CN113423341A - 用于可穿戴电极传感器系统的自动校准的方法和装置 - Google Patents
用于可穿戴电极传感器系统的自动校准的方法和装置 Download PDFInfo
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Abstract
用于校准被配置为穿戴在用户的手腕或前臂上的可穿戴设备的位置和/或取向的方法和系统。该方法包括:使用布置在可穿戴设备上的多个传感器感测来自用户的多个神经肌肉信号;提供该多个神经肌肉信号和/或从该多个神经肌肉信号导出的信号作为一个或更多个训练过的自动校准模型的输入;至少部分地基于该一个或更多个训练过的自动校准模型的输出,确定可穿戴设备在用户上的当前位置和/或取向;以及至少部分地基于可穿戴设备在用户上的当前位置和/或取向以及多个神经肌肉信号来生成控制信号。
Description
相关申请
本申请根据35 U.S.C.§119(e)要求于2018年11月27日提交的、标题为“METHODSAND APPARATUS FOR AUTOCALIBRATION OF A WEARABLE SURFACE EMG SENSOR SYSTEM”的美国临时申请62/771,957的优先权,其全部内容通过引用并入本文。
背景
一些检测用户生成的信号的智能可穿戴设备可以被用户穿戴在身体上的各种取向和位置。针对这些类型的设备的一个常见问题是,如果可穿戴设备被穿戴在设备未针对性能进行优化的位置或取向上,则信号检测可能会受到负面影响。
概述
一些实施例涉及一种用于校准被配置为穿戴在用户的手腕或前臂上的可穿戴设备的位置和/或取向的系统。所述系统包括布置在所述可穿戴设备上的多个传感器和至少一个计算机处理器,其中所述多个传感器被配置为连续感测来自所述用户的多个神经肌肉信号。所述至少一个计算机处理器被编程为:提供所述多个神经肌肉信号和/或从所述多个神经肌肉信号导出的信号作为一个或更多个训练过的自动校准模型的输入;至少部分地基于所述一个或更多个训练过的自动校准模型的输出,确定所述可穿戴设备在所述用户上的当前位置和/或取向;以及至少部分地基于所述可穿戴设备在所述用户上的当前位置和/或取向以及所述多个神经肌肉信号来生成控制信号。
在一方面,至少一个计算机处理器被编程为确定可穿戴设备在用户上的当前位置和/或取向,而无需用户在感测多个神经肌肉信号期间执行特定姿势或手势。
在另一方面,至少一个计算机处理器被编程为在将处理过的神经肌肉信号提供给一个或更多个自动校准模型之前处理感测到的多个神经肌肉信号。
在另一方面,至少一个被编程的处理器被进一步编程为:至少部分地基于可穿戴设备的当前位置和/或取向以及多个神经肌肉信号生成校准的神经肌肉信号,并且生成控制信号包括至少部分地基于校准的神经肌肉信号生成控制信号。
在另一方面,至少一个被编程的处理器被进一步编程为:至少部分地基于可穿戴设备的当前位置和/或取向来选择或修改一个或更多个推理模型;以及提供多个神经肌肉信号作为所选择或修改的一个或更多个推理模型的输入,并且其中生成控制信号还至少部分地基于所选择或修改的一个或更多个推理模型的输出。
在另一方面,用于确定可穿戴设备在用户上的当前位置和/或取向的一个或更多个自动校准模型包括神经网络。
在另一方面,神经网络是LSTM神经网络。
在另一方面,LSTM神经网络包括至少一个池化层。
在另一方面,至少一个池化层包括最大池化层。
在另一方面,至少一个池化层提供±1个传感器位置的旋转不变性。
在另一方面,一个或更多个自动校准模型被训练以基于从多个用户获取的神经肌肉信号识别可穿戴设备的取向和/或定位。
在另一方面,当多个用户中的每一个用户执行多个手势和/或姿势、同时可穿戴设备以多种不同配置被定向和/或定位在该用户的前臂和/或手腕上时,获取从多个用户获取的神经肌肉信号。
在另一方面,多个神经肌肉传感器包括多个肌电图(EMG)传感器。
