Location Matters-Can a Smart Golf Club Detect Where the Club Face Hits the Ball?

被引：2

作者：

Hollaus, Bernhard ^{[1
]}

Heyer, Yannic ^{[1
]}

Steiner, Johannes ^{[2
]}

Strutzenberger, Gerda ^{[3
,4
]}

机构：

[1] MCI, Dept Med Hlth & Sports Engn, Maximilianstr 2, A-6020 Innsbruck, Austria

[2] Johannes Steiner Golf, Robert Fuchs Str 40, A-8053 Graz, Austria

[3] UMIT TIROL Private Univ Hlth Sci & Hlth Technol, Inst Sports Med Alpine Med & Hlth Tourism ISAG, Eduard Wallnofer Zentrum 1, A-6060 Hall In Tirol, Austria

[4] MOTUM Human Performance Ctr, Steinbockallee 31, A-6063 Rum, Austria

来源：

SENSORS | 2023年 / 23卷 / 24期

关键词：

golf; swing analysis; impact analyses; convolutional neural network; machine learning; PERFORMANCE; STROKE; SPEED;

D O I：

10.3390/s23249783

中图分类号：

O65 [分析化学];

学科分类号：

070302 ; 081704 ;

摘要：

In golf, the location of the impact, where the clubhead hits the ball, is of imperative nature for a successful ballflight. Direct feedback to the athlete where he/she hits the ball could improve a practice session. Currently, this information can be measured via, e.g., dual laser technology; however, this is a stationary and external method. A mobile measurement method would give athletes the freedom to gain the information of the impact location without the limitation to be stationary. Therefore, the aim of this study was to investigate whether it is possible to detect the impact location via a motion sensor mounted on the shaft of the golf club. To answer the question, an experiment was carried out. Within the experiment data were gathered from one athlete performing 282 golf swings with an 7 iron. The impact location was recorded and labeled during each swing with a Trackman providing the classes for a neural network. Simultaneously, the motion of the golf club was gathered with an IMU from the Noraxon Ultium Motion Series. In the next step, a neural network was designed and trained to estimate the impact location class based on the motion data. Based on the motion data, a classification accuracy of 93.8% could be achieved with a ResNet architecture.

引用

页数：14