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技术说明:利用机器学习从股骨干骺端形状预测大猩猩和人类的运动行为。

Technical Note: Using Machine Learning to Predict Locomotor Behavior in Great Apes and Humans From Femur Metaphyseal Shape.

作者信息

Stamos Peter A, Chaudhari Abhijit J, Grote Mark N, Weaver Timothy D

机构信息

Department of Anthropology, University of California, Davis, Davis, California, USA.

Department of Radiology, University of California, Davis, Davis, California, USA.

出版信息

Am J Biol Anthropol. 2025 Jun;187(2):e70066. doi: 10.1002/ajpa.70066.

DOI:10.1002/ajpa.70066
PMID:40462397
Abstract

OBJECTIVES

The morphology of the hominoid distal femoral metaphyseal surface has been demonstrated to reflect locomotor behavior throughout ontogeny. Here, we quantify metaphyseal surface morphology to evaluate its predictive relationship to locomotor behavioral modes in hominoids.

MATERIALS AND METHODS

We collected three-dimensional (3D) surface laser scans of the femora of 177 human and great ape individuals representing all subadult stages of development. We used the landmark-free Global Point Signature (GPS) method to quantify the shape of the morphologically complex but amorphous metaphyseal surface. We then analyzed the GPS quantifications of shape using support vector machines (SVMs), a machine learning technique, to evaluate the predictive relationships between metaphyseal surface morphology and locomotor behavior in hominoids.

RESULTS

We found that metaphyseal surface morphology is a strong predictor of locomotor behavior in hominoids. Our SVM, which relates nonambulation, bipedal walking, knuckle-walking, and climbing behavior with metaphyseal surface morphology, exhibits ~84% out-of-sample predictive accuracy.

CONCLUSIONS

Our quantitative analyses confirm what has previously been qualitatively described-the metaphyseal surface of the distal femur is highly predictive of the locomotor behavior performed by hominoids during different stages of their lives. These results suggest that this region of the skeleton is suitable for reconstructing the locomotor behavior of extinct hominoid taxa.

摘要

目的

类人猿股骨远端干骺端表面的形态已被证明能反映个体发育过程中的运动行为。在此,我们对干骺端表面形态进行量化,以评估其与类人猿运动行为模式之间的预测关系。

材料与方法

我们收集了177名人类和大猩猩个体股骨的三维(3D)表面激光扫描数据,这些个体代表了所有亚成年发育阶段。我们使用无标记的全局点特征(GPS)方法来量化形态复杂但无定形的干骺端表面的形状。然后,我们使用支持向量机(SVM)这一机器学习技术分析形状的GPS量化结果,以评估类人猿干骺端表面形态与运动行为之间的预测关系。

结果

我们发现干骺端表面形态是类人猿运动行为的有力预测指标。我们的支持向量机将非移动、双足行走、指关节行走和攀爬行为与干骺端表面形态相关联,其样本外预测准确率约为84%。

结论

我们的定量分析证实了之前的定性描述——股骨远端的干骺端表面对类人猿在其生命不同阶段所进行的运动行为具有高度预测性。这些结果表明,骨骼的这一区域适合重建已灭绝类人猿分类群的运动行为。

相似文献

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Technical Note: Using Machine Learning to Predict Locomotor Behavior in Great Apes and Humans From Femur Metaphyseal Shape.技术说明:利用机器学习从股骨干骺端形状预测大猩猩和人类的运动行为。
Am J Biol Anthropol. 2025 Jun;187(2):e70066. doi: 10.1002/ajpa.70066.
2
Ontogeny of the distal femoral metaphyseal surface and its relationship to locomotor behavior in hominoids.同源异形股骨干骺端表面的发生及其与灵长类动物运动行为的关系。
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