在另一方面,至少一个计算机处理器还被编程为:识别可穿戴设备的至少一部分已经在用户的手腕或前臂上移动;以及响应于识别可穿戴设备的至少一部分已经在用户的手腕或前臂上移动,确定可穿戴设备在用户上的当前位置和/或取向。
在另一方面,识别可穿戴设备的至少一部分已经在用户的手腕或前臂上移动包括确定可穿戴设备已经围绕用户的手腕或前臂旋转和/或沿用户的手腕或前臂平移。
在另一方面,识别可穿戴设备的至少一部分已经在用户的手腕或前臂上移动包括检测感测到的多个神经肌肉信号中的一个或更多个移动伪影(artifacts)。
在另一方面,系统还包括至少一个惯性测量单元(IMU)传感器,并且其中识别可穿戴设备的至少一部分已经在用户的手腕或前臂上移动是至少部分地基于由该至少一个IMU传感器感测到的至少一个信号。
在另一方面,确定可穿戴设备的当前位置和/或取向包括确定可穿戴设备相对于可穿戴设备的虚拟参考取向的旋转。
在另一方面,控制信号是控制虚拟现实系统或增强现实系统的一个或更多个操作的控制信号、或控制虚拟现实系统或增强现实系统内的一个或更多个操作的控制信号。
一些实施例涉及一种用于校准可穿戴手环在用户上的位置和/或取向的方法。所述方法包括:使用布置在所述可穿戴设备上的多个传感器感测来自所述用户的多个神经肌肉信号;提供所述多个神经肌肉信号和/或从所述多个神经肌肉信号导出的信号作为一个或更多个训练过的自动校准模型的输入;至少部分地基于所述一个或更多个训练过的自动校准模型的输出,确定所述可穿戴设备在所述用户上的当前位置和/或取向;以及至少部分地基于所述可穿戴设备在所述用户上的当前位置和/或取向以及所述多个神经肌肉信号来生成控制信号。
在一方面,确定可穿戴设备当前位置和/或取向包括确定可穿戴设备当前位置和/或取向而无需用户在感测多个神经肌肉信号期间执行特定姿势或手势。
在另一方面,该方法还包括在将处理过的神经肌肉信号提供给一个或更多个自动校准模型之前处理感测到的多个神经肌肉信号。
在另一方面,该方法还包括:至少部分地基于可穿戴设备的当前位置和/或取向以及多个神经肌肉信号生成校准的神经肌肉信号,并且其中生成控制信号包括至少部分地基于校准的神经肌肉信号生成控制信号。
在另一方面,该方法还包括:至少部分地基于可穿戴设备的当前位置和/或取向来选择或修改一个或更多个推理模型;以及提供多个神经肌肉信号作为所选择或修改的一个或更多个推理模型的输入,其中生成控制信号还至少部分地基于所选择或修改的一个或更多个推理模型的输出。
在另一方面,用于确定可穿戴设备在用户上的当前位置和/或取向的一个或更多个自动校准模型包括神经网络。
在另一方面,一个或更多个自动校准模型被训练以基于从多个用户获取的神经肌肉信号识别可穿戴设备的取向和/或定位。
在另一方面,当多个用户中的每一个用户执行多个手势和/或姿势、同时可穿戴设备以多种不同配置被定向和/或定位在该用户的前臂和/或手腕上时,获取从多个用户获取的神经肌肉信号。
在另一方面,确定可穿戴设备的当前位置和/或取向包括确定可穿戴设备相对于可穿戴设备的虚拟参考取向的旋转。
在另一方面,该方法还包括:识别可穿戴设备的至少一部分已经在用户的手腕或前臂上移动;以及响应于识别可穿戴设备的至少一部分已经在用户的手腕或前臂上移动,确定可穿戴设备在用户上的当前位置和/或取向。
在另一方面,识别可穿戴设备的至少一部分已经在用户的手腕或前臂上移动包括确定可穿戴设备已经围绕用户的手腕或前臂旋转和/或沿用户的手腕或前臂平移。
在另一方面,识别可穿戴设备的至少一部分已经在用户的手腕或前臂上移动包括检测感测到的多个神经肌肉信号中的一个或更多个移动伪影。
在另一方面,该方法还包括识别可穿戴设备的至少一部分已经在用户的手腕或前臂上移动是至少部分地基于由至少一个惯性测量单元(IMU)传感器感测到的至少一个信号。
