TW202025107A - Sports teaching assisted system based on wearable device - Google Patents
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本揭露實施例是有關於一種運動教學輔助系統,且特別是有關於一種基於穿戴式裝置的運動教學輔助系統。 The disclosed embodiment relates to an exercise teaching assist system, and particularly relates to a wearable device-based exercise teaching assist system.
在體育運動的訓練過程中,通常都是透過教練採用一對一或者一對多進行教導的模式,由於是透過人工進行教學,必須要有教練時時在旁觀看,以判斷運動員的姿勢動作是否符合要求,費時費力。再者,運動員在教學之後進行個人的練習時,也無法自行判斷其姿勢動作是否符合要求,使得個人練習的效率大打折扣。因此,實有必要開發一種運動教學輔助系統來解決上述問題。 In the process of sports training, the coach usually uses one-to-one or one-to-many teaching mode. Because the teaching is conducted manually, a coach must be watched from the side to determine whether the athlete’s posture or action Meet the requirements, time-consuming and laborious. Furthermore, when athletes perform personal exercises after teaching, they cannot judge whether their postures and movements meet the requirements, which greatly reduces the efficiency of personal exercises. Therefore, it is necessary to develop a sports teaching aid system to solve the above problems.
本揭露之目的在於提出一種基於穿戴式裝置的運動教學輔助系統,包含穿戴式裝置以及處理單元。穿戴式裝置用以供使用者穿戴於手臂上,穿戴式裝置包含三軸陀螺 儀與至少一肌電(Electromyography,EMG)感測器分別用以感測使用者作出揮臂動作時分別對應不同時間點之三軸角速度值與至少一肌電訊號。處理單元用以接收三軸角速度值與肌電訊號以計算出揮臂動作所對應之姿勢特徵值與力道特徵值,並將姿勢特徵值與力道特徵值輸入標準動作模型中,以計算出姿勢相似度百分比與力道相似度百分比。 The purpose of this disclosure is to provide a wearable device-based exercise teaching assistance system, which includes a wearable device and a processing unit. The wearable device is used for the user to wear on the arm. The wearable device includes a three-axis spinning top The meter and at least one EMG (Electromyography) sensor are used to sense the three-axis angular velocity values and at least one EMG signal corresponding to different time points when the user makes an arm swing. The processing unit is used to receive the three-axis angular velocity value and the electromyographic signal to calculate the posture feature value and the force feature value corresponding to the arm swing, and input the posture feature value and the force feature value into the standard motion model to calculate the posture similarity The percentage of degree and the percentage of similarity of force.
在一些實施例中,上述處理單元藉由黎曼積分(Riemann Integral)來將三軸角速度值轉換成姿勢特徵值並進行正規化,再將正規化後之姿勢特徵值輸入標準動作模型中,以計算出姿勢相似度百分比。 In some embodiments, the aforementioned processing unit uses Riemann Integral to convert the three-axis angular velocity values into posture feature values and normalize them, and then input the normalized posture feature values into the standard motion model to Calculate the percentage of posture similarity.
在一些實施例中,上述處理單元藉由平均絕對值(Mean Absolute Value)運算來將肌電訊號轉換成力道特徵值並進行正規化,再將正規化後之力道特徵值輸入標準動作模型中,以計算出力道相似度百分比。 In some embodiments, the above-mentioned processing unit converts the myoelectric signal into a force characteristic value by means of a mean absolute value (Mean Absolute Value) operation and normalizes the force characteristic value, and then inputs the normalized force characteristic value into the standard motion model. To calculate the force similarity percentage.
在一些實施例中,其中,在處理單元將姿勢特徵值與力道特徵值輸入標準動作模型前,處理單元依據三軸角速度值隨著不同時間點之變化情形來將揮臂動作分割為多個分解動作,處理單元用以計算出每個分解動作所對應之姿勢特徵值與力道特徵值。 In some embodiments, before the processing unit inputs the posture feature value and the force feature value into the standard motion model, the processing unit divides the arm swing action into multiple decompositions according to changes in the three-axis angular velocity value with different time points Action, the processing unit is used to calculate the posture feature value and the force feature value corresponding to each decomposition action.
