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使用步态生物力学和机器学习进行情绪分类.

Angeloh Stout1, Justin Macneal Cadenhead1, Mrigank Maharana1

  • 1Department of Bioengineering, Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas, Richardson, TX, USA.

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概括
此摘要是机器生成的。

从行走模式中识别情绪是可行的,使用3D步态生物力学和机器学习. 这种方法为情绪识别提供了一种新的方法,在心理健康方面有潜在的应用.

关键词:
生物力学 生物力学情绪识别 情绪识别情绪 情绪 情绪步行方式 步行方式机器学习是机器学习.模式识别 模式识别 模式识别

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科学领域:

  • 生物力学 生物力学
  • 机器学习 机器学习
  • 情感计算是一种情感计算.

背景情况:

  • 情绪影响步行模式,使步态成为情绪识别的潜在数据来源.
  • 基于步态的情绪检测在传统方法 (如面部表情) 上具有优势,因为其减少了操纵易感性.

研究的目的:

  • 通过3D步态生物力学和机器学习算法来确定识别情绪状态的可行性.

主要方法:

  • 15名健康的成年人在步行试验中回忆回忆以引起情绪 (愤怒,悲伤,快乐,恐惧,中立).
  • 一个3D光电子运动捕捉系统记录了步态生物力学,提取了155个变量.
  • 通过交叉验证和解决类不平衡的技术,评估了五个机器学习算法.

主要成果:

  • 机器学习模型在分类情绪状态方面实现了高于偶然的准确性 (59%).
  • 极端梯度提升 (XGBoost) 使用前20个生物力学变量表现出最高的性能 (59%的准确性).
  • 悲伤是最准确检测到的情绪 (准确率为66%).

结论:

  • 与机器学习相结合的3D步态分析显示出作为情感识别的替代方法的希望.
  • 这项研究为开发用于检测心理健康状况中的情绪波动的工具提供了基础证据.