不同蹲踞式起跑動作之動力學分析

Dynamics Analysis of Different Types of Crouch Starts

目的：近年來，物聯網 (Internet of Things, IOT) 的普及與人工智慧 (Artificial Intelligen)崛起，相關科技結合運動的跨領域合作越來越盛行，讓收集資料與分析資料變得更簡單也更有效率。本研究主要目的是運用物聯網技術開發起跑架來探討不同蹲踞式起跑動作之動力學分析。方法：受試者為 8名大學田徑隊短距離男子選手 (身高：173.16 ± 4.77公分；體重：65.55 ± 4.52公斤；年齡：20.06 ± 1.09歲)，以一組物聯網系統與人工智慧演算法平臺，使用Arduino、Raspberry Pi與深度學習 (Deep Learning) 演算法開發一組適合平時訓練時所使用的田徑蹲踞式起跑訓練數據收集與分析系統 (專利編號：I632939)。在起跑架前、後踏板各安裝 20片壓力感測器 (取樣頻率1000 Hz) 。安裝前，每片壓力感測器都必須經校正程序，並利用多元線性回歸方程式，計算出其電壓-力量曲線斜率公式後再導入系統中。實驗數據以重複量數單因子變異數分析，比較起跑模式間的差異，若達到顯著差異，以 LSD法進行事後比較，顯著水準為 α = .05。結果：在起跑出發階段，長式起跑後腳在最大反作用發生的時間與作用力均呈現比其它模式較佳的趨勢且踏板產生的初始發力率、最大發力率也較大，同時也發現受試者慣式起跑模式起跑線到前腳踏板間距過長的現象。結論：中式與長式起跑對整體起跑階段動力學參數有較佳的表現。建議選手未來可以在起跑出發階段方面，改變起跑架前踏板與起跑線的距離為 1.5個腳掌長，以利整體起跑出發表現。

 

Purpose: In recent years, with the popularity of the Internet of Things (IoT) and the rise of artificial intelligence (AI), the cross-disciplinary cooperation of related technologies combined with sports has become more and more popular, making it easier and more efficient to collect and analyze data. The purpose of this study is to do dynamics analysis of different types of crouch start by using starting blocks developed by IoT technology. Methods: The subjects were 8 short-distance male athletes from college track and field team (height: 173.16 ± 4.77 cm; weight: 65.55 ± 4.52 kg; age: 20.06 ± 1.09 years old). A set of data collecting and analyzing system developed by IoT systems, AI algorithm platform, Arduino, Raspberry Pi, and Deep Learning was used to collect data (patent number: I632939). Each of front and rear block was installed 20 pressure sensors (sampling frequency 1000Hz), and before installation, each sensor was calibrated, and multiple linear regression was used to calculate the voltage-force curve slope formula and then import it into the system. The experimental data were analyzed by repeated-measures one-way ANOVA to compare the differences between the different crouch starts. If a significant difference was reached, the LSD method was used for post-hoc comparison, and the significant level was α = .05. Results: In the starting phase, the rear foot of elongated staring mode was better than other modes in the time and force of maximal reaction force, and initial force and maximal rate of force development generated by the blocks were also larger. Also, it was also found that the distance from the starting line to front block was too long in subjects’ habitual starting mode. Conclusions: The performance of dynamics analysis was better in the elongated and middle starting mode in the starting phase. It was suggested that athletes could adjust the front block 1.5-feet long from the staring line to improve starting performance.