在一些實施例中,上述標準動作模型之建立流程如下:將穿戴式裝置穿戴於每個教練的手臂上;穿戴式裝置感測每個教練作出多個標準揮臂動作時,每個標準揮臂動作分別對應不同時間點之三軸角速度值與至少一肌電訊號;處理單元將每個標準揮臂動作分割為多個標準分解動 作;處理單元藉由黎曼積分來將每個標準分解動作所對應之三軸角速度值轉換成姿勢特徵值並進行正規化;處理單元藉由平均絕對值運算來將每個標準分解動作所對應之肌電訊號轉換成力道特徵值並進行正規化;以及處理單元將每個標準分解動作所對應之正規化後之姿勢特徵值與力道特徵值做為倒傳遞類神經網路(Back Propagation Artificial Neural Network,BPANN)之訓練資料來訓練標準動作模型。 In some embodiments, the process of establishing the above-mentioned standard motion model is as follows: wear a wearable device on the arm of each coach; the wearable device senses that when each coach makes multiple standard arm swings, each standard swing is The actions correspond to the three-axis angular velocity values at different time points and at least one EMG signal; the processing unit divides each standard arm swing into multiple standard decomposition movements The processing unit uses Riemann integration to convert the three-axis angular velocity value corresponding to each standard decomposition action into a posture feature value and normalizes it; the processing unit uses average absolute value operation to correspond to each standard decomposition action The EMG signal is converted into force eigenvalues and normalized; and the processing unit uses the normalized posture eigenvalues and force eigenvalues corresponding to each standard decomposition action as Back Propagation Artificial Neural Network, BPANN) training data to train the standard motion model.
在一些實施例中,上述處理單元依據三軸角速度值隨著不同時間點之變化情形來將每個標準揮臂動作分割為標準分解動作。 In some embodiments, the above-mentioned processing unit divides each standard arm swing action into a standard decomposition action according to changes of the three-axis angular velocity value with different time points.
在一些實施例中,其中,在標準動作模型之建立之後,處理單元更藉由10折交叉驗證(10-fold cross-validation)來確定該標準動作模型之分類正確率。 In some embodiments, after the standard motion model is established, the processing unit further determines the classification accuracy rate of the standard motion model by 10-fold cross-validation.
在一些實施例中,其中基於穿戴式裝置的運動教學輔助系統更包含人機介面,用以透過雷達圖介面來呈現每個分解動作所對應之姿勢相似度百分比與力道相似度百分比。 In some embodiments, the wearable device-based exercise teaching assistance system further includes a man-machine interface for presenting the posture similarity percentage and the force similarity percentage corresponding to each decomposition action through the radar chart interface.
在一些實施例中,上述人機介面還包含教學建議介面,用以根據每個分解動作所對應之姿勢相似度百分比與力道相似度百分比來提供動作矯正建議給使用者。 In some embodiments, the above-mentioned human-machine interface further includes a teaching suggestion interface for providing action correction suggestions to the user according to the posture similarity percentage and the force similarity percentage corresponding to each decomposition action.
在一些實施例中,上述人機介面還包含教練影片選單與使用者影片選單,使用者透過點選教練影片選單與使用者影片選單來撥放影片以進行動作比對。 In some embodiments, the above-mentioned human-machine interface further includes a coach video menu and a user video menu. The user clicks the coach video menu and the user video menu to play videos for action comparison.
為讓本揭露的上述特徵和優點能更明顯易懂,下文特舉實施例,並配合所附圖式作詳細說明如下。 In order to make the above-mentioned features and advantages of the present disclosure more obvious and understandable, the following specific embodiments are described in detail in conjunction with the accompanying drawings.
100‧‧‧基於穿戴式裝置的運動教學輔助系統 100‧‧‧A sports teaching aid system based on wearable devices
120‧‧‧穿戴式裝置 120‧‧‧Wearable device
122‧‧‧三軸陀螺儀 122‧‧‧Three-axis gyroscope
124‧‧‧肌電感測器 124‧‧‧Myoelectricity sensor
140‧‧‧處理單元 140‧‧‧Processing unit
160‧‧‧人機介面 160‧‧‧Human Machine Interface
162‧‧‧姿勢相似度雷達圖 162‧‧‧Posture similarity radar chart
164‧‧‧力道相似度雷達圖 164‧‧‧Strength similarity radar chart
166‧‧‧教學建議介面 166‧‧‧teaching suggestion interface
168‧‧‧影片選單 168‧‧‧Video Menu
P1~P9‧‧‧點 P1~P9‧‧‧point
從以下結合所附圖式所做的詳細描述,可對本揭露之態樣有更佳的了解。需注意的是,根據業界的標準實務,各特徵並未依比例繪示。事實上,為了使討論更為清楚,各特徵的尺寸都可任意地增加或減少。 From the following detailed description in conjunction with the accompanying drawings, a better understanding of the aspect of the disclosure can be obtained. It should be noted that, according to industry standard practice, each feature is not drawn to scale. In fact, in order to make the discussion clearer, the size of each feature can be arbitrarily increased or decreased.
