CN107430443A - 基于宽场雷达的手势识别 - Google Patents
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- G06F3/011—Arrangements for interaction with the human body, e.g. for user immersion in virtual reality
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Abstract
本文描述了使用基于宽场雷达的手势识别的技术和具体实现基于宽场雷达的手势识别的设备。这些技术和设备可以实现广泛的手势和这些手势的用途，诸如，使用、控制从软件应用到冰箱的计算设备和非计算设备、以及与计算设备和非计算设备交互的手势。
Description
相关申请
本申请要求2015年4月30日提交的美国临时专利申请第62/155,357号的优先权，其全部公开内容以引用的方式并入本文。
背景技术
诸如智能手机和计算手镯、戒指和手表的小屏幕计算设备持续增多。像许多计算设备一样，这些小屏幕设备通常使用虚拟键盘与用户进行交互。然而，在这些小屏幕上，许多人发现通过虚拟键盘进行交互很困难，因为它们往往会导致输入速度慢和输入不准确。这使用户有挫败感，并且限制了小屏幕计算设备的适用性。基于屏幕的手势识别技术已经部分地解决了该问题。然而，由于这些屏幕的尺寸，这些基于屏幕的手势仍然存在重大可用性问题。
为了解决该问题，已经开发了光学手指追踪技术和人手追踪技术，这实现了不在屏幕上进行的手势追踪。然而，这些光学技术庞大、昂贵或者不准确，从而限制了它们在解决小屏幕计算设备存在的可用性问题上的作用。
最近已经开发了另一种其它方式，其中，使用雷达来追踪手势。然而，目前的雷达技术通常需要大型天线阵列，并且存在许多实际困难。这些大型天线阵列使用细波束扫描技术来定位空间中的大量点，这些点包括人类动作的点(例如，手指、手臂或者手)。这些技术追踪人类动作的这些点和空间中的其它点，然后确定哪些点与人类动作有关，哪些点与人类动作无关。在确定了这些动作点后，该技术追踪其移动，并且根据动作点的这些移动来重构整个移动中的动作。利用这种重构的移动，这些技术再确定与这些移动相关联的手势。这允许一些基本的手势识别，但受到大型天线阵列以及在使用细波束扫描技术时所固有的计算困难和资源要求的限制。
发明内容
本文描述了基于宽场雷达的手势识别的技术和设备。这些技术和设备可以准确地识别三维手势，诸如，非屏幕或者“空中”手势。这些空中手势可以从不同的距离做出，诸如，从坐在沙发上的人控制电视、站在厨房里的人控制烤箱或者冰箱、或者离计算手表的小屏幕显示器几厘米。
本发明内容的提供是为了介绍基于宽场雷达的手势识别的简化构思，该构思将在以下具体实施方式中进行进一步说明。本发明内容不旨在识别所要求的主题的基本特征，也不旨在确定所要求的主题的范围。
附图说明
参考以下附图来说明基于宽场雷达的手势识别的技术和设备的实施例。在整个附图中使用相同的数字来引用类似的特征和组件。
图1示出了一种可以实现基于宽场雷达的手势识别的示例环境。
图2详细示出了图1中的基于宽场雷达的手势识别系统和计算设备。
图3示出了一种确定手势的信号元素的示例方法。
图4示出了做出的手势和基于那些手势所确定的信号元素。
图5示出了一种使用图3中确定的信号元素来实现基于宽场雷达的手势识别的示例方法。
图6示出了示例特定于类型的和与类型无关的手势管理器，其包括示例特定于类型的硬件抽象模块。
图7示出了做出的手势和使用特定于类型的硬件抽象模块所确定的信号元素的示例。
图8示出了一种体现基于宽场雷达的手势识别或者其中可以实现能够启用基于宽场雷达的手势识别的技术的示例计算系统。
具体实施方式
概述
本文描述了实现基于宽场雷达的手势识别的技术和设备。这些技术和设备可以实现大量手势和这些手势的用途，诸如，使用、控制、以及与各种设备(从智能手机到冰箱)交互的手势。这些技术和设备能够提供可以使用相对较小的雷达系统，甚至是可以包括在小型设备内的雷达系统，来感测手势的宽场雷达。此外，这些技术不需要追踪和重构人类动作的点以确定手势，这具有如下各种优点。
本文现在转向一种示例环境，之后将描述示例基于宽场雷达的手势识别系统和雷达场、示例方法、开发硬件抽象模块的示例技术及其设备、以及示例计算系统。
示例环境
图1是可以体现使用了基于宽场雷达的手势识别系统102的技术和包括基于宽场雷达的手势识别系统102的设备的示例环境100的图示。环境100包括使用基于宽场雷达的手势识别系统102的两种示例设备和技术，在第一种中，基于宽场雷达的手势识别系统102-1提供中等尺寸的雷达场以与计算设备104中的一个——桌面型计算机104-1——交互，并且在第二种中，基于宽场雷达的手势识别系统102-2提供小型尺寸的雷达场以与计算手表104-2交互。
