• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>2012 4th IEEE RAS amp; EMBS International Conference on Biomedical Robotics and Biomechatronics >Prediction of gait recovery as a tool to rationalize locomotor training in spinal cord injury
【24h】

Prediction of gait recovery as a tool to rationalize locomotor training in spinal cord injury

机译：步态恢复的预测作为合理的脊髓损伤运动训练的工具

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Our objectives were to explore the effects of robotic-orthosis (LOKOMAT) training on walking impairment recovery in subjects with incomplete spinal cord injury (SCI), and to develop robust predictors of these recovery patterns. Twelve SCI subjects with different degrees of ankle spasticity participated in a 12-session LOKOMAT training regimen. One-hour gait training sessions were provided three times per week for four weeks. Subjects were evaluated at baseline, 1, 2 and 4 weeks after training. The 10-meter and 6-min walking tests and the Time-up-and-Go tests were used to evaluate gait speed and endurance, and functional ambulation and balance. A “growth mixture” model was used to characterize different recovery patterns of these measures. Logistic regression was further used to predict these recovery patterns based on the isometric voluntary contractions (MVC) of ankle flexors and extensors at the baseline. Our results showed that subjects were separable into two different classes of recovery based on severity of their baseline impairments; subjects with a higher walking capacity at the start of training showed significant improvement over four weeks of training. Our findings demonstrated that MVCs were able to predict recovery class membership and can potentially be used as significant predictors for therapeutic functional recovery after SCI.
机译：我们的目标是探讨机器人矫形器（LOKOMAT）训练对脊髓不完全损伤（SCI）的受试者行走障碍恢复的影响，并为这些恢复模式建立可靠的预测指标。十二名不同程度的踝痉挛的SCI受试者参加了为期12节的LOKOMAT训练方案。每周提供三次为时一小时的步态训练课程，持续四个星期。在训练后1、2和4周时对受试者进行基线评估。使用10米长和6分钟的步行测试以及“起步”测试来评估步态速度和耐力以及功能性移动和平衡。使用“增长混合”模型来表征这些措施的不同回收模式。基于基线时踝屈肌和伸肌的等距自愿收缩（MVC），进一步使用Logistic回归预测这些恢复模式。我们的结果表明，根据基线损伤的严重程度，受试者可分为两类不同的恢复；训练开始时具有较高步行能力的受试者在训练的四个星期中显示出明显的改善。我们的发现表明，MVC能够预测恢复类别的成员资格，并且有可能被用作SCI后治疗功能恢复的重要预测因子。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号