深度学习和基于物联网的脚脚整形为增强的步态优化
Ferdous Rahman Shefa1, Fahim Hossain Sifat1, Jia Uddin2
1Department of Computer Science and Engineering, University of Liberal Arts Bangladesh, Dhaka 1207, Bangladesh.
Healthcare (Basel, Switzerland)
|November 27, 2024
概括
本研究介绍了一种使用物联网和机器学习进行步行失衡管理的智能脚脚 (AFO). 变压器模型在分类行走阶段方面实现了98.97%的准确性,从而实现了个性化的治疗建议.
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:
- 步态不平衡会影响流动性和生活质量.
- 脚脚节整形 (AFO) 装置为肌肉软弱或的人提供了至关重要的支持.
- 目前的AFO管理层缺乏个性化,数据驱动的洞察力.
研究的目的:
- 开发一个整合的物联网 (IoT) 和机器学习系统来管理步行失衡.
- 通过复杂的,数据驱动的方法来彻底改变医疗整形.
- 通过个性化的步态管理解决方案,增强患者护理.
主要方法:
- 一个智能脚脚 (AFO) 整合了表面肌电图 (sEMG) 和惯性测量单元 (IMU) 传感器.
- 基于云的数据传输通过雾计算进行实时分析.
- 机器学习模型包括变压器,LSTM,ANN,SVM,随机森林和决策树用于步态阶段分类.
主要成果:
- 变压器模型在分类正常与异常步行阶段时,达到98.97%的准确性.
- 准确的预测和测量患者的步行模式的改善被证明.
- 该系统有效地区分正常和异常步行阶段.
结论:
- 预测分析可以为AFO使用时间和强度提供量身定制的建议.
- 医生验证和患者获取进展报告,确保持续监测和治疗调整.
- 这种方法促进了个性化康复和改善患者的治疗结果.
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