CN101394788A - 步态分析 - Google Patents

步态分析 Download PDF

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CN101394788A
CN101394788A CNA200780007944XA CN200780007944A CN101394788A CN 101394788 A CN101394788 A CN 101394788A CN A200780007944X A CNA200780007944X A CN A200780007944XA CN 200780007944 A CN200780007944 A CN 200780007944A CN 101394788 A CN101394788 A CN 101394788A
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杨广中
卢秉礼
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Abstract

一种用于通过在垂直方向测量头部加速度来分析对象的步态模式的方法和系统。系统包括安装在对象头部的加速计。分析包括根据加速度数据计算特征波形，使用傅立叶变换，包括第一个谐波的能量以及将特征波形与基线特征波形比较。基线特征波形是以前存储的特征波形的代表。完成比较以便随着时间的过去监测步态特征波形的改变。特征波形的熵可用以执行比较。自组织映射用来将测量到的步态信号分类。

Description

步态分析
本发明涉及分析步态的方法和系统。
分析步态时经常期望普遍地监测步态模式，即，与依靠对象在摄像机前踏车上(treadmill)行走形成对比，在对象的自然的环境中监测步态模式。已知的普遍的步态分析系统通常将传感器放置在对象的脚踝、膝部或腰部，目的在于根据腿部运动记录步态模式。然而，由于传感器位置的不同，这些系统往往不能提供准确的测量结果或需要大量的校准来测定可预测的步态模式，例如在受伤后不正常的步态模式。
发明人令人惊讶地发现使用放置在对象头部的加速计，例如使用耳机，可执行有效的步态分析。这样的耳机可被普遍地佩戴，并可例如在伤后恢复的研究中或有关运动的研究中，为步态分析提供对象的步态的准确的测量结果。
本发明在独立权利要求1和10中提出。另外，本发明的实施方式的可选择的特征在剩余的权利要求中提出。
分析可包括通过将由检测的头部加速度得到的特征波形(signature)与一个或更多基线(baseline)特征波形比较，测定步态模式的确定的类型。分析还可包括通过存储由加速度信号得到的特征波形，监测对象的步态模式的历史发展，并将以后的特征波形与一个或更多存储的特征波形比较(因此存储的特征波形起基线的作用)。
优选地，加速度传感器在对象处于直立的姿势时，在基本上垂直的方向感测头部加速度。认为此过程在对象行走或跑步期间，其脚部对地面冲击时，测量了通过脊柱传播到头部的冲击波。
加速度传感器可以用多种方式安装在头部，例如其可在放置在外耳内的耳机中，在耳附近和耳后佩戴的助听器样式的夹子(clip)中，或在佩戴在耳垂上的耳夹或耳环中。可选择地，加速度传感器可固定到另外形式的头部用具(gear)，例如，头巾或帽子、助听器或眼镜，且加速度传感器可在某些应用中通过外科手术植入。
可使用多种技术，例如傅立叶变换或小波分析，由加速度信号得到特征波形。特征波形可以多种方式分析，如下面更加详细地描述的，所述方式包括计算特征波形的熵、将熵用作自组织映射(SOM)或时空自组织映射(STSOM)的输入。
本发明的示例性的实施方式现参考附图描述，其中：
图1A至C示意性地显示了将加速度传感器附于对象的头部的多种不同的方式；
图2A至C显示了利用本发明的实施方式获得的、对象受伤之前和之后以及恢复后的加速度数据；以及
图3A至C显示了相应的傅立叶变换的曲线。
图1A至C示出了用于测量头部加速度的加速度传感器的三种不同的壳体(housing)(A：耳塞；B：耳后夹；C：耳夹或耳环)。在壳体内设置加速度传感器，耦合到用于将加速度信号传输到分析这些信号的处理单元的装置。另外，如下面所详细描述的，壳体也可容纳用于处理这些加速度信号的装置。此处理的结果随即或者传输到处理单元来进一步处理，或者可存储在数字存储装置，比如壳体内的闪存上。尽管图1A-C显示了将加速度传感器安装在对象的耳部上的不同的方式，但将传感器安装在头部的可选择的装置也是可以构想的，例如安装在头巾或帽子上，或结合在一副眼镜或头戴耳机中。
加速度传感器可沿着一个或更多轴，例如当对象站直时与水平线对准的轴以及与垂直线对准的轴，测量加速度。当然，也可使用三轴的加速计。
应理解，壳体也可容纳更多的运动传感器，比如回转器(gyroscope)或滚珠(ball)或杠杆开关传感器。此外，使用任何类型的运动传感器来测定头部运动的步态分析也是可以构想的。
图2A至C显示了如所描述的佩戴的这样的加速度传感器的两轴的每轴的输出，用深色迹线(trace)显示水平分量而用较浅的迹线显示垂直分量。图2A至C中曲线图的y轴显示了以任意单位测量到的加速度，而x轴表示以50Hz的取样频率的连续的样本。迹线的循环特征清楚地表明，每幅图都显示了几个脚步(footstep)循环。
本实施方式使用头部加速度的垂直分量(图2A至C中较浅的迹线)来分析步态。认为此加速度信号代表在行走或跑步期间，脚部冲击地面时，沿脊柱向上传播的冲击波。此冲击波已被发现富有关于对象的步态模式的信息。