桌面型计算机104-1包括宽场雷达手势识别系统102-1或者与宽场雷达手势识别系统102-1相关联。这些设备协同工作，以改善用户与桌面型计算机104-1的交互。例如，假设桌面型计算机104-1包括可以执行显示和用户交互的触摸屏108。该触摸屏108可以为用户呈现一些挑战，诸如，需要人坐在特定定向，诸如，竖直和向前，以能够触摸到屏幕。此外，通过触摸屏108选择控件的尺寸可以使一些用户的交互困难且耗时。然而，考虑到基于宽场雷达的手势识别系统102-1，该系统102-1提供使用户的手与桌面型计算机104-1进行交互的雷达场106-1，诸如，利用小的或者大的、简单的或者复杂的手势，包括利用一个或者两个手进行的手势和三维手势。显然，可供用户通过其做出选择的大体积实质上可以更容易并且可以在诸如触摸屏108的平坦表面的平坦表面上提供更好的体验。
相似地，考虑基于宽场雷达的手势识别系统102-2，该基于宽场雷达的手势识别系统102-2提供雷达场106-2，使用户能够近距离与计算手表104-2进行交互，从而实现手指、手和手臂手势。如此一来，可以使得用户的选择比诸如计算手表104-2的小屏幕的小型计算设备的小屏幕更简易。
基于宽场雷达的手势识别系统102可以与计算设备104的应用或者操作系统交互，或者通过响应于识别到手势而发送输入来通过通信网络远程进行交互。可以将手势映射到各种应用和设备，从而实现对许多设备和应用的控制。基于宽场雷达的手势识别系统102可以识别许多复杂而独特的手势，从而，即使对于多个应用，也能够进行精确控制和/或单手势控制。无论是与计算设备集成、具有计算能力、或者具有很少计算能力，基于宽场雷达的手势识别系统102都可与各种设备和应用进行交互。
更详细地，考虑图2，其图示出了基于宽场雷达的手势识别系统102，作为其中计算设备104的部分。计算设备104示出有各种非限制性示例设备，所提到的桌面型计算机104-1、计算手表104-2、以及智能电话104-3、平板104-4、计算戒指104-5、计算眼镜104-6和微波104-7，尽管也可以使用其它设备，诸如家用自动化控制系统、娱乐系统、音频系统、其它家用电器、安全系统、上网本和电子阅读器。注意，计算设备104可以是可穿戴的，也可以是不可穿戴的但却可移动的，或者相对不动的(例如，桌面型设备和电器)。
还应注意，基于宽场雷达的手势识别系统102可以与许多不同的计算设备或者外设一起使用，或者，嵌入到许多不同的计算设备或者外设中，诸如，嵌入家庭的墙壁中以控制家用电器和系统(例如，自动化控制面板)、嵌入汽车中以控制内部功能(例如，音量、巡航控制、甚或汽车驾驶)、或者作为膝上型计算机的附件来控制膝上型计算机的计算应用。
此外，雷达场106可以是不可见的并且能穿透一些材料，例如纺织品，从而进一步扩展可以使用和体现基于宽场雷达的手势识别系统102的方式。虽然本文示出的示例通常显示每个设备有一个基于宽场雷达的手势识别系统102，但是可以使用多个系统，从而增加手势的数量和复杂性、以及准确性和鲁棒识别。
计算设备104包括一个或多个计算机处理器202和计算机可读介质204，该计算机可读介质204包括存储器介质和存储介质。在计算机可读介质204上体现为计算机可读指令的应用和/或操作系统(未示出)可由处理器202执行以提供本文描述的一些功能。计算机可读介质204还包括手势管理器206(如下所述)。
计算设备104还可以包括用于通过有线、无线、或者光学网络传送数据的网络接口208、和显示器210。作为示例而非限制，网络接口208可以通过局域网(LAN)、无线局域网(WLAN)、个域网(PAN)、广域网(WAN)、内联网、互联网、对等网络、点对点网络、网状网络等来传送数据。
如上所述，基于宽场雷达的手势识别系统102被配置成感测手势。为了实现这一点，基于宽场雷达的手势识别系统102包括雷达发射元件212和天线元件214。
通常，雷达发射元件212被配置成提供与窄波束扫描雷达场相反的宽场雷达。在一个实施例中，使用大型连续场，而不是波束扫描场。该场中的反射然后可以被一个或多个天线接收。该反射信号包括许多信号和信号元素，因此可以称为反射信号或反射信号集合，但是在这两种情况下都包括多个信号元素。在一种情况下，在接收器处接收大型雷达场和大场反射，但是通过数字分解接收到的反射来处理接收到的信号。然后单独分析分解后的信号。这可以称为波束窃取，尽管在大型雷达场中实际上没有形成波束。可以使用其它数字处理，诸如，其中允许不同相位的场的相位阵列，每个相位阵列用于对反射信号进行分析。
