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Sensor Location Matters When Estimating Player Workload for Baseball Pitching

delete2022-11-21
delete3
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OA
AI
C
Cristine Agresta *
M
Michael T. Freehill
J
Jessica Zendler
G
Georgia Giblin
S
Stephen M. Cain
DOI:10.3390/s22229008delete
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摘要

摘要

En 中文
Estimating external workload in baseball pitchers is important for training and rehabilitation. Since current methods of estimating workload through pitch counts and rest days have only been marginally successful, clubs are looking for more sophisticated methods to quantify the mechanical loads experienced by pitchers. Among these are the use of wearable systems. While wearables offer a promising solution, there remains a lack of standards or guidelines for how best to employ these devices. As a result, sensor location and workload calculation methods vary from system to system. This can influence workload estimates and blur their interpretation and utility when making decisions about training or returning to sport. The primary purpose of this study was to determine the extent to which sensor location influences workload estimate. A secondary purpose was to compare estimates using different workload calculations. Acceleration data from three sensor locations-trunk, throwing upper arm, and throwing forearm-were collected from ten collegiate pitchers as they threw a series of pitches during a single bullpen session. The effect of sensor location and pitch type was assessed in relation to four different workload estimates. Sensor location significantly influenced workload estimates. Workload estimates calculated from the forearm sensor were significantly different across pitch types. Whole-body workload measured from a trunk-mounted sensor may not adequately reflect the mechanical loads experienced at throwing arm segments. A sensor on the forearm was the most sensitive to differences in workloads across pitch types, regardless of the calculation method.
Keyword:
load
stress
torque
wearables
arm
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期刊

Sensors 封面图
Sensors
IF:
3.5
论文数:
7.2W
被引数:
20.9W

机构

U
University of Washington
学者数:
8.0W
论文数: 7.0W
被引数: 12.5W
S
Stanford University
学者数:
9.6W
论文数: 8.2W
被引数: 17.0W
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