[圖1]係根據本揭露的實施例之基於穿戴式裝置的運動教學輔助系統的示意圖。 [Fig. 1] is a schematic diagram of an exercise teaching assist system based on a wearable device according to an embodiment of the disclosure.
[圖2]係根據本揭露的實施例之穿戴式裝置的示意圖。 [Figure 2] is a schematic diagram of a wearable device according to an embodiment of the disclosure.
[圖3]係根據本揭露的實施例之揮臂動作分割為四個分解動作的示意圖。 [Fig. 3] is a schematic diagram of the arm swing action divided into four decomposition actions according to the embodiment of the disclosure.
[圖4]係根據本揭露的實施例之人機介面的呈現畫面示意圖。 [Fig. 4] is a schematic diagram of the display screen of the human-machine interface according to the embodiment of the disclosure.
以下仔細討論本發明的實施例。然而,可以理解的是,實施例提供許多可應用的概念,其可實施於各式各樣的特定內容中。所討論、揭示之實施例僅供說明,並非用以限定本發明之範圍。 The embodiments of the present invention are discussed in detail below. However, it can be understood that the embodiments provide many applicable concepts, which can be implemented in various specific contents. The discussed and disclosed embodiments are for illustration only, and are not intended to limit the scope of the present invention.
圖1係根據本揭露的實施例之基於穿戴式裝置的運動教學輔助系統100的示意圖,基於穿戴式裝置的運動教學輔助系統100包含穿戴式裝置120與處理單元140,穿
戴式裝置120用以供使用者穿戴於手臂上,從而偵測使用者的揮臂動作。
1 is a schematic diagram of a wearable device-based exercise
圖2係根據本揭露的實施例之穿戴式裝置120的示意圖,穿戴式裝置120包含三軸陀螺儀(Gyroscopes)122與肌電(Electromyography,EMG)感測器124。三軸陀螺儀122用以感測使用者作出揮臂動作時的動作角度,意即,三軸陀螺儀122用以感測使用者作出揮臂動作時對應不同時間點之三軸角速度值。其中,三軸角速度值包含X軸角速度值、Y軸角速度值與Z軸角速度值。肌電感測器124用以感測使用者作出揮臂動作時的肌力,意即,肌電感測器124用以感測使用者作出揮臂動作時對應不同時間點之肌電訊號。在本揭露的實施例中,穿戴式裝置120包含八個肌電感測器124包覆使用者的手臂的八個不同部位,用以感測使用者作出揮臂動作時對應不同時間點之八個肌電訊號,從而更完整地感測使用者作出揮臂動作時的肌力,然而,本揭露不限於此,設計者可依實際需求選擇穿戴式裝置120所包含之肌電感測器124的數量。
FIG. 2 is a schematic diagram of a
請回到圖1,處理單元140透過無線傳輸的方式,例如藍芽或無線網路等,來從穿戴式裝置120接收使用者作出揮臂動作時對應不同時間點之三軸角速度值與肌電訊號。由於一個揮臂動作當中可能包含不同的揮臂方式,舉例而言,羽球的正拍殺球動作依序包含四個子動作:出手前放鬆、殺球前預備動作、食指帶動手腕發力下扣、出手後放鬆,若能把一個揮臂動作再分解為多個分解動作將有助於更
有效地進行運動教學輔助。在本揭露的實施例中,處理單元140依據三軸角速度值隨著不同時間點之變化情形來將一個揮臂動作分割為多個分解動作,一旦分割完成後,即可取得每個分解動作所分別對應之三軸角速度值與肌電訊號。以下透過圖3來舉例說明,本揭露如何依據三軸角速度值隨著不同時間點之變化情形來將一個揮臂動作分割為多個分解動作。
Please return to FIG. 1, the
圖3係根據本揭露的實施例之揮臂動作分割為四個分解動作的示意圖。圖3所示者,是揮臂動作為羽球的正拍殺球時,三軸角速度值當中的Y軸角速度值隨著時間的變化示意圖,其中分解動作1為出手前放鬆,分解動作2為殺球前預備動作,分解動作3為食指帶動手腕發力下扣,分解動作4為出手後放鬆。當進行分解動作3:食指帶動手腕發力下扣時,使用者的身體快速轉回正面對網同時手臂必須快速由上往下扣球,因此Y軸角速度值會急遽減少至負值,而最小負值會在圖3中呈現明顯的波谷狀,即分解動作3當中的點P6。接著找出分解動作3的起始點與結束點,關於分解動作3的起始點,首先由點P6往前找到“前最大值”(即點P5),再接著往前找到Y軸角速度值趨近於零的點P4,Y軸角速度值趨近於零代表角度沒有發明變化,因此可判斷點P4為分解動作3的起始點;關於分解動作3的結束點,首先由點P6往後找到“後最大值”(即點P7),再接著往後找到Y軸角速度值變化趨於平坦並接近於零的點P8,Y軸角速度值變化趨於平坦並接近於零代表角度沒有發明變化,因此可判 斷點P8為分解動作3的結束點。當進行分解動作1:出手前放鬆時,手臂必須平舉於腰間,且保持靜止狀態,因此Y軸角速度值會持續保持在相當低的數值;當進行分解動作2:殺球前預備動作時,使用者須側身對網,同時手臂向上舉起與地面幾近垂直以準備擊球。根據上述,若欲找出分解動作2的起始點與結束點,分解動作2的結束點即為分解動作3的起始點(即點P4),而關於分解動作2的起始點,首先由點P4往前找到“前最大值”(即點P3),再接著往前找到Y軸角速度值變化趨於平坦的點P2,Y軸角速度值變化趨於平坦代表手臂保持靜止,因此可判斷點P2為分解動作2的起始點。若欲找出分解動作1的起始點與結束點,分解動作1的結束點即為分解動作2的起始點(即點P2),分解動作1的起始點即為圖3的首點(即點P1)。若欲找出分解動作4的起始點與結束點,分解動作4的起始點即為分解動作3的結束點(即點P8),分解動作4的結束點即為圖3的末點(即點P9)。根據上述判斷流程,確立了分解動作1的時間區段落在點P1至點P2之間,分解動作2的時間區段落在點P2至點P4之間,分解動作3的時間區段落在點P4至點P8之間,分解動作4的時間區段落在點P8至點P9之間。 