力量,跳跃高度,着陆和机动性指标预测火力和移动评估的高低:一种机器学习方法。

Ayden McCarthy,Joel Thomas Fuller,Jodie Anne Wills,Steve Cassidy,Mita Lovalekar,Bradley C Nindl,Tim L A Doyle
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引用次数: 0

摘要

作战机动性对士兵的生存能力至关重要。军事组织通过例行评估确保有效的作战机动性。先进的统计分析可以提高战斗运动效率的做法。本研究通过探索性因素分析(EFA)对身体素质(如力量、力量、机动性)进行分组,并提取因素来比较高绩效和低绩效,并开发预测模型。方法34名参与者完成了两个阶段的身体素质和战斗运动表现评估。参与者被分为“高”和“低”两类(即分别完成50圈的评估或完成不到50圈的评估)。进行EFA以将物理质量数据集维度降低到特定因素。t检验和效应量比较了高绩效和低绩效之间的因素。对逻辑回归、多层感知器和随机森林模型进行了训练和测试,以根据因子值对表演者进行分类。特征重要性分数决定了对参与者分类影响最大的因素。结果4个因素的方差解释率为81.46%。因子1代表等长强度、跳跃和落地能力。因子2-4分别代表下半身和上半身的等长强度和力量发展速度,以及头顶深蹲能力。各组间各因素差异均有统计学意义(p < 0.05),高绩效者的平均值高于低绩效者。因子1显示了非常大的效应量(d = 2.15),而因子2-4为中等大效应量(d = 0.72-0.81)。逻辑回归模型在测试阶段的准确率为100%,而其他模型的准确率为86%。因素1是所有模型中影响最大的因素(大约是其他因素的六倍)。结论所采用的模型在作战机动性高低分类中具有一定的军事适用性。物理干预优化因子1可以提高作战机动性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Strength, Jump Height, Landing, and Mobility Metrics Predict High and Low Performers of a Fire and Move Assessment: A Machine Learning Approach.
PURPOSE Combat manoeuvrability is critical for soldier survivability. Military organisations ensure effective combat manoeuvrability through routine assessments. Advanced statistical analyses may improve combat movement efficiency practices. This study grouped physical qualities (e.g., strength, power, mobility) via an Exploratory Factor Analysis (EFA) and extracted factors to compare high and low performers and develop predictive models. METHODS 34 participants completed two sessions assessing physical qualities and combat movement performance. Participants were classified as either "high" or "low" performers (i.e., completed 50 laps of the assessment or completed less than 50 laps, respectively). An EFA was conducted to reduce physical quality dataset dimensions into specific factors. T-test and effect size compared factors between high and low performers. Logistic regression, multilayer perceptron, and random forest models were trained and tested to classify performers based on factor values. Feature importance scores determined factors most influential in classifying participants. RESULTS EFA resulted in four factors (81.46% variance explained). Factor 1 represented isometric strength, jumping, and drop landing ability. Factors 2-4 represent isometric strength and rate of force development in the lower and upper body, and overhead squat ability, respectively. All factors significantly differed between groups, with high performers demonstrating higher mean values than low performers (p < 0.05). Factor 1 demonstrated a very large effect size (d = 2.15), while factors 2-4 were moderate-large (d = 0.72-0.81). The logistic regression model had 100% accuracy in the testing phase, while other models achieved 86%. Factor 1 was the most influential factor across models (approximately six times more than other factors). CONCLUSIONS Utilised models show military applicability in classifying high or low performers for combat manoeuvrability. Physical interventions optimising Factor 1 may enhance combat manoeuvrability.
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