在另一个实施例中，使用多个来自不同的方向或者具有诸如不同的频率或者相位的其它差异的场，并且可由一个或者多个专用接收器(例如，天线元件214)接收。
更具体地说，这些场包括许多类型的雷达场，诸如，来自连续波和脉冲雷达系统的雷达场，并且可以排除相控天线阵列。脉冲雷达系统通常具有较短的发射时间和较高的峰值功率，并且包括冲击(impulse)和啁啾(chirped)雷达系统。脉冲雷达系统具有基于飞行时间的范围和基于频移的速度。啁啾雷达系统具有基于飞行时间(脉冲被压缩)的范围和基于频移的速度。
连续波雷达系统通常具有相对较长的发射时间和较低的峰值功率。连续波雷达系统包括单频区(tone)、线性调频(FM)和步进FM类型。单频区雷达系统具有非常有限的基于相位的范围和基于频移的速度。线性FM雷达系统具有基于频移的范围和同样基于频移的速度。步进FM雷达系统具有基于相位或飞行时间的范围和基于频移的速度。虽然在此提及了这五种类型的雷达系统，但是也可以使用其它系统，诸如正弦调制方案雷达系统。
这些类型的雷达系统提供的雷达场的尺寸不同，从诸如0或者1或者几毫米至1.5米的小尺寸、或者从诸如大约1米至约30米的中等尺寸。在中等尺寸中，天线元件214被配置成接收和处理雷达场的反射，以基于由身体、手臂或者腿部移动引起的来自人类组织的反射提供大型身体手势，尽管也能感测到更小的手势和更精确的手势。示例中等尺寸的雷达场包括其中用户使用手势从沙发上控制电视、跨越房间内改变来自立体声的歌曲或者音量、关闭烤箱或者烤箱定时器(近场也会是有用的)、打开或者关闭房间中的灯等等的雷达场。
相反，雷达发射元件212可以被配置成如果离计算设备或者其显示器存在任何距离，则从小雷达场提供宽雷达场，该雷达场包括与波束扫描雷达场相反的完全连续的雷达场。图1利用雷达场106示出了示例。
雷达发射元件212可以被配置成提供上述各种类型的宽场雷达。天线元件214被配置成接收雷达场的反射或者感测雷达场中的交互。在某些情况下，反射包括来自雷达场内的人类组织的反射，诸如手或者手臂移动。天线元件214可以包括一个或者多个天线或者传感器，诸如辐射传感器阵列，阵列中的数量基于期望的分辨率以及该场是表面还是体积。
雷达发射元件212提供的场可以是三维(3D)体积(例如，半球、立方体、体积的扇形、锥体或者柱体)以感测空中手势，尽管可以代替地使用表面场(例如，投影到人的表面上)。在一些情况下，天线元件214被配置成接收来自两个或者更多个目标(例如，手指、手臂或者人)的雷达场中的交互的反射，并提供复合信号。
图1中示出了示例雷达场106，其中，用户可以用中断雷达场的手臂、身体、手指、多个手指、单手、或者双手(或者，比如触控笔等的设备)执行复杂或者简单的手势。示例手势包括当前触敏显示器可用的许多手势，诸如，滑动、双指捏合、伸展、旋转、轻击等。也支持其它复杂的、或者简单的但却是三维的手势，示例包括许多符号语言手势，诸如美国符号语言(ASL)的符号语言和全世界的其它符号语言。这种符号语言的几个示例是：上下方向的拳头，在ASL中的意思是“是”；张开的食指和中指移动连接到张开的拇指，意思是“否”；平摊的手向上一步，意思是“前进”；平摊而有角度的手上下移动，意思是“下午”；握紧手指并张开拇指移动到张开的手指和张开的拇指处，意思是“出租车”；食指在大致垂直的方向上移动，意思是“向上”；等等。这些仅仅是一些可以被感测到并且可以被映射到特定设备或者应用中的手势，诸如，用于跳过到基于web的无线电应用的另一首歌曲、立体声上播放的光盘上的下一首歌曲、或者计算机显示器或者数码相框上的文件或者相册中的下一页或者图像的前进手势。
返回到图2，基于宽场雷达的手势识别系统102还包括被配置成向远程设备发送反射信号的发送设备，尽管当基于宽场雷达的手势识别系统102集成有与计算设备104时不需要使用该发送设备。当被包括时，可以按照可供远程计算设备使用的格式提供足以使远程计算设备在基于宽场雷达的手势识别系统102或者计算设备104不确定手势的那些情况下确定手势的反射信号。
更详细地，雷达发射元件212可以被配置成发射1GHz至300GHz范围内的微波辐射、3GHz至100GHz范围内的微波辐射、以及诸如57GHz至63GHz等较窄频带以提供雷达场。该范围影响天线元件214接收交互的能力，诸如，以跟随两个或更多个目标的位置达到约2毫米至约25毫米的分辨率。雷达发射元件212可以被配置成：与基于宽场雷达的手势识别系统102的其它实体一起，具有较快的更新速率，该较快的更新速率可以有助于交互的分辨率。
通过选择特定频率，基于宽场雷达的手势识别系统102可以操作以基本穿透衣服，但基本上不穿透人类组织。因此，仍然可以使用基于宽场雷达的手势识别系统102来感测戴着手套或穿长袖衬衫的人，在一些常规技术中，手套或者长袖衬衫可能会干扰到感测手势。
