Prediction of lower limb joint stiffness and optimization of anthropometric parameters in countermovement jump using an anthropometry-informed neural network

IF 1.4 3区医学 Q4 ENGINEERING, BIOMEDICAL

Clinical Biomechanics Pub Date : 2025-08-06 DOI:10.1016/j.clinbiomech.2025.106646

Parisa Hejazi Dinan , Hamed Nazemi , Amirhossein Emamian

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引用次数: 0

Abstract

Background

The Countermovement Jump (CMJ) test, widely used to assess athletes' musculoskeletal and neuromuscular readiness, hinges on the performance of the hip, knee, and ankle joints. Despite extensive research, there is no consensus on which joint is most critical for CMJ performance. This study aims to identify the primary lower limb joint contributing to CMJ execution by analyzing maximum energy production and peak stiffness. Additionally, a novel neural network model was developed to predict joint stiffness during CMJ based on jump height and detailed anthropometric parameters, including body fat mass, lower body mass, upper body mass, and skeletal muscle mass ratios. Finally, a genetic algorithm was employed to optimize these parameters, maximizing joint stiffness and energy output.

Methods

Twelve male athletes performed CMJs, with data cleaning applied to their trials. Energy production and stiffness of the hip, knee, and ankle joints were calculated. The neural network, trained on joint stiffness data, facilitated two optimization problems solved via a genetic algorithm to determine optimal anthropometric parameters for maximizing joint peak stiffness and energy.

Findings

The hip joint was identified as the primary energy contributor (4.75 ± 1.71 J/kg), while the knee exhibited the highest peak stiffness (0.37 ± 0.04 N.m/°kg). The knee outperformed the hip (0.29 ± 0.02 N.m/°kg) and ankle (0.25 ± 0.04 N.m/°kg) in stiffness.

Interpretation

The hip generates the most energy during CMJ, while knee stiffness is crucial. Jump height, body fat, and skeletal muscle mass ratios significantly influence joint stiffness.

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利用人体测量信息神经网络预测反动作跳跃中下肢关节刚度并优化人体测量参数

反向运动跳跃（CMJ）测试，广泛用于评估运动员的肌肉骨骼和神经肌肉准备情况，取决于髋关节，膝关节和踝关节的表现。尽管广泛的研究，没有共识的关节是最关键的CMJ的性能。本研究旨在通过分析最大能量产生和峰值刚度来确定对CMJ执行有贡献的下肢主要关节。此外，开发了一种新的神经网络模型，基于跳跃高度和详细的人体测量参数（包括体脂质量、下体质量、上体质量和骨骼肌质量比）来预测CMJ期间的关节刚度。最后，采用遗传算法对这些参数进行优化，使关节刚度和能量输出最大化。方法12名男性运动员进行CMJs，并对其试验进行数据清洗。计算髋关节、膝关节和踝关节的能量产生和刚度。该神经网络在关节刚度数据上进行训练，通过遗传算法解决了两个优化问题，以确定最大关节峰值刚度和能量的最佳人体测量参数。研究结果髋关节被认为是主要的能量贡献者（4.75±1.71 J/kg），而膝关节表现出最高的峰值刚度（0.37±0.04 N.m/°kg）。膝关节刚度优于髋关节（0.29±0.02 N.m/°kg）和踝关节（0.25±0.04 N.m/°kg）。在CMJ过程中，髋部产生的能量最多，而膝关节的僵硬是至关重要的。跳跃高度、体脂和骨骼肌质量比显著影响关节刚度。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Clinical Biomechanics 医学-工程：生物医学

CiteScore

3.30

自引率

5.60%

发文量

189

审稿时长

12.3 weeks

期刊介绍： Clinical Biomechanics is an international multidisciplinary journal of biomechanics with a focus on medical and clinical applications of new knowledge in the field. The science of biomechanics helps explain the causes of cell, tissue, organ and body system disorders, and supports clinicians in the diagnosis, prognosis and evaluation of treatment methods and technologies. Clinical Biomechanics aims to strengthen the links between laboratory and clinic by publishing cutting-edge biomechanics research which helps to explain the causes of injury and disease, and which provides evidence contributing to improved clinical management. A rigorous peer review system is employed and every attempt is made to process and publish top-quality papers promptly. Clinical Biomechanics explores all facets of body system, organ, tissue and cell biomechanics, with an emphasis on medical and clinical applications of the basic science aspects. The role of basic science is therefore recognized in a medical or clinical context. The readership of the journal closely reflects its multi-disciplinary contents, being a balance of scientists, engineers and clinicians. The contents are in the form of research papers, brief reports, review papers and correspondence, whilst special interest issues and supplements are published from time to time. Disciplines covered include biomechanics and mechanobiology at all scales, bioengineering and use of tissue engineering and biomaterials for clinical applications, biophysics, as well as biomechanical aspects of medical robotics, ergonomics, physical and occupational therapeutics and rehabilitation.