例如，对于健康的对象，正如在显示健康的对象的加速度迹线的图2A中可看到的，步态模式往往是高度地重复的。相比之下，在显示脚踝受伤后的对象的加速度迹线的图2B中，可以看到受伤后，特别是对于垂直加速度(较浅迹线)，加速度迹线变得更加多变。认为这种情况与当对象用伤腿行走时的保护行为有关，例如对象将脚放下时脚趾先着地而不是脚跟先着地，随后进行如正常行走时脚步的起伏。
图2C显示了来自相同的对象的恢复后的加速度迹线，很显然，规律性的重复特征，特别是垂直加速度迹线的重复特征已被恢复。
基于上面的发现，代表受伤的步态模式的测定(或者，一般地，不同于基线步态模式的步态模式的测定)可通过对上述的加速度信号的适当的分析而实现。在一个实施方式中，使用傅立叶变换分析垂直的加速度信号，例如，使用具有1024个样本的滑动窗口的快速傅立叶变换(FFT)算法计算垂直的加速度信号。不正常的步态模式可随即根据频率含量测定。
图3A至C显示了图2A至C的各自加速度测量结果的FFT。y轴以任意单位为单位而x轴以(25/512)Hz，即，约0.05Hz为单位。尽管FFT的能量的绝对值(沿y轴作图)将依赖于以下因素：比如与通过脊柱传播的冲击波相关的加速度传感器的精确方向和加速度传感器在头部的位置，以及步态的整个步调，但曲线清楚地包含了关于不同频率下的FFT的能量的相对量值中的步态模式类型的信息。很显然FFT峰的相对量值已经改变。
如可从图3A中看到的，健康的对象的加速度信号的FFT显示了多个衰减的谐波。相比之下，腿伤数据(图3B)显示了更广泛的频率含量，在这些频率含量中频谱没有图3A的明确定义的峰，且不一致的谐波指示不正常的步态。图3C显示了相同对象恢复后的加速度数据的FFT，且可看出，很大程度上，伤前模式已经恢复。
概括来说，指示步态模式的特征波形可由加速度数据得到，并用以将步态模式分类为例如由以上数据表明的上述正常的或受伤的步态模式。在上面的实例中，特征波形为傅立叶变换。应理解，计算特征波形的其他的方法同样地可以构想。例如，可利用小波分析计算特征波形，例如通过传递经过小波变换(例如，一阶Debauchies)的数据，并随即使用变换后的数据作为例如SOM的分类器的输入。例如，只有小波变换的第一个高频分量可用来作为分类器的输入。
一旦特征波形如上面描述的被得到，其可被自动地分析以便测定步态模式中的改变。一方面，期望测定所述步态模式是否接近期望的步态模式。例如在训练运动员方面，这非常有用。为了此目的，获得从对象例如运动员的加速度数据得到的特征波形，并与从代表期望行为的基线数据获得的基线特征波形比较。作为结果的信息可随即用以在运动员的训练时帮助运动员，例如帮助长跑运动员调整其腿部的运动。
另一方面，期望使用上面的分析来测定对象随着时间过去的改变。例如，这在普遍的健康监测方面非常有用，在普遍的健康监测中，病人的步态模式可被监测，这使得到当测定出指示损伤的步态模式的改变时，可告知医生或保健专业人员。
例如，可用来测定特征波形中的改变的一种测量方法是计算特征波形的熵。在参考图3A至C描述的FFT的实施例中，很明显受伤数据的熵值会远大于正常数据的熵值。
一种比较并分类特征波形的方法是将特征波形用作自组织映射(SOM)的输入。例如，在前四个谐波处的FFT的能量可用作SOM的输入矢量。本领域中的技术人员将意识到SOM对于数据分析和分类的作用，且如上所述的分析特征波形的SOM的实现完全是本领域的普通技术人员力所能及的。简要地，在用来允许SOM确定的足够长时间的训练期间，向SOM提供由上面描述的特征波形得到的输入矢量。随后，SOM的输出单元的启动可随即用来将数据分类。例如，已发现在来自图2和图3的对象的训练过的SOM数据可在受伤前启动单元的第一子集而在受伤后启动单元的第二子集。
在上述的实施方式中，使用滑动窗口FFT计算了特征波形。同样地，作为结果的特征波形将随时间变化，这使得SOM的多于一个的单元将随时间的过去而被启动。如果期望分析由特征波形得到的输入矢量的随时间变化的特征，则可使用在此作为引用而并入的共同未决专利申请WO2006/097734中描述的可选择的分析技术。所述申请描述了下面称为时空SOM(STSOM)的SOM的一种配置，其中，依赖于第一层SOM的输出的时间变化的测量，向第二层SOM供给测量原始输入矢量中的特征的时间变化的变换后的输入矢量。如在常规的SOM中，第二时间层SOM的输出可随即用来基于时间结构对数据进行分类。
简要地，使用STSOM对数据记录进行分类包括：
(a)定义指示在时间窗口内的传感器信号的时间变化的选择变量；
(b)定义选择变量的选择标准；
(c)将选择变量的值与选择标准比较以选择自组织映射的输入表示，并根据所选择的输入表示从时间窗口内的数据样本得到输入；以及
(d)相应于所选择的输入表示法，将输入应用于自组织映射并基于自组织映射的得到的输出单元，对数据记录进行分类。
例如，选择变量可基于SOM的输出单元的时间变化性计算。
训练STSOM可包括：
(a)计算代表时间窗口内的动态数据记录的特征的时间变化的所得到的表示；
(b)将所得到的表示用作第二自组织映射的输入；以及
(c)根据训练算法，更新自组织映射的参数。
训练可能包括基于时间变化的测量将训练数据划分为静态记录和动态记录的预备步骤。训练STSOM和将其用于分类的进一步的细节可在上述的公开的专利申请中找到。
应理解，上面描述的实施方式的传感器信号也可用于人的姿势分析及/或行为识别。另外，上面描述的系统可以是传感装置的身体传感器网络的主要部分，其中在身体上分布的多种传感装置通过无线通信链路连接。