一些实施例涉及一种用于训练用于可穿戴设备的自动校准的推理模型的方法。所述方法包括:当多个用户中的每一个用户穿戴所述可穿戴设备并执行多个手势和/或姿势时,接收从所述多个用户感测到的神经肌肉信号,其中对于所述多个手势和/或姿势中的每一个,所述可穿戴设备在所述用户上的取向或位置不同;在感测相应的神经肌肉信号期间,基于所述可穿戴设备的取向或位置,为所述多个手势和/或姿势中的每一个标记接收到的神经肌肉信号;以及至少部分基于标记的神经肌肉信号训练一个或更多个自动校准模型。
在一方面,该方法还包括:从接收的神经肌肉信号中提取多个模板,其中该多个模板中的每一个对应多个手势和/或姿势之一;以及至少基于提取的多个模板生成校准的神经肌肉信号,并且其中训练一个或更多个自动校准模型包括至少部分地基于校准的神经肌肉信号训练一个或更多个自动校准模型。
在另一方面,该方法还包括:使用不同取向的多个模拟来预测可穿戴设备的多个旋转偏移。
在另一方面,一个或更多个自动校准模型包括神经网络。
在另一方面,神经网络是LSTM神经网络。
一些实施例涉及一种用多个处理器可执行指令编码的非暂时性计算机可读存储介质,这些指令在由一个或更多个计算机处理器执行时执行一个或更多个前述方法。
应当理解,上述概念和下面更详细讨论的附加概念的所有组合(假设这些概念不是相互矛盾的)都被认为是本文公开的发明主题的一部分。特别地,出现在本公开末尾的要求保护的主题的所有组合都被认为是本文公开的发明主题的一部分。
附图简述
将参考以下附图描述本技术的各种非限制性实施例。应当理解,这些图不一定是按比例绘制的。
图1示出了根据本文描述的技术的一些实施例的可穿戴系统,该可穿戴系统具有围绕手环(band)周向布置的十六个EMG传感器,手环被配置为围绕用户的下臂或手腕穿戴;
图2A和图2B示意性地示出了根据本文描述的技术的一些实施例的分别包括可穿戴部分和加密狗部分的基于计算机的系统;
图3是示出根据一些实施例的由训练过的自动校准模型生成的输出的示例分布的图。输出的分布是跨数据集生成的,该数据集具有从不同用户收集的数据;
图4示出了根据一些实施例的从训练过的自动校准模型输出的预测。预测值是跨时间平均的,这导致模型预测具有更大的置信度值;
图5示出了根据一些实施例的训练过的自动校准模型的准确度图。准确度以接受者操作特征曲线下面积(AUC)表示;
图6示出了根据一些实施例的用于可穿戴设备的自动校准传感器的、在基线模型与增强模型之间的相关性的图;
图7示出了根据一些实施例的用设置为±1、±2和±3的电极不变性训练过的增强推理模型与基线模型的相关性的图;
图8示意性地示出了可穿戴设备上的单个EMG电极在可穿戴设备已经旋转的第一位置和第二位置处的能量图的可视化;
图9示出了根据一些实施例的基于从多个用户记录的神经肌肉信号对自动校准模型进行离线训练的过程的流程图;以及
图10示出了根据一些实施例的使用训练过的自动校准模型来校准可穿戴设备上的传感器的位置和/或取向的过程的流程图。
详细描述
本文公开的方法和装置涉及传感器系统的自动校准,使得当用户身体部位以各种取向和/或定位穿戴传感器时,系统表现良好。在本文描述的技术的一些实施例中,传感器信号可用于预测关于用户身体的一个或更多个部分(例如,腿、手臂和/或手)的位置和/或运动的信息,用户身体可表示为带有连接刚体系统的多个节段(segment)的接头的多节段铰接式刚体系统。例如,在手部运动的情况下,由放置在用户身体位置(例如,用户的手臂和/或手腕)处的可穿戴神经肌肉传感器感测到的信号可以作为输入提供给一个或更多个推理模型,该推理模型经过训练以例如在用户进行一种或更多种手部运动时,预测与手相关联的基于计算机的肌肉骨骼表示中与多个刚性节段相关联的位置(例如,绝对位置、相对位置、取向)和力的估计。和与手相关联的肌肉骨骼表示的节段相关联的位置信息和力信息的组合在本文中可以被称为肌肉骨骼表示的“手状态(handstate)”。当用户进行不同的运动时,训练过的推理模型可以将由可穿戴神经肌肉传感器感测到的神经肌肉信号解释为用于更新肌肉骨骼表示的位置和力估计(手状态信息)。
