加速規是否能判斷籃球之運動強度？

Can accelerometer determine the exercise intensity in basketball drills?

10.6222/pej.201909_52(3).0004

蔡琪揚(Chi-Yang Tsai)；李逸驊(Yi-Hua Lee)；相子元(Tzyy-Yuang Shiang)

加速度 ； 穿戴裝置 ； 心率 ； 運動量 ； acceleration ； wearable devices ； heart rate ； exercise volume

體育學報

52卷3期（2019 / 09 / 01）

319 - 328

繁體中文

緒論：隨著科技進步，穿戴裝置以輕便即時的姿態出現在市場上，深受專業運動員及一般大眾喜愛，而其中以加速規最為實用。目前已有文獻證明，在不同強度活動下隨著活動強度增強，攝氧量、心率及加速度值皆會隨之增加，顯示三項數據指數皆可用作評估運動強度。本文利用內含三軸加速規的智慧手環及心率錶為實驗工具，觀察選手執行三項不同強度籃球專項動作時各部位加速度及心率。藉以評估加速規是否能區別不同籃球專項動作之強度，並藉由加速度和心率的相關性，期盼能確實分辨加速規放置於身體的何種部位，才能準確評估運動量。方法：共12位受試者參與本實驗，每位受試者執行三項籃球專項動作，分別為跑動運球、行進推傳及原地跳躍，每動作執行三分鐘。過程中收取受試者四肢及軀幹加速度訊號及心率數據。加速規訊號以一分鐘為單位，擷取每單位合加速度前10大峰值進行分析；心率數據則將每分鐘平均值帶入TRIMP公式計算。以皮爾森積差相關分析比較各部位加速度與心率間的相關性。結果：慣用手、非慣用手及軀幹加速度在三項動作強度趨勢上與心率相同，能區分設計動作之強度，統計結果顯示慣用手及軀幹加速度與心率有顯著相關（r = .456、r = .332），且慣用手相關性最高。結論：加速規數據能呼應心率用於分辨運動強度，其不會延滯及即時性的特性適合用於監控籃球運動，且慣用手加速度較其他部位加速度與心率間有著最高的相關性，表示在進行籃球項目運動量監控時，將加速規擺放至慣用手最能反映實際情況。

Introduction: With rapid advances in technology, the improvements have been made to make wearable device a lightweight and prompt tool for elite athletes and general population in the market; in particular, accelerometer has been practical among these wearable devices. Recently, several previous studies proven that by the increase of exercise intensity in different intensive exercise level, the rate of oxygen uptake, heart rate, and accelerometer would be increased accordingly, which determined that these three statistics could be used in exercise volume estimates. This study tested for the relationship between acceleration and heart rate, using smart wristband and Polar heart rate monitor to collect accelerometer signals from limbs and heart rate signals with three different basketball movement intensities, to determine acceleration data from which limbs is closer to exercise volume. Methods: Before the participants performed three basketball movement tests of ＂Dribbling Moves＂, ＂Passing on the Move＂, ＂Vertical Jump＂, (each test for 3 min), we placed the smart wristband at multiple positions of participants' body and to evaluate their heart rate and performance. The training load was calculated using Banister's Training Impulse (TRIMP). The acceleration data were collected through ten strongest peaks every minute of resultant acceleration. Pearson product-moment correlation was used to analyze the data. Results: Acceleration data from dominant hand, non-dominant hand, and trunk were in line with heart rate, it revealed that acceleration can distinguish exercise intensity. Furthermore, dominant hand acceleration was positively correlated with heart rate. Conclusion: Acceleration data are suitable for basketball movement monitoring, especially when it's applied in recognizing exercise intensity along with heart rate. Moreover, the acceleration data collected from dominate hand is highly related to heart rate. Thus, the data collected from smart wristband at dominate hand may be closer to real exercise intensity.

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