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Measurement of Dynamic Scapular Kinematics Using an Acromion Marker Cluster to Minimize Skin Movement Artifact
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一种用于跟踪部运动的新方法,使用可与皮肤相适应的无线加速计:试点研究

Le Huang1, Keum San Chun2, Lian Yu2

  • 1Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.

Digital biomarkers
|April 12, 2024
PubMed
概括

一个新的传感器ADAM和人工智能算法可以在椎手术后跟踪部运动. 这项技术提供了持续的监测,以帮助康复和改善患者在前椎切除和融合后的结果.

关键词:
宫脊椎的部 脊椎的部 脊椎的部数字健康数字健康机器学习是机器学习.康复 康复 康复 康复可以穿戴的电子产品.

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科学领域:

  • 生物医学工程 生物医学工程
  • 康复技术 康复技术 康复技术
  • 医疗保健中的机器学习

背景情况:

  • 椎脊椎疾病显著影响生活质量,往往需要前椎切除和融合 (ACDF).
  • 宫运动范围减少 (CROM) 和疼痛是ACDF后常见的并发症.
  • 目前的CROM评估方法是主观的,很少使用.

研究的目的:

  • 引入先进的声机传感器 (ADAM) 用于在ACDF患者中持续监测CROM.
  • 开发和验证用于分类部运动的机器学习算法.
  • 为ACDF后康复和监测提供一个客观的工具.

主要方法:

  • 开发了一种可安装在皮肤上的声学机械传感器 (ADAM).
  • 使用传感器数据训练和验证了一个卷积神经网络 (CNN) 算法.
  • 该系统在12名健康受试者和5名ACDF患者身上进行了测试,以分类八种不同的部运动.

主要成果:

  • 该算法在健康受试者中在各种部运动中实现了平均准确率为80.0%.
  • 患者数据显示,算法平均准确率为67.5%.
  • 具体运动精度各不相同,旋转的精度更高,收缩的精度更低.

结论:

  • ADAM传感器和人工智能算法显示出作为监测术后 ACDF 患者部运动的康复工具的潜力.
  • 持续监测可以提供客观数据,以指导恢复.
  • 未来的生命体征和其他事件的整合可以提供更全面的患者监测.