在某些实施例中,从多个用户收集处理过的神经肌肉信号和/或神经肌肉信号模式(例如,EMG信号和/或EMG信号模式),并且处理过的信号和/或信号模式用于生成通用模型以预测包含EMG传感器的臂环的取向和/或定位。例如,如果用户在有或没有系统监督的情况下执行不同的手状态配置(例如,包括在四个主要方向上的各种捏合和手腕运动的手势或姿势),当臂环被定位在前臂上和/或以各种方式定向时,可以获得EMG信号的时空模式,并且这些信号可用于训练广义推理模型,以基于检测到的EMG信号预测用户的手势。在某些实施例中,本文公开的系统和方法包括识别“虚拟”参考电极并将臂环的特定取向映射到与电极和“虚拟”参考电极相关联的偏移值。
如本文所用,术语“手势(gesture)”可以指一个或更多个身体部位的静态或动态配置,包括一个或更多个身体部位的位置以及与该配置相关联的力。例如,手势可以包括不连续的手势,例如将手掌放在或向下压在固体表面上,或抓住球,或将两根手指捏在一起(例如,形成姿势);或连续的手势,如来回挥动手指、抓球和扔球、向一个方向旋转手腕等;或不连续和连续手势的组合。手势可以包括其他人可能察觉不到的隐蔽手势,例如通过同时收缩相对的肌肉或使用肌肉下激活或“脱离流形(off-manifold)”激活来轻微拉紧关节。在训练推理模型时,可以使用被配置为提示用户执行手势的应用来定义手势,或者替代地,手势可以由用户任意定义。用户执行的手势可以包括符号手势(例如,映射到其他手势、交互或命令的手势,例如,基于指定映射的手势词表)。在某些情况下,手势和手臂姿势可能是象征性的,并且用于根据文化标准进行交流。
系统的自动校准之后,在各种实施例中,可以从记录和/或检测到的信号和/或基于信号的信息中识别用户的多个肌肉激活状态,以在用户环境中的对象被配置为接收控制信号时,实现对这些对象的改进的选择和/或控制。对对象的控制可以直接从神经肌肉活动设备执行,也可以通过另一个系统间接执行,例如增强现实(AR)系统或任何扩展或交叉现实系统(XR系统或环境),包括但不限于混合现实(MR)、虚拟现实(VR)等。
下面阐述的描述旨在描述主题技术的各种配置,而不是旨在表示可以在其中实践本主题技术的唯一配置。附图并入本文并构成详细描述的一部分。详细描述包括用于提供对主题技术的透彻理解的特定细节。然而,将清楚和明显的是,主题技术不限于本文阐述的具体细节并且可以在没有这些具体细节的情况下实践。在某些情况下,以框图形式显示众所周知的结构和组件,以避免混淆主题技术的概念。
术语“计算机”、“处理器”、“计算机处理器”以及“计算设备”等应被广泛地解释为涵盖具有数据处理能力的任何类型的电子设备,包括作为非限制性示例的数字信号处理器(DSP)、微控制器、现场可编程门阵列(FPGA)、专用集成电路(ASIC)或包括一个或更多个任何类型的处理器的任意其他电子计算设备,或其任意组合。
如本文所用,短语“例如”、“诸如”以及“举例说”及其变体描述了目前公开的主题的非限制性实施例。说明书中对“一种情况”、“一些情况”以及“其他情况”或其变体的引用意味着结合实施例描述的特定特征、结构或特性包括在目前公开的主题的至少一个实施例中。因此,短语“一种情况”、“一些情况”“其他情况”或其变体的出现不一定指代相同的实施例。
应当理解,除非另外具体说明,为了清楚起见,在单独实施例的背景下描述的当前公开的主题的某些特征也可以在单个实施例中组合提供。相反,为了简洁起见,在单个实施例的背景下描述的当前公开的主题的各种特征也可以单独提供或以任何合适的子组合提供。
图1示出了手环系统,该手环系统具有围绕弹性手环102周向布置的十六个EMG传感器,手环102被配置为围绕用户的下臂穿戴。例如,图1示出了围绕弹性手环102周向布置(例如,对称地间隔开)的EMG传感器101。应当理解,可以使用任何合适数量的神经肌肉传感器,并且所使用的神经肌肉传感器的数量和布置可以取决于可穿戴设备被用于的特定应用。例如,可穿戴臂环或腕环可用于生成用于控制增强现实系统、机器人、控制车辆、滚动文本、控制虚拟化身或任何其他合适的控制任务的控制信息。此外,手环系统可以被配置为穿戴在用户身体的其他部位,例如他们的大腿或小腿。
