量化骨盆肌肉訓練的感測器數據

The Quantification of Sensor Data for Pelvic Floor Muscle Training

郭兆原(Zhao-Yuan Quek)

指導教授 ： 陳瑞發

淡江大學/工學院/資訊工程學系碩士班/碩士(2012年)

https://doi.org/10.6846/TKU.2012.00320

摘要

治療尿失禁的復健方式，分為侵入式復健和非侵入式復健兩大類。但因涉及個人私密性問題，患者普遍對於侵入式復健感到排斥，使得復健效果不佳。研究中，採用非侵入式的復健，但由於傳統的骨盆底肌肉訓練，患者自行施作時，容易訓練到錯誤的肌肉部位，導致效果不彰。所以提出改良式骨盆底肌肉訓練系統，將復健器材與感測器結合，並透過無線模組將數據傳輸到行動載具上，進行數據的量化、分群與分類，給予患者即時性的回饋，提昇整體療效。
本論文著重在量化骨盆底肌肉訓練的數據，進行數據量化時，若包含前後極端值或突波的情況，會產生失真的情形。分析下列方法進行量化:Average Method、Twice Average Method、James Method、Average Weight Method、Ramer Douglas Peucker Algorithm。並透過比較與驗證，找出適合本系統的骨盆肌肉訓練數據的量化方法，使得量化復健數據後，獲得更能代表實際施力的特徵。

關鍵字

骨盆肌肉訓練 ； 量化分析 ； Ramer Douglas Peucker演算法

並列摘要

The way to threat Incontinence can be divided into Invasive and Non-Invasive. But for the personal privacy, the patients often commonly discriminate the Invasive rehabilitation, which leads to an ineffective result. In this research, we found taking traditional Pelvic Floor Muscle Training is not easy for patients to train muscle in the right position especially when exercise the training themselves. So, we propose a data feedback system to achieve improving traditional Pelvic Floor Muscle Training effectiveness. The way of this system is to link rehabilitation equipment and the sensor and then through wireless device deliver the collected data, which is analyzed by comparison in place of quantification, cluster and classification, into rehabilitation equipment giving patients a real-time feedback to the patients and elevating the whole efficacy.
This thesis is to focus on the Pelvic Floor Muscle Training data comparison. To avoid data distortion, we exclude outlier and surge to explore the reliable and effective data, which can be widely used in improving Pelvic Floor Muscle Training effectiveness, we use Average method, Twice Average Method, James Method, Average Weight Method, RDP Method as an analysis basis to make data comparison and verification. Through comparing and verifying to find a suitable quantification way for this Pelvic Floor Muscle Training System, which are more accurate and can also represent more actual force characteristic than the original functions.

並列關鍵字

Pelvic Floor Muscle Training ； Quantification Analysis ； Ramer Douglas Peucker Algorithm

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