使用基于压力中心的算法进行跨步检测,用于实时应用程序
Matjaž Zadravec1, Zlatko Matjačić2
1University Rehabilitation Institute Republic of Slovenia, Linhartova 51, SI-1000, Ljubljana, Slovenia. matjaz.zadravec@ir-rs.si.
Journal of neuroengineering and rehabilitation
|September 16, 2024
概括
这项研究引入了一种新的算法,用于使用来自仪器跑步机的压力中心 (CoP) 信号来实时检测步态事件. 该算法准确地识别了步行事件和跨步,改善了康复中的实际应用.
科学领域:
- 生物力学 生物力学
- 康复工程 康复工程
- 人类机动运动分析分析
背景情况:
- 步行事件检测对于行走康复和生物反至关重要.
- 当前的在线算法通常需要附加传感器,从而限制了实用性.
- 使用压力中心 (CoP) 信号的仪器化跑步机提供了一个无传感器的替代方案,但与乱的行走作斗争.
研究的目的:
- 开发和验证基于CoP的实时算法,用于检测步行事件和跨步.
- 为了解决现有方法在扰乱步行时检测十字步的局限性.
- 为了评估算法在不同的人群和行走条件中的表现.
主要方法:
- 开发了一种结合值和逻辑技术的新型算法.
- 该算法在健康个体,中风幸存者和经过横截肢的人的COP数据上进行了测试.
- 通过比较实时检测到的事件与线下识别的事件来评估性能,测量绝对平均误差 (AME) 和成功率.
主要成果:
- 该算法在检测乱行走期间的本地步态事件和交叉步态事件方面取得了高准确性.
- 在跨步检测中观察到94%的成功率,平均AME的脚跟打击是64 ms,脚脱落是111 ms.
- 在不同的人群中,表现一致,尽管步行速度影响了健康参与者的成功率.
结论:
- 开发的基于CoP的算法推进了仪器跑步机上的实时步态事件检测.
- 它有效地识别了步态事件和跨步,为人类运动提供了有价值的见解.
- 该算法在不同人群和步态模式中的强度支持其在临床步态分析和康复中的潜力.
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