在一些实施例中,传感器101仅包括多个神经肌肉或肌肉传感器或电极(例如,EMG电极/传感器、MMG电极/传感器、SMG电极/传感器等)。在其他实施例中,传感器101包括多个神经肌肉传感器和被配置为连续记录多个辅助信号的至少一个“辅助”传感器。辅助传感器的示例包括但不限于其他传感器,例如惯性测量单元(IMU)传感器、麦克风、成像设备(例如,相机)、与辐射生成设备一起使用的基于辐射的传感器(例如,激光扫描设备),或其他类型的传感器,如心率监测器。
在一些实施例中,一个或更多个传感器的输出可以可选地使用硬件信号处理电路进行处理(例如,以执行放大、滤波和/或整流)。在其他实施例中,传感器输出的至少一些信号处理可以在软件中执行。因此,由传感器感测的信号的信号处理可以在硬件、软件中或通过硬件和软件的任意合适的组合来执行,因为本文描述的技术的方面并不限于本方面。下面结合图2A和图2B更详细地讨论用于处理来自传感器101的记录数据的信号处理过程的非限制性示例。
图2A和图2B示出了根据本文描述的技术的一些实施例的具有十六个传感器(例如,EMG传感器)的可穿戴系统的内部组件的示意图。如图所示,可穿戴系统包括可穿戴部分210(图2A)和加密狗(dongle)部分220(图2B)。尽管未示出,但是加密狗部分220与可穿戴部分210通信(例如,通过蓝牙或其他合适的短距离无线通信技术)。如图2A中所示,可穿戴部分210包括传感器101,上文结合图1描述了其示例。传感器101向模拟前端230提供输出(例如,感测到的信号),模拟前端230对感测到的信号执行模拟处理(例如,降噪和滤波等)。由模拟前端230产生的处理过的模拟信号然后被提供给模数转换器232,模数转换器232将处理过的模拟信号转换成可由一个或更多个计算机处理器处理的数字信号。根据一些实施例,可使用的计算机处理器的示例是微控制器(MCU)234。如图2A中所示,MCU 234还可以接收来自其他传感器(例如,IMU 240)以及电源和电池模块242的输入。可以理解,MCU 234可以接收来自未具体示出的其他设备的数据。MCU 234的处理输出可以提供给天线250,用于传输到加密狗部分220,如图2B所示。
加密狗部分220包括与可穿戴部分210的天线250通信的天线252。天线250和252之间的通信可以通过任何合适的无线技术和协议发生,其非限制性示例包括射频信令和蓝牙(Bluetooth)。如图所示,由加密狗部分220的天线252接收到的信号可以被提供给主计算机用于进一步处理、用于显示和/或用于实现对一个或多个特定物理或虚拟对象的控制(例如,以执行在AR或VR环境中的控制操作)。
虽然在与EMG传感器接口的背景下讨论了参考图1、图2A和图2B提供的示例,但是应理解,本文所描述的可穿戴系统也可以用其他类型的传感器来实现,包括但不限于肌动图(MMG)传感器、声肌图(SMG)传感器和电阻抗断层扫描(EIT)传感器。
如上面结合图2A和图2B所描述的,可穿戴系统可以包括被编程为与传感器(例如,神经肌肉传感器101和/或IMU传感器240)通信的一个或更多个计算机处理器(例如,MCU234)。例如,由一个或更多个传感器记录的信号可以提供给处理器,该处理器可以被编程为执行处理由传感器捕获的信号的一个或更多个训练过的推理模型或机器学习模型。
在一些实施例中,训练过的推理模型可以包括神经网络,并且例如可以包括循环神经网络。在一些实施例中,循环神经网络可以是长短期记忆(LSTM)神经网络。然而,应当理解,循环神经网络不限于LSTM神经网络并且可以具有任何其他合适的架构。例如,在一些实施例中,循环神经网络可以是完全循环神经网络、门控循环神经网络、递归神经网络、Hopfield神经网络、联想记忆神经网络、Elman神经网络、Jordan神经网络、回声状态神经网络、二阶循环神经网络和/或任何其他合适类型的循环神经网络。在其他实施例中,可以使用不是循环神经网络的神经网络。例如,可以使用深度神经网络、卷积神经网络和/或前馈神经网络。在一些实施方式中,推理模型可以包括无监督机器学习模型,即,用户不需要执行推理模型预先被训练来进行预测或识别的一组预定的手状态配置。在其他实施例中,推理模型可以包括一个或更多个受监督的机器学习模型,其中用户响应于指令执行特定手势或手状态配置,并且来自电极或传感器的检测到和处理过的信号已经与执行的手势或手状态配置相关联。