基于宽场雷达的手势识别系统102还可以包括一个或多个系统处理器218和系统介质220(例如，一种或者多种计算机可读存储介质)。系统介质220包括系统管理器222和硬件抽象模块224。系统管理器222可以执行各种操作，包括：基于反射信号来确定手势，将确定的手势映射到与触摸屏108相关联的应用的控制输入的相关联的预配置控制手势上，并且，使收发器216将控制输入发送到远程设备以有效实现对应用的控制(如果是远程的)。这只是可以通过基于宽场雷达的手势识别系统102实现上述控制的一种方式。下面更详细地提供了系统管理器222的操作，作为方法300和500的部分。硬件抽象模块224是下面在图6和图7中描述的替选实施例的部分。
下面更详细地阐述了这些和其它功能与配置、以及图1和图2的实体发挥作用与交互的方式。这些实体可以进一步被划分、组合等。图1的环境100和图2的详细说明示出了能够采用所描述的技术的许多可能的环境和设备中的一些。
示例方法
图3和5描述了实现基于宽场雷达的手势识别的方法。方法300确定信号元素，这些信号元素可以被用于实现从具有信号元素的稍后接收的反射信号中确定手势。方法500基于与手势相关联的信号元素从反射信号确定手势。这些方法被示出为框的集合，这些框指定了所执行的操作但是不必局限于用于通过各个框执行操作所示的顺序或者组合。在以下讨论的部分中，可以参考图1的环境100，如图2中详细描述的，对其的参考仅出于示例。这些技术不限于在一个设备上操作的一个或者多个实体的性能。
更详细地，方法300通过多次重复，与特定手势相关联的信号元素建立数据库。实际上，方法300从正被执行特定手势的反射信号学习。方法300可以针对各种不同类型的雷达系统中的每个这样做，尽管在随后的方法300和方法500阐述的替选实施例中，可以针对每个这种雷达系统开发硬件抽象层以允许与硬件无关的手势管理器。
在302中，提供雷达场。如图2所示，系统管理器222可以使基于宽场雷达的手势识别系统102的雷达发射元件212提供上面所描述的雷达场中的一种(例如，投影或者发射)。
在304中，接收由在雷达场内执行手势的第一人类动作的第一交互引起的第一反射信号集合。该反射信号集合表示第一人类动作与雷达场交互期间的第一时间段。作为第一示例，假设测试人员在雷达场中执行特定的手势。测试人员可以继续执行该特定手势，或者另一个人可以执行特定手势，例如，在不同的角度、具有不同的手尺寸或者不同的手指尺寸、在不同的位置、在不同的定向、和具有不同的衣服、背景、和可能会影响反射信号的其它方面。因此，反射信号包括由正在执行的手势引起的信号元素之外的信号元素。在某些情况下，可以通过提供相对一致的一个物体和运动、以及不一致的其它物体和运动，有意使该方法成为学习过程的一部分。这可以有助于下述学习过程。
在306中，接收由在雷达场内执行手势的第二人类动作的第二交互引起的第二反射信号集合。第二反射信号集合表示第一人类动作或者第二人类动作与雷达场交互期间的第二时间段。继续正在进行的示例，方法300接收第二反射信号集合，尽管如图3中的虚线箭头所示，该技术可以执行与许多不同的人的操作、相同手势的重复等，以更好地理解与做出的手势相关的信号元素。
在308中，分析第一反射信号集合和第二反射信号集合以确定这两个反射信号集合共同的信号元素。更详细地，分析多个反射信号确定出这两个反射信号共同的信号元素。这可以通过将反射信号分解成许多信号元素并且确定哪个信号元素对应于相同手势的第一交互和第二交互来实现。这在一定程度上被简化了，因为可能需要执行许多手势的重复来精确地确定与手势对应的信号元素，例如50次、100次、甚或数百次重复。
作为该过程的一部分，众多信号元素可能与物体和移动、甚或噪音相关联，而与手势本身无关。如果发现这些信号元素与手势无关，则在一定程度上可以忽略这些众多信号元素。
在某些情况下，这种分析不是基于人类动作的追踪点或元素，诸如，确定特定点和那些点的定向，重构动作，然后确定身体部位如何移动或者更改以确定正在执行的手势。
在310中，将信号元素与手势相关联，以有效使得稍后接收到的由不同(或相同)人类动作的交互引起的反射信号能够与手势相关联。在312中，为与执行的手势相关联存储这些确定的信号元素。
作为一种说明，考虑图4，其示出了正在执行的三种不同的手势，一次执行一个手势，重复多次，以确定与手势相关联的信号元素。所示的手势包括手摆动手势402、拳头摇动手势404(美国符号语言(ASL)手势“是”)和捏合手指手势406。