FIG. 3 is a schematic diagram of the arm swing action divided into four decomposition actions according to the embodiment of the disclosure. Figure 3 shows a schematic diagram of the Y-axis angular velocity change over time when the arm swing is a forward shot of a shuttlecock. Among the three-axis angular velocity values, the decomposition action 1 is to relax before the shot, and the decomposition action 2 is to kill. Preparatory action before the ball, decomposition action 3 is the index finger drives the wrist to buckle down, and decomposition action 4 is relaxation after the shot. When performing decomposition action 3: When the index finger drives the wrist to buckle down, the user's body quickly turns back to face the net and the arm must smash from top to bottom quickly. Therefore, the Y-axis angular velocity value will suddenly decrease to a negative value, and the minimum Negative values will appear obvious troughs in Figure 3, that is, point P6 in the decomposition action 3. Then find out the starting point and ending point of decomposition action 3. Regarding the starting point of decomposition action 3, first find the "previous maximum value" (ie point P5) from point P6, and then go forward to find the Y axis angular velocity value Point P4 approaching zero, the Y-axis angular velocity value approaching zero means that there is no invention change in the angle. Therefore, it can be judged that point P4 is the starting point of decomposition action 3; regarding the end point of decomposition action 3, first from point P6 to the back Find the "post-maximum value" (ie point P7), and then find the point P8 where the Y-axis angular velocity value changes flat and close to zero. The Y-axis angular velocity value changes flat and close to zero, which means that the angle has not changed. , So it can be judged Breakpoint P8 is the end point of decomposition action 3. When performing decomposition action 1: When relaxing before the shot, the arm must be raised flat on the waist and kept still, so the Y-axis angular velocity value will continue to be kept at a relatively low value; when performing decomposition action 2: preparatory action before the shot , The user must face the net sideways, while raising his arms almost perpendicular to the ground to prepare to hit the ball. According to the above, if you want to find the starting point and ending point of decomposition action 2, the end point of decomposition action 2 is the starting point of decomposition action 3 (ie point P4), and for the starting point of decomposition action 2, first Find the "previous maximum value" from point P4 (ie point P3), and then go forward to find the point P2 where the Y-axis angular velocity value changes flat. The Y-axis angular velocity value changes flat, which means that the arm remains stationary, so it can be judged Point P2 is the starting point of decomposition action 2. If you want to find the starting point and ending point of decomposition action 1, the end point of decomposition action 1 is the starting point of decomposition action 2 (ie point P2), and the starting point of decomposition action 1 is the first point in Figure 3. (Ie point P1). If you want to find the start point and end point of decomposition action 4, the start point of decomposition action 4 is the end point of decomposition action 3 (ie point P8), and the end point of decomposition action 4 is the end point ( Click P9). According to the above judgment process, it is established that the time zone paragraph of decomposition action 1 is between points P1 and P2, the time zone paragraph of decomposition action 2 is between points P2 and P4, and the time zone paragraph of decomposition action 3 is between points P4 and P4. Between point P8, the time zone segment of decomposition action 4 is between point P8 and point P9.
