基于脚的IMU算法为检测日常生活中的行走提供了高特异性
概括
一个使用双脚上的惯性测量单位 (IMU) 的新算法准确地检测日常生活中的行走时间. 这种方法为监测行走障碍的个体提供了高度的特异性.
科学领域:
- 生物力学 生物力学
- 可穿戴技术可穿戴技术
- 步态分析 步态分析
背景情况:
- 生态有效的步态特征对于监测步态障碍的个体至关重要.
- 从长期记录中精确选择步行时间是步态分析的先决条件.
研究的目的:
- 开发和验证一种新的步行检测算法,使用双脚上的惯性测量单位 (IMU).
- 为了比较"双脚"算法的性能与使用圣骨加速计和单脚陀螺数据的现有方法.
- 识别可能损害行走检测算法的特异性的日常活动.
主要方法:
- 开发了一种利用双脚IMU的算法,涉及潜在步行期选择,排除非步行期和验证最小步行期要求的阶段.
- 算法性能 (特异性,灵敏度,准确性) 在32名参与者 (12名健康者,20名神经性行走障碍者) 中在20-30分钟的日常活动中得到验证,使用视频录像作为基本真相.
- 与使用圣骨加速计数据和单脚陀螺仪数据的算法进行了比较.
主要成果:
- 与"单脚" (92.1%) 和"Sacrum" (72.1%) 算法相比",双脚"算法显示出更高的特异性 (96.6%).
- 爬楼梯是最常被错误地归类为跨越所有测试算法的活动.
- 虽然"Sacrum"算法获得了更高的灵敏度 (99.5%),但其特异性和准确性较低.
结论:
- "双脚"算法的高特异性使其适合在日常生活中获得时空步态特征.
- 该算法适用于监测影响行走的轻度神经疾病的个体.
- 进一步的研究应该解决错误的分类活动,如爬楼梯.
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