Modern motion capturing technologies are capable of collecting quantitative, biomechanical data on golf swings that can help to improve our understanding of golf theory and facilitate the establishing of new, optimized swing paradigms.This study explored the possibility of utilizing Microsoft’s Kinect sensor to analyse the biomechanics of golf swings. Following design-science research principles, it presents a software prototype capable of capturing, recording, analysing and comparing movement patterns using three-dimensional vector angles. The tracking accuracy and data validity of the software were then evaluated in a set of experiments in optimal and real-world conditions using actual golf swing recordings.The results indicate that the software is providing accurate data on joint vector angles with a clear profile view, while visually occluded and frontal angles are more difficult to determine precisely. The employed position detection algorithm demonstrated good results in both optimal and real-world environments. Overall, the presented software and its approach to position analysis and detection show great potential for use in further research efforts. / Program: Magisterutbildning i informatik

Denna studie presenterar en artefakt som med hjälp av Microsoft Kinect, samlar in och beräknar biomekanisk data från golfsvingar för att identifiera nyckelpositioner på ett automatiserat sätt. Den övergripande metoden som används är design-science research. Kinect sensorn är egentligen gjord för tv-spel, men kan även användas allmänt för att fånga och samla in kvantitativ biomekanisk data. Sensorn är inte specifikt utformad för golfsvingar, och saknar dessutom förmågan att spåra externa objekt som golfklubbor. Dessa problem var grunden för denna studies övergripande syfte, nämligen att utveckla en mjukvara med förmågan att identifiera de fem viktigaste nyckelpositionerna i golfsvingen. Nyckelpositionerna definieras utifrån mätbara egenskaper vilket för nyckelpositionen impact, krävde att man utnyttjade sensorns förmåga att spela in ljud. I empirin som genomfördes på en driving range samlades data från sammanlagt 20 svingar in. Varje identifierad nyckelposition analyserades på ett kvalitativt sätt utifrån ett antal sammanställda kriterier. Kinectsensorn hade problem att identifiera vissa positioner, men sammantaget bedömdes 87 % av de insamlade nyckelpositionerna som korrekt identifierade. För nyckelpositionen impact, där insamlad ljuddata användes för identifiering, bedömdes 85 % av de insamlade nyckelpositionerna som korrekt identifierade. Studien begränsar sig till utvalda nyckelpositioner men visar potential för automatiserad insamling av kvalitativ golfsvingdata, och ger uppenbara möjligheter för vidare forskning. / This study presents an artifact that is using Microsoft Kinect for motion capturing of golf swings, in order to identify key positions in an automatic fashion. The main method used is design science research. The Kinect sensor, which is developed for the Xbox video game console, can be used for general motion capture. It is, however, not tailored for golf swings and it also lacks the ability to track external objects such as the golf club. These problems were the main motivation for the purpose of this study, i.e. to develop an application to identify the five most important positions in the golf swing. The key positions were defined based on measurable traits, making it necessary to use the audio recording ability of the sensor for the impact position. The empirical investigation was performed at a driving range, and data from a total of 20 golf swings were gathered. In the next step, every key position was analyzed in a quantitative manner based on a number of criteria. The results show that the Kinect sensor may have some troubles recognizing certain positions, but still 87 % of the key positions captured were considered to be successfully identified. Specifically, 85 % of all impact positions were successfully identified. The study was limited to the chosen key positions, but shows good potential for automatic capturing of quantitative golf swing data, thus suggesting several possible directions for future research.