請回到圖1,處理單元140藉由黎曼積分(Riemann Integral)來將使用者作出揮臂動作時對應不同時間點之多個三軸角速度值轉換成姿勢特徵值。在本揭露的實施例中,若處理單元140先將一個揮臂動作分割為多個分解動作,則處理單元140藉由黎曼積分來將每個分解動作對
應不同時間點之多個三軸角速度值轉換成姿勢特徵值,意即,四個分解動作會分別對應到四個姿勢特徵值。另一方面,處理單元140藉由平均絕對值(Mean Absolute Value)運算來將使用者作出揮臂動作時對應不同時間點之多個肌電訊號轉換成力道特徵值。在本揭露的實施例中,若處理單元140先將一個揮臂動作分割為多個分解動作,則處理單元140藉由平均絕對值運算來將每個分解動作對應不同時間點之多個肌電訊號轉換成力道特徵值,意即,四個分解動作會分別對應到四個力道特徵值。在本揭露的實施例中,若穿戴式裝置120包含多個肌電感測器124,則處理單元140藉由平均絕對值運算來將每個肌電感測器124對應不同時間點之多個肌電訊號轉換成力道特徵值,意即,八個肌電感測器124會分別對應到八個力道特徵值。
Please return to FIG. 1, the
處理單元140接著將姿勢特徵值進行正規化,使正規化後之姿勢特徵值範圍落在0~1之間,處理單元140也將力道特徵值進行正規化,使正規化後之力道特徵值範圍落在0~1之間,處理單元140最後將正規化後之姿勢特徵值與正規化後之力道特徵值輸入標準動作模型中,以計算出姿勢相似度百分比與力道相似度百分比。姿勢相似度百分比或力道相似度百分比越高代表使用者的揮臂動作與標準動作模型中的標準揮臂動作越相似。在本揭露的實施例中,若處理單元140先將一個揮臂動作分割為多個分解動作,則處理單元140會對應計算出多個姿勢相似度百分比與多個力道相似度百分比,意即,四個分解動作會分別對應到四個姿勢
相似度百分比與四個力道相似度百分比。
The
請回到圖1,基於穿戴式裝置的運動教學輔助系統100更包含人機介面160,用以透過雷達圖介面來呈現揮臂動作或每個分解動作所對應之姿勢相似度百分比與力道相似度百分比。圖4係根據本揭露的實施例之人機介面160的呈現畫面示意圖。如圖4所示,人機介面160包含姿勢相似度雷達圖162與力道相似度雷達圖164,用以透過雷達圖介面來呈現每個分解動作所對應之姿勢相似度百分比與力道相似度百分比。如圖4所示,人機介面160還包含教學建議介面166,用以根據每個分解動作所對應之姿勢相似度百分比與力道相似度百分比來提供動作矯正建議給使用者,讓使用者能具體地知曉要如何進行動作矯正。此外,教學建議介面166還包含教練指導語輸入格,以利於教練根據每個分解動作所對應之姿勢相似度百分比與力道相似度百分比來寫入相應的指導語,讓使用者能知曉動作矯正須注意的細節。如圖4所示,人機介面160還包含影片選單168,用以提供教練影片選單與使用者影片選單,使得使用者可透過點選教練影片選單和/或使用者影片選單來撥放教練影片和/或使用者影片以進行動作比對,讓使用者能透過影像重現來知曉自身的揮臂動作與教練的標準揮臂動作之間的差異。
Please return to Figure 1, the wearable device-based exercise
上述的說明內容中提到,處理單元140會將正規化後之姿勢特徵值與正規化後之力道特徵值輸入標準動作模型中,以計算出姿勢相似度百分比與力道相似度百分比,以下將針對標準動作模型之建立流程作說明。在本揭露
的實施例中,標準動作模型之建立乃是利用倒傳遞類神經網路(Back Propagation Artificial Neural Network,BPANN)技術,將多位教練作出之標準揮臂動作作為倒傳遞類神經網路的訓練資料,經由訓練從而建立出標準動作模型,流程如下。將穿戴式裝置120穿戴於多個教練之每一者的手臂上;穿戴式裝置120感測每個教練作出多個標準揮臂動作時,每個標準揮臂動作分別對應不同時間點之三軸角速度值與肌電訊號;處理單元140依據三軸角速度值隨著不同時間點之變化情形來將每個標準揮臂動作分割為多個標準分解動作;處理單元140藉由黎曼積分來將每個標準分解動作所對應之三軸角速度值轉換成姿勢特徵值並進行正規化;處理單元140藉由平均絕對值運算來將每個標準分解動作所對應之肌電訊號轉換成力道特徵值並進行正規化;處理單元140將每個標準分解動作所對應之正規化後之姿勢特徵值與正規化後之力道特徵值做為倒傳遞類神經網路之訓練資料,從而經由訓練來建立出標準動作模型。
