利用穿戴式肌電感測器實現肌肉疲勞評估-以舉啞鈴為例

Muscle Fatigue Estimation with Wearable EMG Sensors: A Case Study of Lifting Dumbbell

林冠錞(Kuan-Chun Lin)

指導教授 ： 李明穗

國立臺灣大學/電機資訊學院/資訊工程學研究所/碩士(2015年)

https://doi.org/10.6342/NTU.2015.00344

摘要

由於近年智慧型手機以及晶片越來越小且方便，穿戴式裝置也隨之流行了起來，大家也開始利用穿戴式裝置來記錄自己的身體狀況，這一、兩年如心跳感測器、計步器等，都在智慧型手錶中看見其蹤跡。而肌電感測器在未來也極有可能成為穿戴式設備的重要一員，目前已利用肌電感測器做為輸入設備的產品問世。此外也越來越多人參與健身這項活動，但是高強度的運動也伴隨著高受傷風險，因此我們結合肌電感測器與記錄自身狀態的風潮，發展出利用Arduino肌電感測器來回饋健身時肌肉疲勞狀態之系統，讓使用者能根據回饋判斷是否停止活動以降低受傷發生機率。本篇論文提出一個評估使用者肌肉疲勞程度之系統，首先利用原始訊號的波形分割成「動作」，並經過傅立葉轉換轉為頻譜，濾掉不需要的頻段後在每下動作內計算能量總量並正規化數值後套入假設模型，進而估計出使用者可能的疲勞程度。在實驗部分，我們設計了一套舉啞鈴的試驗來供受測者作為疲勞性運動。最後我們的方法可以將運動清楚地分割出每個「動作」，並且也提出了一個計算肌肉疲勞的新觀點，在實驗結果也呈現出評估疲勞結果與現實情況之誤差皆在10%以內。

關鍵字

肌肉疲勞 ； 肌電圖 ； 疲勞評估

並列摘要

Due to the smartphones and SoC are more and more convenient and small, wearable devices become common and popular recently. People start to use wearable devices to record their body status. Heart rate sensors, pedometers are seen in smartwatches in large quantities these two years. Also, in the future, EMG sensor will very likely play an important role in wearable devices. A new type of input device which utilize EMG sensor came out several months ago. Furthermore, fitness is a very popular high intensity activity. However, high intensity activity comes with high risk injury. As a result, utilizing EMG sensor and the trend to recording users’ own information, we developed a Arduino system which combine EMG sensor and feedback of muscle fatigue level when fitness. In this thesis, we proposed a system for estimating users’ level of muscle fatigue. At the beginning, we separate whole fitness activity into “action” from raw signal. After then, the signal is performed by Fourier transform and unwanted frequencies are eliminated. Finally, we normalize the total energy, which is generated by motor unit, and apply to our model to estimate the level of users’ muscle fatigue. In experiment, a lifting dumbbell trial was designed as the exhausting exercises. In summary, we proposed a method to separate each action during lifting dumbbell clearly and a new aspect of muscle fatigue evaluation; and results show that our system is able to estimate the level of muscle fatigue with 10% errors between evaluation and reality.

並列關鍵字

Muscle Fatigue ； EMG ； Fatigue Level

參考文獻

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