Publication: 生物阻抗與類神經網路應用於優秀運動員的身體組成分析
| dc.contributor.advisor | 陳裕鏞 | |
| dc.contributor.advisor | Chen, Yue-Yawn | |
| dc.creator | 王家偉 | |
| dc.creator | Wang, Chia-Wei | |
| dc.date | 2010 | |
| dc.date.accessioned | 2017-02-27T06:08:07Z | |
| dc.date.accessioned | 2025-07-30T15:25:49Z | |
| dc.date.available | 2017-02-27T06:08:07Z | |
| dc.date.issued | 2017-02-27T06:08:07Z | |
| dc.description | 學位類別：碩士 | |
| dc.description | 校院名稱：國立台灣體育大學 | |
| dc.description | 系所名稱：體育研究所 | |
| dc.description | 學號：19701004 | |
| dc.description | 畢業學年度：98年 | |
| dc.description | 論文頁數：57頁 | |
| dc.description.abstract | 生物阻抗分析法（BIA）可簡便、快速、無侵入性地估測身體組成。研究指出估測方程式是造成BIA不準確的主要原因，更建議要依受測者條件選擇適合的估測方程式。 本篇研究以24名台灣體院男子足球隊員為受測者（年齡20.3±1.9歲，身高173.6±5.6 cm，體重66.1±5.3 kg），以雙能X光吸收儀（DEXA）測量之身體組成為效標，比較生物阻抗分析儀（BC-418）所估算出的去脂肪質量（FFM），以BIA所測得的阻抗值搭配年齡、身高及體重等參數分別以線性迴歸（LR）的迴歸方程式及類神經網路（ANN）估測身體組成。結果顯示，LR的r2=.897，RMSE=1.678 kg；五個神經元ANN的r2=.996，RMSE=0.328 kg，ANN優於LR。LR與ANN對DEXA的FFM偏差值（bias）均約為0 kg，BC-418對DEXA的bias= - 0.628 kg，LR與ANN優於BC-418的內建估測方程；LR、BC-418及ANN對DEXA的FFM偏差範圍（2 S.D.）分別為3.357、3.958及0.656 kg，ANN優於LR及BC-418。結論：本篇研究以類神經網路估測足球員身體組成優於線性迴歸及生物阻抗分析儀（BC-418）內建方程的估測結果。 | |
| dc.description.abstract | Bioelectrical impedance analysis (BIA) can estimate body composition easily, rapidly and non-invasively. Some papers have indicated that the accuracy of predictive equations of BIA mainly depend on the equations itself, even, the specific subjects need specified equation. The purpose of this study was to estimate body composition of the football players with the BIA measurement compared to Dual-energy X ray absorptiometry (DEXA).Method: Subjects, 24 football players of National Taiwan College of Physical Education, with mean age at 20.3±1.9 years, mean height at 173.6±5.6 cm and mean weight at 66.1±5.3 kg. To evaluate the accuracy of predictive fat-free mass (FFM) of body composition by bioelectrical impedance analyser (BC-418), DEXA, as criteria method, was compared. By the measured data as factors including the bioelectrical impedance values (Z) of hand-to-foot modulation by BIA in right side, gender, age, height, and weight, the predictive equation by traditional linear regression analysis (LR) for FFM by DEXA was gained, also, the ANN predictive model created. Result: The lower r2 and greater RMSE in LR (r2=.897 and RMSE=1.678 kg) than in ANN (r2=.996 and RMSE=0.328 kg) were gained. ANN is better than LR. The biases that FFM of LR and ANN compare to DEXA are about 0 kg. The bias that FFM of BC-418 compares to DEXA is -0.628 kg. LR and ANN are better than BC-418. The FFM’s range of bias (2 SD.) of FFM of LR, BC-418 and ANN compared to DEXA are 3.357, 3.958 and 0.656 kg, respectively. ANN is better than LR and BC-418. Conclusion: To estimate body composition of football players, ANN is more applicable than LR and estimated equation of BC-418. | |
| dc.description.tableofcontents | 目 次 第一章 緒論……………………………………………………………1 第一節 問題背景……………………………………………………2 第二節 研究目的……………………………………………………4 第三節 研究假設……………………………………………………4 第四節 研究範圍與限制……………………………………………4 第五節 操作性定義…………………………………………………4 第二章 文獻探討………………………………………………………6 第一節 肥胖、體脂肪與健康………………………………………6 第二節 身體組成模式的分類………………………………………7 第三節 身體組成的評估方式………………………………………9 第四節 身體組成測量對運動員的重要性…………………………16 第五節 生物阻抗分析………………………………………………18 第六節 BIA在運動方面的應用………………………………………28 第七節 類神經網路…………………………………………………30 第三章 研究方法………………………………………………………35 第一節 研究對象……………………………………………………35 第二節 實驗設計與流程……………………………………………35 第三節 資料處理與統計分析………………………………………36 第四章 結果與討論……………………………………………………39 第一節 FFM的估測方程式……………………………………………39 第二節 估測運動員FFM之比較………………………………………41 第三節 估測運動員體脂肪百分率之比較…………………………44 第五章 結論……………………………………………………………48 參考文獻…………………………………………………………………50 | |
| dc.format.extent | 788224 bytes | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | https://ir.ntus.edu.tw/handle/987654321/70953 | |
| dc.language | zh-TW | |
| dc.publisher | 體育研究所 | |
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| dc.subject | 生物阻抗分析;類神經網路;雙能X光吸收儀;去脂肪質量;足球員 | |
| dc.subject | Bioelectrical impedance analysis (BIA);Artificial neural network (ANN);Dual-energy X-ray absorptiometry (DEXA);fat-free mass (FFM);football player | |
| dc.title | 生物阻抗與類神經網路應用於優秀運動員的身體組成分析 | |
| dc.title | THE APPLICATION OF BIOELECTRICAL IMPEDANCE ANALYSIS BY NEURAL NETWORKS APPLIED IN EVALUATION OF BODY COMPOSITION IN ELITE ATHLETES | |
| dc.type | thesis | |
| dspace.entity.type | Publication |
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