Performance Index Modeling for Golf Greenside Bunker Shots: IMU Sensor based Approach Josefa Wivou, Pubudu N. Pathirana and Van T. Huynh School of Engineering, Deakin University Melbourne, Australia jwivou @ deakin.edu.au pubudu.pathirana @ deakin.edu.au Lanka Udawatta Higher Colleges of Technology Department of Mechatronics Engineering RAK Campus, UAE lanka @ ieee.org Abstract— There has been substantial scientific experimental research undertaken with the aim of determining factors that contribute to club-head speed while few on effects of club-head speed on drives, irons shot and putts in the game of golf. However, analysis of club-head velocity for an effective greenside bunker shot has not, and identification of associated key features for evaluation to obtain performance index relative to professional golfers (PG). Data from twenty-two (22) golfing subjects were analyzed through mathematical model that was applied at club- head peak velocity. For an effective greenside bunker shot, results indicated that PG delivered very high club-head speed at impact, low handicap golfers (LHG) delivered high club-head speed while novice golfers (NG) had low club-head speed. Result also showed that optimized performance index (OPI) value increased as the golfer’s handicap index increased dramatically relative to PG golfers. The result of this study may serve as a novelty to the scientific analysis of an effective greenside bunker shot peak velocity and identification of golfer’s performance index for skills enhancement through proper training guidelines. Keywords—performance evaluation; handicap; greenside bunkers; peak velocity data I. INTRODUCTION Game of golf is played globally by many people of all ages and skill level, to enjoy the game, a study [1] recommended that players undertake golf-specific exercises programs for performance. Although exercise programs aimed at improving physical measurement and other related variables to optimize golfer’s performance it has not integrated well, hence the study suggested that exercises must be developed that will benefit all the golfers and reduce early injury. Another study [2] revealed that golf players are recommended to seek for assistance from accredited coaches for strength and conditioning in a proper gym training program promoting health and wellbeing which has a high probability of performance impact on longer drives and increase resilience to golf related injuries. Another study [3] revealed that positive relationship between handicap and swing performance exists, a positive correlation between skill and muscle strength and finally there is a relationship between driving distance, swing speed, ball speed and muscle strength. The study suggested that training leg-hip, trunk power and grip strength are significant for golf performance improvement. Yet, another study [4] investigated the effect of dynamic and static stretching warm-up routines on golf driving performance. The result showed that there was significant difference between dynamic and static stretching protocols in terms of driving distance and accuracy. The study concluded that performing dynamic warm-up increased driving distance and accuracy than static warm-up. Fig. 1. (a) Ball adress in Bunker shot, (b) Ball to rest within closer to the pin, (c) Club head entry into the sand behind the ball Swing kinematic and ground reaction force analysis to identify key variables to driving ball velocity study [5] showed that upper torso-pelvis rotation (X-Factor), delayed release of the double pendulum (arms and wrists), trunk shift and lateral tilting, and shift of weight while swinging were dramatically related to ball velocity. After reviewing available literature it is evident, a gap of scientific knowledge exists in the analysis and identification of key variables in the club-head speed, its relationship to handicap and performance to an effective greenside bunker shot (see Fig. 1 for typical setup). The rest of the research paper is organized as follows: In Section II, methodology including data capturing, velocity data analysis and modeling of the performance index is presented. Different data sets from the experiment and nature of data are explained in Section III. Performance model and correlation results obtained through the investigation are given in Section IV. Finally, conclusions are highlighted in Section V.