在第一种情况下，多次执行手摆动手势402，由天线元件214接收多个反射信号408。天线元件214将反射信号传递给手势管理器206，手势管理器206执行针对操作308描述的分析。在第二种情况下，多次执行拳头摇动手势404，由天线元件214接收多个反射信号410。天线元件214再将反射信号传递给手势管理器206，手势管理器206确定拳头摇动手势404的信号元素。同样，在第三种情况下，在雷达场内多次执行捏合手指手势406，此时由天线元件214接收反射信号，天线元件214将反射信号传递给手势管理器206。然后，手势管理器206确定捏合手指手势406的信号元素。这些记录的信号元素中的每一个稍后可以用于确定在现实生活中执行的手势，而非作为确定手势本身的部分，尽管可以基于用户在其正常生活过程中执行的手势识别的准确性或者不准确性来设想实现信号元素的持续改进的反馈回路。
可选地，在314中，可以基于与其它手势相关联的其它信号元素来细化针对手势确定的信号元素。例如，考虑对图4所示的三个手势中的每个手势执行方法300的情况，产生三个不同的确定的信号元素。这些不同的信号元素可以用来相互细化。假定针对拳头摇动手势404确定了一个信号元素集合，并且，针对捏合手指手势406确定了第二信号元素集合。在314中，可以对第一信号元素集合和第二信号元素集合进行分析和比较，并且，基于此，可以减少或增加各种信号元素的权重。因此，如果相似，则可以减轻信号元素的权重或去除信号元素。如果不相似，则增加信号元素的权重。如果是唯一的，则也增加信号元素的权重。进一步地，针对第三手势——手摆动手势402——确定的信号元素也可以用于细化针对其它手势的任一个或者两个信号元素。
方法500通过基于与手势相关联的信号元素的反射信号来确定手势。因此，在方法300中确定的信号元素用于确定方法500中的手势。
在502中，提供雷达场，诸如上述宽雷达场。作为示例，考虑图2，在该图中，基于宽场雷达的手势识别系统102包括雷达发射元件212、天线元件214和系统管理器222。雷达发射元件212提供宽雷达场。
在504中，接收反射信号。如图2所示，天线元件214基于与所提供的雷达场的一些交互来接收反射信号。将该反射信号传递到系统管理器222进行分析。
在506中，确定来自反射信号内的信号元素。这可以通过上面和下面描述的许多不同的方式来执行。可以使用与类型无关的和特定于类型的手势管理器两者。
在508中，将接收的反射信号的信号元素同已知手势的已知信号元素进行比较。这些已知信号元素是在上述方法300中执行的过程提供的信号元素。再次考虑图4所示的示例，在该示例中，示出了摆动手势402。假设，在该示例中，用户正试图通过摆动手势与具有基于宽场雷达的手势识别系统的计算手表进行交互。用户在雷达场内执行手势，该手势在计算手表的雷达系统所提供的雷达场的数厘米甚或一米或者二米之内并且使反射信号被天线元件接收。系统管理器222分析该反射信号，这包括对已知与各种手势相关联的信号元素的访问。系统管理器222对这些进行比较并且确定反射信号的信号元素与已知信号元素相对应。
在510中，基于信号元素和对应的手势的已知信号元素之间的对应关系来确定所做出的手势。继续正在进行的示例，系统管理器222确定用户已经执行了摆动手势。
在512中，将手势传递到应用或者操作系统。该应用或操作系统可以是作为手势传递目标的实体的主动操作系统，可以基于本领域针对被动手势已知的方式。结束正在进行的示例，应用或操作系统接收摆动手势并且相应地做出响应。作为传递手势的部分或者在传递手势之前，手势管理器206可以确定手势是针对哪个应用或者设备。这样做可以基于身份特定手势、用户当前正在进行交互的当前设备、和/或基于通过其用户与应用交互的控件。可以通过界面的检查(例如，视觉控件)、已发布的API等来确定控件。
可选地，在514中，提供反馈。该反馈可以响应于手势识别失败或成功。假设在本示例中用户执行摆动手势并且该手势未被识别为摆动手势。再假设用户指示了这点或者以其它方式确定未正确识别出手势，诸如用户继续执行手势直到识别到摆动手势。系统管理器222将该失败传递给手势管理器206或者与手势管理器206相关联的实体，从而可以改变或者改进已知的信号元素集合以识别摆动手势。同样，可以提供成功的手势识别来改进手势管理器206的识别。
在一些情况下，方法300或者方法500在远离正受控制的设备的设备上操作。在这种情况下，远程设备包括图1和图2的计算设备104的实体，并且通过一种或者多种通信方式传递手势，诸如，通过收发器和/或网络接口(例如，网络接口208和收发器216)无线地传递手势。该远程设备不需要计算设备104的所有元件：基于宽场雷达的手势识别系统102可以传递足够具有手势管理器206的另一设备确定并且使用手势的反射信号。
