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在大麻和酒精之间进行歧视 走路障碍 使用 CNN 与TICA 聚合

Ruojun Li1, Samuel Chibuoyim Uche1, Emmanuel Agu1

  • 1Worcester Polytechnic Institute Worcester MA 01609 USA.

IEEE open journal of engineering in medicine and biology
|November 12, 2025
PubMed
概括

机器学习可以从智能手机步行数据中识别酒精或大麻损伤. 新的MariaGait深度学习模型使用传感器数据准确地区分这些物质障碍.

关键词:
加速度计的速度计.酒精损伤 酒精损伤 酒精损伤深度学习是一种深度学习.陀螺仪陀螺仪的使用方法大麻损伤损伤

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科学领域:

  • 生物医学工程 生物医学工程
  • 数据科学数据科学数据科学
  • 神经科学是一个神经科学.

背景情况:

  • 步态分析是物质损伤的潜在生物标志物.
  • 区分酒精和大麻的使用障碍使用步态是具有挑战性的.

研究的目的:

  • 开发和验证一个机器学习模型,MariaGait,使用智能手机步行数据来区分酒精和大麻损伤.
  • 评估深度学习技术的有效性,特别是卷积神经网络 (CNN),用于此分类任务.

主要方法:

  • 利用时间序列智能手机加速度计和陀螺仪数据从受损的步态数据集.
  • 将步态数据编码成格拉米安角场 (GAF) 图像.
  • 雇佣了一个带有TICA组合的有的CNN,预先训练了清醒步行样本来处理不平衡的数据.

主要成果:

  • 玛丽亚盖特实现了94.61%的准确性,88.61%的F1得分和94.33%的ROC AUC.
  • 超过基线模型的表现,包括MLP,LSTM,随机森林和SVM.
  • 在将酒精与大麻损伤分类方面表现出很高的表现.

结论:

  • 玛丽亚盖特提供了一种实用且非侵入性的方法来识别物质损伤的类型.
  • 智能手机传感器数据与深度学习相结合,可以根据步态模式有效区分酒精和大麻中毒.