Informing golf equipment construction through golfer biomechanics

操纵运动器材来匹配个人的运动模式或人体测量学在许多运动中都很普遍。在高尔夫行业，高尔夫球杆的个性化是为专业人士和业余爱好者提供的，目的是提供一个最佳的球杆，将提高性能。虽然球杆装配在行业中很普遍，但很少有证据表明，在为个人开出最佳球杆的过程中，该过程的有效性。目前的拟合过程使用一系列静态测量来找到一个基线球杆，然后高尔夫球手使用稍微修改的球杆进行多次击球，以找到产生最佳冲击参数和球飞行的设置。这种试错过程效率低下，可能无法产生可重复的结果。因此，这个研究项目有两个主要目的。第一个目标是了解和评估高尔夫球杆定制装配过程中使用的当前流程。第二个目标是分析使用不同力学性能的高尔夫球杆对高尔夫球手生物力学和性能的影响，为开发推荐引擎提供信息，该引擎使用高尔夫球手生物力学来预测最佳球杆规格。这将减少进行试装所需的击球次数，同时增加高尔夫球手和试装员对所提出建议的信任。创建这个推荐引擎的方法将首先关注于理解过滤器目前使用的方法和工具。使用定性分析技术，对装配者进行观察和访谈，以了解当前使用的流程，他们对流程有效性的信念以及高尔夫球杆装配的未来方向。其次，通过纵向研究对高尔夫球杆配具的效果进行分析。通过在定制球杆前后对球手进行长时间的监测，分析使用一套“合适”的高尔夫球杆的即时效果，以及球手如何适应新球杆的长期影响。一旦分析了定制配件的现状，该项目将研究如何操纵球杆的机械特性将影响高尔夫球手的生物力学和表现。这将使我们了解在高尔夫球手的生物力学和表现受到影响之前，球杆的力学特性可以改变的公差。根据所展示的动力学和运动学特征，高尔夫球手将被分类为具有相似生物力学模式的组，以了解他们是否具有共同的最佳球杆性能。这将用于推荐引擎的开发，该引擎将寻求评估高尔夫球手的生物力学特征，以建议高尔夫俱乐部的力学特性，从而优化他们的比赛。这种新颖的高尔夫俱乐部定制方法旨在提高工程设计过程的效率和有效性。主要受益者将是设备制造商、球杆装配商和高尔夫球手本身，然而，所使用的方法可以很容易地适用于其他领域，在这些领域，对人与产品相互作用的分析可以为特定用户带来更优化的设计。

The manipulation of sporting equipment to match an individual's movement patterns or anthropometrics is prevalent in numerous sports. In the golf industry, personalisation of golf clubs is available to both professionals and amateurs, with the aim to provide an optimal club that will improve performance. While club fitting is prevalent in the industry, little evidence exists for the efficacy of the process in prescribing an optimal club to an individual. The current fitting process uses a series of static measurements to find a baseline club, followed by the golfer hitting multiple shots with slightly modified clubs to find a set up that produces optimal impact parameters and ball flight. This trial and error process is inefficient and may not result in a repeatable outcome. This research project, therefore, has two key aims. The first aim is to understand and assess the current processes used during golf club custom fitting. The second aim is to analyse the effect of using golf clubs with differing mechanical properties on golfer biomechanics and performance to inform the development of a recommendation engine that uses golfer biomechanics to predict optimal club specifications. This will decrease the number of shots required to perform a fitting, while increasing the trust both golfers and fitters have in the recommendations made.The approach to create this recommendation engine will firstly focus on understanding the methods and tools that fitters currently use. Using qualitative analysis techniques, fitters will be observed and interviewed to understand the current processes used, their beliefs on the effectiveness of the process and the future directions of golf club fitting. Secondly, the efficacy of golf club fitting will be analysed through a longitudinal study. By monitoring golfers for a prolonged period of time before and after a custom fitting, the immediate effects of using a 'correctly fitted' set of golf clubs will be analysed, as well as the long term effects of how a golfer adapts to the new clubs over time. Once the current state of custom fitting has been analysed, this project will investigate how manipulating mechanical characteristics of clubs will affect the biomechanics and performance of golfers. This will allow an understanding of the tolerances that mechanical properties of a club can be changed before golfer's biomechanics and performance are affected. From the kinetic and kinematic profiles exhibited, golfers will be categorised into groups with similar biomechanical patterns to understand if they share common optimal club properties. This will feed into the development of the recommendation engine which will seek to evaluate the biomechanical profile of a golfer to suggest the mechanical properties of a golf club that will optimise their game.This novel approach to golf club customisation seeks to improve the efficiency and effectiveness of the Engineering Design process. The primary beneficiaries will be equipment manufacturers, club fitters and the golfers themselves, however , the approach used could easily be adapted for other sectors where the analysis of human-product interactions could lead to more optimal designs for a particular user.