It is mentioned in the above description that the
在上述流程中所述之將每個標準揮臂動作分割為多個標準分解動作基本上與前述之將每個揮臂動作分割為多個分解動作的方式類似,於此不再贅述。在上述流程中所述之藉由黎曼積分來將每個標準分解動作所對應之三軸角速度值轉換成姿勢特徵值並進行正規化基本上與前述之藉由黎曼積分來將每個分解動作所對應之三軸角速度值轉換成姿勢特徵值並進行正規化的方式類似,於此不再贅述。在上述流程中所述之藉由平均絕對值運算來將每個標準分 解動作所對應之肌電訊號轉換成力道特徵值並進行正規化基本上與前述之藉由平均絕對值運算來將每個分解動作所對應之肌電訊號轉換成力道特徵值並進行正規化的方式類似,於此不再贅述。 The division of each standard arm swing into multiple standard decomposition actions described in the above process is basically similar to the aforementioned method of dividing each arm swing into multiple decomposition actions, and will not be repeated here. In the above process, the Riemann integration is used to convert the three-axis angular velocity value corresponding to each standard decomposition action into the posture eigenvalue and the normalization is basically the same as the aforementioned Riemann integration for each decomposition. The three-axis angular velocity value corresponding to the action is converted into the posture feature value and normalized in a similar manner, which will not be repeated here. In the above process, the average absolute value operation is used to divide each standard The EMG signal corresponding to the solution action is converted into a force characteristic value and normalized. It is basically the same as the above-mentioned average absolute value operation to convert the EMG signal corresponding to each decomposition action into a force characteristic value and normalized. The method is similar and will not be repeated here.