Claims (20)

1.一种分析步态的方法，包括测量表示步态被分析的对象的头部的加速度的信号，以及将变换应用于所测量到的信号以计算表示所述对象的所述步态的步态特征波形。
2.如权利要求1所述的方法，进一步包括将所述步态特征波形与基线特征波形比较以测定其间的差别。
3.如权利要求2所述的方法，其中一个或更多的特征波形随着时间的过去被存储以及所述基线特征波形表示一个或更多存储的特征波形，以便监测随着时间的过去所述步态特征波形中的改变。
4.如权利要求1、权利要求2或权利要求3所述的方法，其中当所述对象处于直立的姿势时，测量到的特征波形代表在基本上垂直的方向上的加速度。
5.如先前的权利要求的任一项所述的方法，其中所述变换为傅立叶变换。
6.如权利要求5所述的方法，其中所述特征波形包括前n个谐波的能量的值。
7.如权利要求1至4的任一项所述的方法，其中所述变换为小波分析。
8.如先前的权利要求的任一项所述的方法，其中所述特征波形用作自组织映射或时空自组织映射的输入。
9.如先前的权利要求的任一项所述的方法，包括计算所述特征波形的熵，并使用计算得到的熵比较特征波形。
10.一种步态分析系统，包括加速度传感器以及分析器，所述加速度传感器安装在适于固定到人的头部的传感器壳体中，所述分析器可操作地耦合到传感器并可操作以接收表示来自头部加速度的输出，且对所述头部加速度应用变换来计算表示步态模式的步态特征波形。
11.如权利要求10所述的系统，其一步包括可操作以将所述特征波形与基线特征波形比较以便测定其间的差别的比较器。
12.如权利要求11所述的系统，其进一步包括存储器，所述存储器用于存储其中基线代表一个或更多存储的特征波形的一个或更多特征波形，以使得所述比较器可用来监测随着时间的过去所述特征波形的改变。
13.如权利要求10至12的任一项所述的系统，其中所述壳体适于被安装，以使得当所述对象处于直立的姿势时，所述输出表示在基本上垂直的方向上的头部加速度。
14.如权利要求10至13的任一项所述的系统，其被包括在所述壳体中。
15.如权利要求10至14的任一项所述的系统，其中所述壳体包括耳塞、耳后夹、耳环、耳夹、助听器或一副眼镜。
16.如权利要求10至15的任一项所述的系统，其中所述壳体被固定到头巾、帽子或其他的头部用具。
17.如权利要求10至16的任一项所述的系统，其中所述变换为傅立叶变换。
18.如权利要求17所述的系统，其中所述特征波形包括前n个谐波的能量的值。
19.如权利要求10至16的任一项所述的系统，其中所述变换为小波分析。
20.如权利要求10至19的任一项所述的系统，其进一步包括另外的分析器，所述分析器包括可操作以接收作为输入的所述特征波形的自组织映射或时空自组织映射。
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