利用Kinect传感器纠正不正确的保龄球形式

Brisaac Johnson, Chris Crawford
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引用次数: 0

摘要

当我们去保龄球馆时,我们常常会被一个似乎每次挥杆都能击中的人迷住。是他们挥球的方式吗?还是因为他们脚的位置?然而,答案比人们想象的要简单得多;我们打保龄球的姿势会使我们得到一个全中球和一个沟球有很大的不同。在这项运动中表现不佳会让人感到沮丧和尴尬,因为不断的沟球和不稳定的摆动。为了增加命中的机会,需要适当的姿势。在本文中,我们提出了一个应用程序,Bowler Correcter,将纠正的形式,一个实时的投球。为了改善投球手的姿势,使用了Xbox Kinect传感器V2来提供必要的调整,以纠正投球手的姿势。为了以合适的形式训练传感器,熟练的投球手向传感器提供训练数据。在筛选状态不佳的投球手时,在非熟练的投球手中观察到以下情况:肩膀紧张,球的摆动不正确,在跟随阶段没有蹲下,跳过站位阶段。我们捕捉的是投球手的骨架,而不是动作,这使我们能够更清楚、更准确地评估投球手的形态。因为这是一个复杂的手势,所以它必须被分解成三个手势,然后存储在保龄球数据库中——跟随,姿态和臂背摇摆;将这个手势分成几个部分可以让传感器更容易检测到,并提供更好的反馈。Kinect SDK和Kinect库用于为Kinect传感器提供必要的驱动程序,并提供创建应用程序所需的库。在为传感器收集训练数据时,使用了应用程序kinect Studio;这个应用程序和视觉手势生成器可以在Kinect SDK中找到。为了训练Kinect传感器,在Visual Gesture Builder中录制的片段被加载到Kinect Studio中,其中每个片段都标注了姿态存在的位置。为了测试应用程序的有效性,使用了PlayStation 3的Sports Champion 2,使用了PlayStation Eye和Move控制器,因为AR和VR的普及使参与者无需前往保龄球馆就可以体验保龄球。此外,考虑到Covid问题,这为确保参与者在保龄球时的清洁、舒适和安全提供了一种方法。状态不佳的保龄球运动员被要求打两场保龄球比赛,一场没有纠正器,另一场有纠正器,难度等级设定为冠军;这增加了移动控制器的灵敏度,并移除了所有阻止球进入排水沟的辅助装置。结果发现,该应用程序为较差的投球手提供了必要的反馈,以纠正他们的姿势并提高他们的表现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Leveraging the Kinect Sensor to Correct Improper Bowling Form
When we go to bowling alleys, more often than not, we are often mesmerized by the one person who seems to get astrike with every swing. Is it the way they swing the ball? Or is it due to their feet placement? However, the answeris more straightforward than one might think; the form we use to bowl can make a massive difference betweengetting a strike and getting a gutter ball. Having poor performance at this sport can make playing it frustrating andembarrassing due to the constant gutter balls and inconsistent swings. To increase the chance of getting a strike,proper form is needed to do so. In this paper, we propose an application, Bowler Correcter, that will correct the formof a bowler in real-time. To improve the form of a poor bowler, the Xbox Kinect sensor V2 was used to provide thenecessary adjustments to correct a bowler's form. To train the sensor with proper form, proficient bowlers providedtraining data to the sensor. When selecting bowlers with poor form, the following was observed in non-proficientbowlers during a screening: tensed shoulders, an improper swing of the ball, not crouching in the follow-throughstage, and skipping the stance stage. We capture the skeleton of the bowler instead of the movement, which allowsus to assess the form of the bowler more clearly and accurately. Since this is a complex gesture, it had to be split upinto three gestures that would be later stored in the bowling database - Follow Through, Stance, and Swing ArmBack; breaking this gesture into parts makes it easier for the sensor to detect and to provide better feedback. TheKinect SDK, along with the Kinect library, was used to provide the necessary drivers for the Kinect sensor andprovide the needed libraries for creating the application. When gathering training data for the sensor, the applicationKinect Studio was used; this application and the Visual Gesture Builder can be found in the Kinect SDK. To trainthe Kinect sensor, clips recorded in the Visual Gesture Builder are loaded into Kinect Studio, where each clip istagged where the stance is present. To test the effectiveness of the application, Sports Champion 2 for PlayStation 3using the PlayStation Eye and Move controller was used as the increase in popularity in AR and VR allowedparticipants to experience bowling without needing to travel to a bowling alley. Additionally, with Covid concerns,this provided a way of ensuring cleanliness, comfort, and the safety of our participants as they bowled. The bowlerswith poor form were asked to play two games of bowling, one game without the corrector and the other with thecorrector, with the difficulty level set to championship; this increased the sensitivity of the Move controller andremoved all aides that prevented the ball from going into the gutter. It was found that this application provided thenecessary feedback needed for poor bowlers to correct their form and improve their performance.
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