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深度学习模型用于使用IMU传感器对肩膀疼痛康复练习进行分类.

Kyuwon Lee1, Jeong-Hyun Kim1, Hyeon Hong1

  • 1Dept. of Rehabilitation Medicine, Seoul Metropolitan Government Boramae Medical Center, Seoul, South Korea.

Journal of neuroengineering and rehabilitation
|March 28, 2024
PubMed
概括

这项研究表明,人工智能 (AI) 可以使用IMU传感器数据准确地分类肩部疼痛康复练习. 这使得远程患者监测和改善反能够有效恢复.

关键词:
深度学习模型深度学习模型深度神经网络 (DNN) 是一个深度神经网络.练习分类活动的分类.IMU 传感器的传感器机器学习是机器学习.康复练习是一种康复练习.肩膀疼痛 肩膀疼痛 肩膀疼痛 肩膀疼痛可穿戴式传感器

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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
  • 医疗保健中的人工智能

背景情况:

  • 人工智能 (AI) 越来越多地被用于康复,通过传感器技术监控运动遵守.
  • 以前使用IMU传感器对肩膀炼的AI分类仅限于没有疼痛的受试者.
  • 这项研究解决了对患者肩部疼痛康复练习的分类需求.

研究的目的:

  • 用人工智能算法对患有肩部疼痛的患者进行11种类型的肩部康复练习进行分类.
  • 为了证明在临床人群中监测运动遵守的可行性.
  • 为了验证人工智能驱动的练习分类的准确性.

主要方法:

  • 收集了58名患者 (37-82岁) 的数据,这些患者患有肩部疾病,如粘性囊炎和旋转手套疾病.
  • 患者进行了11种类型的肩部疼痛康复练习,每项10次,佩戴IMU传感器.
  • 在深度神经网络 (DNN) 模型中利用了Rectified Linear Unit (ReLU) 和SoftMax激活功能.

主要成果:

  • 一个深度神经网络 (DNN) 模型,采用多层感知算法,在获得的传感器数据上进行训练.
  • 经过训练的模型在分类练习中获得了0.975的高训练精度和0.925的测试精度.
  • 证明了11种不同的肩部疼痛康复练习的有效分类.

结论:

  • 由AI处理的IMU传感器数据可以准确地分类肩部疼痛康复练习,提供更好的患者反.
  • 开发的模型可以支持远程患者监控系统来监测运动表现.
  • 深度学习对于在患者监测和康复方面创新医疗保健服务提供具有重大潜力.