可以重复方法300和方法500的操作，诸如，通过确定多个其它应用和可以用来控制该多个其它应用的其它控件。然后，方法500可以指示各种不同的控件来控制与应用或者行动者相关联的各种应用。在某些情况下，这些技术可以为不同的应用确定或分配唯一的和/或复杂且三维的控件，从而允许用户控制大量应用，而无需选择在它们之间切换控件。因此，行动者可以分配特定的手势来控制计算设备104上的一个软件应用，并且分配另一个特定的手势来控制另一个软件应用，并且，分配又一个特定的手势来控制恒温器或者立体声。该手势可以由多个不同的人使用，或者一旦确定了行动者的身份，可以将该手势与该特定的行动者相关联。
前面的讨论描述了与基于宽场雷达的手势识别有关的方法。可以在硬件(例如，固定逻辑电路)、固件、软件、手动处理或者其任何组合中实施这些方法的各个方面。这些技术可以体现在图1、图2、图4、图6和图8(下面在图8中描述计算系统800)所示的实体中的一个或者多个实体上，这些实体可以进一步划分、组合等等。因此，这些图示出了能够使用所描述的技术的许多可能的系统或者装置中的一些。这些图的实体通常表示软件、固件、硬件、整个设备或网络、或者其组合。
示例替选硬件抽象模块
如上文部分地提到的，手势管理器206可以基于使用的雷达系统来确定每个手势的信号元素。因此，如果在雷达场之间、在针对方法300执行的和稍后的由反射信号存在实质差异的其它雷达系统或者类似雷达系统提供的之间存在实质差异，那么手势识别的准确性可能会受到影响。然而，这种影响可以通过调整针对使用的雷达系统的每个手势的每个信号元素集合来抵消。在替选实施例中，为每个不同的雷达系统建立硬件抽象层，在很大程度上避免了针对每个手势需要不同的手势管理器或者不同的信号元素，尽管这些硬件抽象层分别是按照类似于方法300的方式来训练的。
举例说明，考虑图6，其图示出了特定类型的基于雷达的手势识别系统602和特定于类型的手势管理器604。它们是手势管理器和上述各种类型的雷达系统的图示，其中，雷达系统被用作学习过程的一部分以确定该雷达类型专用的信号元素。可替代地，通过这些雷达系统的一些任意数“N”，如606-N所示，考虑各种特定类型的基于雷达的手势识别系统606-1和606-2。仍然参见与相应类型的雷达系统匹配的特定类型的硬件抽象模块224，这些抽象模块如608-1、608-2和608-N所示。如虚线框所示，特定类型的手势管理器604是其对雷达系统类型的依赖性的图示，且示出了对应的组件606-1、608-1、以及与类型无关的手势管理器610的一部分。然而，注意，与类型无关的手势管理器610在很大程度上与雷达系统的类型无关，如将在图7中所述。
还考虑图7，在这种情况下，多次执行各种手势402、404和406，并且分别通过特定类型的基于雷达的手势识别系统606-1(并且每个系统通过系统“N”)来接收反射信号408、410和412。注意，对于这些特定类型的系统中的每一个，将不同的反射信号区别表示为参考信号1、参考信号2、和参考信号N。类似于方法300，针对特定手势确定信号元素，尽管在这种情况下通过抽象模块进行该确定然后提供与硬件无关的反射信号，如图7所示。然后，由与类型无关的手势管理器610来接收这些与硬件无关的反射信号，该手势管理器610再确定在方法500中执行的手势。
更详细地，其中可以开发特定于类型的硬件抽象模块608的技术如下所示。这些技术也可以用于协助开发特定于类型的手势管理器604的信号元素。因此，这些技术可以用作方法300的实施例。在一些情况下，硬件抽象模块608可以在单频区、步进FM、线性FM、冲击和啁啾雷达系统中操作。如上文部分地提到的，单频区雷达架构可以是具有单频区或者步进频率的60GHz连续波。步进FM雷达架构可以是步进频率的96GHz连续波。线性FM可以是60GHz的调频连续波。冲击雷达架构可以是60GHz的脉冲。而啁啾雷达架构具有啁啾雷达场。注意，雷达架构产生等效的数据产品，时域和频域可互换。
示例计算系统
图8示出了可以被实施为参照先前的图1至图7描述的用于实施基于宽场雷达的手势识别的任何类型的客户端、服务器和/或计算设备的示例计算系统800的各种组件。
计算系统800包括能够进行设备数据804(例如，接收的数据、正在接收的数据、计划进行广播的数据、数据的数据包等)的有线和/或无线通信的通信设备802。设备数据804或者其它设备内容可以包括设备的配置设置、存储在设备上的媒体内容、和/或与设备的用户相关联的信息(例如，执行手势的行动者的身份)。存储在计算系统800上的媒体内容可以包括任何类型的音频、视频和/或图像数据。计算系统800包括可以经由其接收任何类型的数据、媒体内容和/或输入的一个或多个数据输入806，诸如，人类话语、与雷达场的交互、用户可选择的输入(显式或隐式)、消息、音乐、电视媒体内容、录制的视频内容、以及从任何内容和/或数据源接收的任何其它类型的音频、视频和/或图像数据。
