School of Sport, Exercise and Rehabilitation, Faculty of Health, University of Technology Sydney, NSW, Australia.
Tennis Australia, Melbourne, Australia ; and.
J Strength Cond Res. 2023 Jun 1;37(6):1271-1276. doi: 10.1519/JSC.0000000000004283. Epub 2023 Apr 6.
Perri, T, Reid, M, Murphy, A, Howle, K, and Duffield, R. Determining stroke and movement profiles in competitive tennis match-play from wearable sensor accelerometry. J Strength Cond Res 37(6): 1271-1276, 2023-This study determined stroke and movement accelerometry metrics from a wearable sensor and compared between court surface (grass vs. hard) and match outcome (win vs. loss) during competitive tennis match-play. Eight junior high-performance tennis players wore a trunk-mounted global positioning system, with in-built accelerometer, magnetometer, and gyroscope during singles matches on hard and grass courts. The manufacturer software calculated accelerometer-derived total player load (tPL). A prototype algorithm classified forehands, backhands, serves, and "other" strokes, thereby calculating stroke PL (sPL) from individual strokes. Movement PL (mPL) was calculated as the difference between tPL and sPL, with all metrics reported as absolute and relative (min -1 , %, and ·stroke). Analysis of accelerometer load and stroke count metrics was performed through a two-way (surface [grass vs. hard] × match outcome [win vs. loss]) analysis of variance ( p < 0.05) and effect sizes (Cohen's d ). No interaction effects for surface and match outcome existed for absolute tPL, mPL, and sPL ( p > 0.05). Increased mPL% featured on grass courts, whereas sPL% was increased on hard courts ( p = 0.04, d = 1.18[0.31-2.02]). Elevated sPL·min -1 existed on hard courts ( p = 0.04, d = 1.19[0.32-2.04]), but no differences in tPL·min -1 and mPL·min -1 were evident for surface or outcome ( p > 0.05). Relative forehand sPL (FH-sPL·min -1 ) was higher on hard courts ( p = 0.03, d = 1.18[0.31-2.02]) alongside higher forehand counts ( p = 0.01, d = 1.29[0.40-2.14]). Hitting demands are heightened on hard courts from increased sPL and counts. Conversely, increased mPL% on grass courts likely reflects the specific movement demands from point-play. Physical preparation strategies during training blocks can be tailored toward movement or hitting loads to suit competitive surfaces.
佩里、里德、墨菲、豪尔和达菲。从可穿戴传感器的加速计中确定竞技网球比赛中的击球和移动模式。J 力量与调理研究 37(6):1271-1276,2023-本研究从可穿戴传感器中确定了击球和移动加速度计指标，并比较了硬地和草地场地上的比赛表面（草地与硬地）和比赛结果（赢与输）之间的差异。在硬地和草地场地上进行单打比赛时，八名初中高水平网球运动员穿着躯干安装的全球定位系统，内置加速度计、磁力计和陀螺仪。制造商软件计算了来自可穿戴传感器的总球员负荷（tPL）。一个原型算法对正手、反手、发球和“其他”击球进行分类，从而从各个击球中计算出击球 PL（sPL）。运动 PL（mPL）被计算为 tPL 和 sPL 之间的差值，所有指标均以绝对和相对（min-1、%和·stroke）形式报告。通过表面（草地与硬地）×比赛结果（赢与输）的双向方差分析（p<0.05）和效应大小（科恩 d）对加速度计负荷和击球计数指标进行了分析。对于绝对 tPL、mPL 和 sPL，表面和比赛结果之间没有交互作用（p>0.05）。在草地上，mPL%增加，而在硬地上，sPL%增加（p=0.04，d=1.18[0.31-2.02]）。在硬地上，sPL·min-1 升高（p=0.04，d=1.19[0.32-2.04]），但表面或结果均无差异 tPL·min-1 和 mPL·min-1（p>0.05）。硬地的相对正手 sPL（FH-sPL·min-1）更高（p=0.03，d=1.18[0.31-2.02]），正手击球次数也更高（p=0.01，d=1.29[0.40-2.14]）。在硬地上，由于 sPL 和计数的增加，击球需求增加。相反，草地上 mPL%的增加可能反映了从点到点比赛的特定运动需求。在训练期间，体能准备策略可以根据比赛表面的需要，针对运动或击球负荷进行调整。
