年齡區間,有助於確認年齡和平均ARDK值之間的實質關係。此外,本研究亦提升軟體演算法的效能與精確性,具有實務應用的意義。
Nowadays, ageing and health monitoring have become trending topics. The proportion of population aging over 60 years old will increase from 12% up to 22% by 2050. This group of people is usually the most affected by the high risk of various diseases. It needs proper healthcare and regular check-ups in hospitals and monitoring to maintain a high quality of life. The Automatic Reflective Diagnosis Komplex (ARDK) is the complex (system) developed to measure the electrodermal response of 24 special acupuncture points on both sides of the body's wrists and ankles. It could be a solution for health monitoring. The system includes the device for measuring skin conductance and the software for analyzing and getting reports based on collected data by the device.
This study has been conducted to better understand the relationship between resistance of acupuncture points and human age and also proposes a method for determining the general state of health and biological age by analyzing the skin's electrical resistance in biologically active points. The paper explores a large dataset that has been being collected for over eight years with the ARDK device and contains 154922 records.

The dataset has been split into gender groups and different age groups. T-test has been performed to test the hypothesis that the male group and the female group were associated with statistically significantly different mean ARDK values. One-way ANOVA has been performed to test if there are significant differences in the mean ARDK value between age groups. Non-linear least squares regression analysis has been done to find out the relationship between age and the mean ARDK value for the whole dataset together (Model 1) and also separately for male group and female group (Model 2). The results of the t-test showed significant differences in the mean ARDK value between gender groups, and the results of one-way ANOVA and post hoc analysis showed significant differences in the mean ARDK value between all age groups.
Regression analysis showed a logarithmic relationship between age and the mean ARDK value for both models. The mean ARDK value decreases with increasing age. The models we built can determine healthy from unhealthy by using boundaries as parts of standard deviation. After the whole dataset has been classified with these models, it seems that there are more unhealthy examinations (≈55.3%) than healthy (≈44.7%) in the dataset (Model 1) and ≈55% of unhealthy and ≈45% of healthy examinations (Model 2). Model 2 also showed that males have a higher energy rate than females during their lifetime.

Confidence intervals and data variance analysis showed that skin conductance (bioenergy) is less stable during childhood and younger age and becomes more stable after the age of 20. However, it becomes unstable again in elderly age.
This research helped to complement previous existing studies as there are not so many of them in this field, and they used a much smaller amount of data. The study contributed to the current understanding of how the real relationship between age and the mean ARDK value looks like with a larger amount of data available and a bigger age range.
Also, it helped to improve software algorithms, as practical implications, so that reports are even more accurate than they were before.
Abstract ....................................................................................................................................... i
Contents ................................................................................................................................... iii
Figures........................................................................................................................................ v
Tables ........................................................................................................................................ ix
Chapter I: Introduction ............................................................................................................... 1
Section I: Background and Motivation .......................................................................... 1
Section II: Research Problems ....................................................................................... 3
Chapter II: Literature Review .................................................................................................... 6
Section I: Acupuncture and Human Body ...................................................................... 6
Section II: Data Analysis .............................................................................................. 13
Chapter III: Research Methodology......................................................................................... 21
Section I: Research Scheme ......................................................................................... 21
Section II: Materials ..................................................................................................... 22
Section III: Software and Tools .................................................................................... 26
Section IV: Analysis ..................................................................................................... 26
Chapter IV: Results and Discussion ......................................................................................... 29
Section I: Results ......................................................................................................... 29
Section II: Discussion .................................................................................................. 59
Chapter V: Contribution, Suggestions and Conclusion ........................................................... 64
Section I: Contribution ................................................................................................. 64
Section II: Suggestions ................................................................................................. 64
Section III: Conclusion ................................................................................................ 65
References ................................................................................................................................ 67
Appendices ............................................................................................................................... 72
Appendix I: Privacy Policy and Informed Consent ..................................................... 72
Appendix II: Jupyter Notebook Output........................................................................ 77
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