计算系统800还包括可以被实施为串行和/或并行接口、无线接口、任何类型的网络接口、调制解调器、以及任何其它类型的通信接口中的任何一个或多个的通信接口808。通信接口808提供计算系统800与通信网络之间的连接和/或通信链路，其它电子设备、计算设备和通信设备通过该连接和/或通信链路与计算系统800传送数据。
计算系统800包括一个或多个处理器810(例如，微处理器、控制器等中的任何一种)，该处理器处理各种计算机可执行指令以控制计算系统800的操作并且实现用于基于宽场雷达的手势识别的技术或者可以体现基于宽场雷达的手势识别的技术。替选地或另外地，计算系统800可以实施有硬件、固件或者固定逻辑电路中的任何一个或者其组合，结合通常标识为812的处理与控制电路来实施该固定逻辑电路。虽然未示出，但是计算系统800可以包括将设备内的各种组件耦合在一起的系统总线或数据传输系统。系统总线可以包括不同总线结构中的任何一个或者其组合，诸如，存储器总线或者存储器控制器、外围总线、通用串行总线、和/或利用各种总线架构中的任何一种的处理器或者本地总线。
计算系统800还包括计算机可读介质814，诸如，实现永久性和/或非暂时性数据存储(即，与简单的信号传输相反)的一个或者多个存储器设备，这种存储器设备的示例包括随机存取存储器(RAM)、非易失性存储器(例如，只读存储器(ROM)、闪速存储器、EPROM、EEPROM等中的任何一种或者多种)和磁盘存储设备。磁盘存储设备可以被实施为任何类型的磁性或光学存储设备，诸如，硬盘驱动器、可记录和/或可重写的光盘(CD)、任何类型的数字通用盘(DVD)等。计算系统800还可以包括大容量存储介质设备(存储介质)816。
计算机可读介质814提供数据存储机制以存储设备数据804、以及各种设备应用818和与计算系统800的各个操作方面有关的任何其它类型的信息和/或数据。例如，可以利用计算机可读介质814将操作系统820保存为计算机应用，并且使其在处理器810上执行。设备应用818可以包括设备管理器，诸如，任何形式的控制应用、软件应用、信号处理与控制模块、特定设备原生的代码、特定设备的硬件抽象层等等。设备应用818还包括系统组件、引擎或者管理器以实施基于宽场雷达的手势识别，诸如，手势管理器206、系统管理器222、以及在手势管理器206是与类型无关的情况下硬件抽象模块224。
结论
尽管已经以特征和/或方法特有的语言描述了使用基于宽场雷达的手势识别的技术和包括基于宽场雷达的手势识别的装置，但是，要理解，随附权利要求书的主题不必局限于所描述的特定特征或者方法。相反，具体特征和方法是作为基于宽场雷达的手势识别的示例实施方式而公开的。
Claims (20)
1.一种计算机实现的方法，包括：
由雷达系统的发射机提供雷达场；
在所述雷达系统的接收器处，接收由在所述雷达场内执行手势的第一人类动作的第一交互引起的第一反射信号集合，所述第一反射信号集合表示所述第一人类动作与所述雷达场交互期间的第一时间段；
在所述雷达系统的所述接收器处，接收由在所述雷达场内执行所述手势的第二人类动作的第二交互引起的第二反射信号集合，所述第二反射信号集合表示所述第二人类动作与所述雷达场交互期间的第二时间段；
分析所述第一反射信号集合和所述第二反射信号集合以确定所述第一反射信号集合和所述第二反射信号集合共同的信号元素；以及
将所述信号元素与所述手势相关联，以有效使得稍后接收到的由不同人类动作的交互引起的反射信号能够与所述手势相关联。
2.根据权利要求1所述的计算机实现的方法，其中，分析所述第一反射信号集合和所述第二反射信号集合以确定所述第一反射信号集合和所述第二反射信号集合共同的信号元素，包括：将所述第一反射信号集合和所述第二反射信号集合分解为多个信号元素并且确定所述信号元素中的哪些与执行所述手势的所述第一人类动作的所述第一交互和执行所述手势的所述第二人类动作的所述第二交互两者都对应。
3.根据权利要求1所述的计算机实现的方法，其中，分析所述第一反射信号集合和所述第二反射信号集合不基于所述第一人类动作或者所述第二人类动作的追踪点或元素。
4.根据权利要求1所述的计算机实现的方法，其中，所述雷达场是完全连续的场。
5.根据权利要求1所述的计算机实现的方法，其中，所述雷达系统不是相控天线阵列。
6.根据权利要求1所述的计算机实现的方法，进一步包括：接收由在所述雷达场内执行所述手势的所述第一人类动作、所述第二人类动作或者其它人类动作的多个其它交互引起的多个其它反射信号集合，以及，其中，分析所述第一反射信号集合和所述第二反射信号集合是分析所述第一反射信号集合、所述第二反射信号集合和所述多个其它反射信号集合。
7.根据权利要求6所述的计算机实现的方法，其中，接收到所述第一反射信号集合、所述第二反射信号集合和所述其它反射信号集合的所述雷达场是由相同雷达系统的相同发射机提供的相同雷达场。
