全球已漸漸步入老年化社會，老年後的生活品質也逐漸被重視。許多研究調查得知跌倒所導致的骨折是造成老人無法維持正常生活與造成死亡最大的因素(Gadelha et al., 2018)，而造成老人容易跌倒的主要原因的其中之一就是肌肉的退化，從醫學上研究我們知道全身肌肉伴隨年齡的增加會逐漸老化稱之為肌少症(Marcell, 2003)。針對肌少症造成患者行動不便或是膝蓋疼痛，復健醫師通常建議老人在醫院的復健科執行復健療程，並搭配護膝輔具給予膝關節支撐的力量。目前醫療院由於CT及MRI價格高昂的原因，並無法針對個案膝蓋關節附近肌群做量化評估，醫師通常依照觀察行走速度及問卷判斷肌少症情形，且不易依照科學數據方式得知復健效果，對於患者同時無法數據化看見復健成效。近年人工智慧與醫療輔具的崛起，醫療輔具已不再僅有單一功能，現今的科技輔具已漸漸朝向智慧化發展，故本研究以科技輔具作為數據蒐集並將其資料結合肌肉綜合分類指標的數據供患者及醫師做為診斷參考。
本研究使用臺灣新創公司(智遊科技)研發的新型智慧型復健護膝，於臺灣台南的地方社區針對50-70歲的老人，經受測者同意並實施其公司產品所引導之復健運動，提取表面肌電訊號(sEMG)及六軸感測器(G-sensor)數值將其運用在支持向量機(SVM)、決策樹(DT)、隨機森林(RF)、K鄰近法(kNN)及樸素貝葉氏(Naive Bayes)對數據做分類處理比較。依照數據中sEMG訊號的評估肌肉強度的均方根值(RMS)、評估肌肉疲勞程度MNF(mean frequency)以及六軸感測器之Y方向加速度作為機器學習訓練資料。此三種資料代表在固定的復健運動下，肌肉強度數值表現與在基礎平面上位移關係並將其肌肉的疲勞程度納入分群標準。本研究成功以上述資料訓練決策樹(DT)93.56% 、支持向量機(SVM) 81.56%、隨機森林(RF)96.37 %、K鄰近法(kNN)89.65%及樸素貝葉氏(Naive Bayes 75.52%的準確率。我們的目標是使用機器學習給予五個肌力等級，作為醫師對於肌少症診斷時一種非侵入式的評估工具。這將有助於提高對肌少症的診斷準確性和個體化治療策略的制定。

The world is gradually moving towards an aging society, and the quality of life in old age is increasingly being emphasized. Many studies have found that falls leading to fractures are the major factors causing the elderly to be unable to maintain a normal life and leading to death (Gadelha et al., 2018). One of the main reasons the elderly are prone to falls is muscle degeneration, and medically, we know that whole body muscles gradually age with the increase in age, known as sarcopenia (Marcell, 2003). To address mobility issues or knee pain caused by sarcopenia, rehabilitation physicians usually recommend elderly patients undertake rehabilitation courses in hospital rehabilitation departments, supported with knee brace aids to provide knee joint support. Current medical institutions cannot quantitatively assess the muscle groups around the knee joint for each case due to the high cost of CT and MRI. Physicians usually judge the condition of sarcopenia based on observation of walking speed and questionnaires, and it is not easy to know the rehabilitation effect scientifically, and patients cannot see the rehabilitation results in a quantified way. With the rise of artificial intelligence and medical aids in recent years, medical aids are no longer single-function. Modern technological aids are gradually developing toward intelligence. Therefore, this study uses technological aids for data collection and combines the data with comprehensive muscle classification indicators for patients and doctors as diagnostic references.
We uses a novel intelligent rehabilitation knee brace developed by a Taiwanese start-up company (Ai Free) on elderly individuals aged between 50-70 in a local community in Tainan, Taiwan. With the consent of the participants and under the guidance of the company's product-induced rehabilitation exercises, surface electromyographic signals (sEMG) and six-axis sensor (G-sensor) values were extracted and used in Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), K-Nearest Neighbors (kNN), and Naive Bayes for data classification comparison. The Root Mean Square (RMS) of the sEMG signal, assessing muscle strength, the Mean Frequency (MNF) evaluating muscle fatigue, and the Y-direction acceleration of the six-axis sensor was taken as the machine learning training data. These three types of data represent the relationship between muscle strength numerical performance and displacement on the baseline plane under fixed rehabilitation exercise, and the muscle fatigue level is included in the clustering standard. In this study, we successfully trained the Decision Tree (DT) with an accuracy of 93.56%, Support Vector Machine (SVM) 81.56%, Random Forest (RF) 96.37%, K-Nearest Neighbors (kNN) 89.65%, and Naive Bayes with an accuracy of 75.52%. Our goal is to use machine learning to provide five muscle strength levels as a non-invasive evaluation tool for doctors when diagnosing sarcopenia. This will help improve the diagnostic accuracy of sarcopenia and the formulation of personalized treatment strategies.

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