在本揭露的實施例中,教練數量為4名,各進行15次的標準揮臂動作,意即,共採用了60個標準揮臂動作作為倒傳遞類神經網路的訓練資料,但本揭露不限於此。另外,將姿勢特徵值與力道特徵值皆進行正規化後再輸入倒傳遞類神經網路的用意在於,加快倒傳遞類神經網路的辨識速度。在本揭露的實施例中,在標準動作模型之建立之後,處理單元140藉由10折交叉驗證(10-fold cross-validation)來確定標準動作模型之分類正確率,從而驗證標準動作模型是準確的。
In the embodiment of this disclosure, the number of coaches is 4, and each performs 15 standard arm swings, which means that a total of 60 standard arm swings are used as the training data of the backward transfer neural network, but this disclosure Not limited to this. In addition, the purpose of normalizing the posture eigenvalues and the force eigenvalues and then inputting the backward transfer neural network is to speed up the recognition speed of the backward transfer neural network. In the embodiment of the present disclosure, after the standard motion model is established, the
此外,在上述之利用圖3來說明本揭露如何將揮臂動作分割為四個分解動作的說明當中,或有可能會發生在使用者在不熟悉標準揮臂動作的情況下,無法作出與教練相似的揮臂動作,如此一來,可能無法依據其揮臂動作所對應之三軸角速度值當中的Y軸角速度值隨著不同時間點之變化情形來正確地辨識出判斷點P4和/或判斷點P8,因此當分解動作3的起始點和/或結束點無法利用Y軸角速度值隨著不同時間點之變化情形來判斷時,處理器140將使用多個標準揮臂動作所對應之分解動作3之點P4和/或點P8相對於時間軸的平均值來做為分解動作3的起始點和/或結束點,再接著即可依據前述的方式來分別找出分解動作1、分解動作2
與分解動作4的起始點與結束點。
In addition, in the above description of using Figure 3 to explain how the present disclosure divides the arm swing into four decomposition actions, it may happen that the user is not familiar with the standard arm swing and cannot make contact with the coach. Similar arm swing action, as a result, it may not be able to correctly identify the judgment point P4 and/or judgment based on the change of the Y axis angular velocity value among the three-axis angular velocity values corresponding to the arm swing action with different time points Point P8. Therefore, when the start point and/or end point of the decomposition action 3 cannot be determined by the change of the Y-axis angular velocity value with different time points, the
綜合上述,本揭露提出一種基於穿戴式裝置的運動教學輔助系統,包含穿戴式裝置以及處理單元。藉由穿戴式裝置所包含之三軸陀螺儀與肌電感測器來分別感測穿戴著穿戴式裝置之使用者作出揮臂動作時分別對應不同時間點之三軸角速度值與肌電訊號。處理單元接收三軸角速度值與肌電訊號以計算出揮臂動作所對應之姿勢特徵值與力道特徵值,並將姿勢特徵值與力道特徵值輸入標準動作模型中,以計算出姿勢相似度百分比與力道相似度百分比。因此,藉由本揭露所提出之基於穿戴式裝置的運動教學輔助系統,使用者進行訓練時,不須有教練在旁觀看,基於穿戴式裝置的運動教學輔助系統能夠呈現結果告知使用者其揮臂動作是否符合標準揮臂動作,從而使得個人練習的效率大幅提升。 In summary, this disclosure proposes a wearable device-based exercise teaching assistance system, which includes a wearable device and a processing unit. The three-axis gyroscope and the myoelectric sensor included in the wearable device are used to sense the three-axis angular velocity values and myoelectric signals corresponding to different time points when the user wearing the wearable device makes an arm swing. The processing unit receives the three-axis angular velocity value and the EMG signal to calculate the posture feature value and the force feature value corresponding to the arm swing, and inputs the posture feature value and the force feature value into the standard motion model to calculate the posture similarity percentage Percentage of similarity with force. Therefore, with the wearable device-based exercise teaching aid system proposed in this disclosure, the user does not need a coach to watch while training, and the wearable device-based exercise teaching aid system can present the results to inform the user of his arm swing Whether the movement conforms to the standard arm swing movement, which greatly improves the efficiency of individual practice.
以上概述了數個實施例的特徵,因此熟習此技藝者可以更了解本揭露的態樣。熟習此技藝者應了解到,其可輕易地把本揭露當作基礎來設計或修改其他的製程與結構,藉此實現和在此所介紹的這些實施例相同的目標及/或達到相同的優點。熟習此技藝者也應可明白,這些等效的建構並未脫離本揭露的精神與範圍,並且他們可以在不脫離本揭露精神與範圍的前提下做各種的改變、替換與變動。 The features of several embodiments are summarized above, so those who are familiar with the art can better understand the aspect of the disclosure. Those who are familiar with this technique should understand that they can easily use the present disclosure as a basis to design or modify other processes and structures, thereby achieving the same goals and/or the same advantages as the embodiments described herein. . Those who are familiar with this art should also understand that these equivalent constructions do not depart from the spirit and scope of this disclosure, and they can make various changes, substitutions and alterations without departing from the spirit and scope of this disclosure.
100‧‧‧基於穿戴式裝置的運動教學輔助系統 100‧‧‧A sports teaching aid system based on wearable devices
120‧‧‧穿戴式裝置 120‧‧‧Wearable device
140‧‧‧處理單元 140‧‧‧Processing unit
160‧‧‧人機介面 160‧‧‧Human Machine Interface
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