8.根据权利要求1所述的计算机实现的方法，进一步包括：基于与其它手势相关联的其它信号元素来细化与所述手势相关联的信号元素。
9.一种计算机实现的方法，包括：
提供宽雷达场；
接收在所述宽雷达场内做出的手势的反射信号；
确定所述反射信号的信号元素；
将所述反射信号的所述信号元素和与已知手势相关联的已知信号元素相比较；
基于所述反射信号的所述信号元素对应于所述已知信号元素，确定在所述宽雷达场中做出的所述手势是所述已知手势中的一种已知手势；以及
将所述已知手势中的所述一种已知手势传递给应用或操作系统。
10.根据权利要求9所述的方法，进一步包括：
接收第二手势的第二反射信号；
确定所述第二反射信号的第二信号元素；
将所述第二反射信号的所述第二信号元素和与所述已知手势相关联的所述已知信号元素相比较；
基于所述第二信号元素不对应于所述已知信号元素，确定未能正确识别所述第二手势；以及
基于所确定的第二信号元素更改所述手势的所述已知信号元素。
11.根据权利要求9所述的方法，进一步包括：
从用户接收来自在所述宽雷达场中做出的独特手势的多次重复的多个反射信号，所述独特手势不是所述已知手势中的一种已知手势；
确定所述反射信号的信号元素，所述信号元素足以确定所述用户的稍后接收到的手势的稍后接收到的反射信号与所述独特手势相匹配；以及
将所确定的信号元素与所述独特手势相关联。
12.根据权利要求11所述的方法，进一步包括：将所述独特手势与应用的控制相关联，所述关联响应于所述用户选择的所述控件和所述应用的分配。
13.根据权利要求9所述的方法，其中，确定所述反射信号的所述信号元素是数字地分解所述反射信号，并每次一个地分析所分解的信号中的每个。
14.根据权利要求9所述的方法，其中，所述宽雷达场包括来自不同方向的多个雷达场或具有不同频率或相位的多个雷达场。
15.根据权利要求9所述的方法，其中，所述宽雷达场是连续波雷达场或连续的雷达场。
16.根据权利要求15所述的方法，其中,所述连续波雷达场是单频区、线性调频(FM)或者步进FM场。
17.根据权利要求9所述的方法，其中，所述宽雷达场是脉冲波雷达场、冲击雷达场或者啁啾雷达场。
18.一种装置，包括：
基于宽场雷达的手势识别系统，所述手势识别系统包括：
雷达发射元件，所述雷达发射元件被配置成提供宽雷达场；以及
天线元件，所述天线元件被配置成：
接收来自在所述宽雷达场内的人类组织的反射信号；以及
传递所接收的反射信号；以及
一个或多个计算机可读存储介质，具有存储在其上的指令，所述指令响应于由一个或多个计算机处理器执行而执行操作，所述操作包括：
使所述基于宽场雷达的手势识别系统利用所述雷达发射元件提供雷达场；
使所述基于宽场雷达的手势识别系统利用所述天线元件接收在所述宽雷达场中的交互的反射信号；
基于所接收到的反射信号确定信号元素；
将所接收到的反射信号的所述信号元素和与已知手势相关联的已知信号元素相比较；
基于所述信号元素对应于所述已知信号元素，确定在所述宽雷达场中做出的所述交互对应于所述已知手势中的一种已知手势；以及
将所述已知手势中的所述一种已知手势传递给应用或操作系统。
19.根据权利要求18所述的装置，其中，所述一个或多个计算机可读存储介质进一步被配置成执行操作，所述操作包括：
接收来自在所述宽雷达场中做出的复杂手势的多次重复的多个反射信号，所述复杂手势不是所述已知手势中的一种已知手势；
确定所述多个反射信号的信号元素，所述信号元素足以确定稍后接收到的手势的稍后接收到的反射信号与所述复杂手势相匹配；以及
将所确定的信号元素与所述复杂手势相关联。
20.根据权利要求18所述的装置，其中，确定所述反射信号的所述信号元素的所述操作数字地分解所述反射信号，并每次一个地分析所分解的信号中的每个。
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| US10664061B2 (en) | 2020-05-26 |
| US20190033981A1 (en) | 2019-01-31 |
| US10139916B2 (en) | 2018-11-27 |
| EP3289434A1 (en) | 2018-03-07 |
| CN111880650A (zh) | 2020-11-03 |
| US20160320852A1 (en) | 2016-11-03 |
| CN107430443B (zh) | 2020-07-10 |
| WO2016176574A1 (en) | 2016-11-03 |
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