在一些实施例中,可以实现一个或更多个推理模型(例如,如上所讨论的神经网络)来对以独特时空模式为特征的多组(sets)EMG信号或模式进行分类,所述时空模式随着用户肌肉或运动单位产生的不同力量的大小而变化。处理过的EMG信号和/或模式可以与用户在信号感测期间穿戴手环系统的方式或方法相关联(例如,手环在用户前臂上的取向和/或定位)。因此,在一个实施例中,推理模型可以将一个或更多个独特的EMG信号或模式与神经肌肉臂环在用户上的特定取向和/或定位相关联(例如,以电极与用户皮肤的某些区域接触的方式)。同样,当用户将臂环从前臂下部位置移动到前臂上部位置(或反过来,即从前臂上部位置移动到前臂下部位置)时,推理模型可以将一个或更多个独特的EMG信号或模式与臂环的旋转取向和/或臂环的重新定位相关联。因此,可以训练推理模型以将各种臂环旋转和/或位置偏移与检测到和处理过的EMG信号或信号模式相关联。一旦系统可以识别手环在用户上的特定取向和/或定位,系统就可以选择并应用处于该特定取向和/或定位的一个或更多个预先训练过的推理模型以预测,例如,与其他推理模型相比具有更高准确度的用户手状态配置,该其他推理模型可能已经使用手环在用户上的不同取向和/或位置预先训练过。换句话说,在某些实施例中,臂环系统可以通过选择特定的推理模型来校准,使得系统适应任何旋转和/或手臂位置偏移而不干扰用户体验,本文也称为自动校准。在其他实施例中,与用于训练一个或更多个推理模型的手环的取向和/或定位相比,检测到的手环在用户上的取向偏移和/或相对定位差异可用于“归一化”用于输入到推理模型中的手环的取向和/或定位。可以定义虚拟参考电极(例如,“0”电极),并且可以使用相对于虚拟参考电极的旋转偏移来归一化手环的取向和/或定位。例如,可以基于检测到和处理过的EMG信号来确定手环相对于虚拟参考电极具有三个电极的旋转偏移。在这种情况下,可以进一步处理检测到的EMG信号以解决这个特定的偏移(在输入到训练过的推理模型之前或之后)。
在一些实施方式中,臂环系统可以被自动校准,使得臂环系统适应可能已经受伤或缺少肌肉、具有不同的脂肪组织或脂肪以及其他解剖变量的用户。尽管下面在多个训练过的推理模型的背景下进行了讨论,但是应当理解,在某些情况下,本文讨论的实施例可以被实现为单个或唯一训练过的推理模型或机器学习模型。还可以理解,可以根据从多个用户收集的数据训练至少一些推理模型。例如,可以在多个用户执行一种或更多种手状态配置(例如姿势或手势)时从记录的EMG信号中收集数据。
在一个实施例中,可以按照如下方式创建对EMG模式进行分类的推理模型:1)建立新的推理模型/实验类,其将一组预处理的EMG信号作为输入;2)通过将旋转偏移随机应用于预处理的EMG信号来生成训练数据;3)当增强偏移为0时产生正标签,否则产生空值;4)校准训练数据,使得在偏移=0处有校准数据;以及5)使用校准后的训练数据训练推理模型,并通过测试不同的旋转偏移来评估训练过的推理模型的性能。
图3中示出了由训练过的推理模型跨具有从不同用户收集的数据的数据集生成的输出的分布的示例。可以理解,与未校准训练数据时相比,校准过训练数据(即偏移=0)时,训练过的模型输出平均显示出更高的置信度值。在此示例中,训练过的模型为收集的EMG数据的每个80毫秒(ms)块生成一个预测,但是类似地,可以使用其他时间间隔。此外,在此示例中,使用二元分类器来确定手环是否在优选取向的“0”电极处校准。图4显示了跨时间平均的预测值,其使得预测具有更大的置信度值。可以理解,通过应用10秒的平滑因子,可以更准确地获取预测偏移,例如具有在±1个电极范围内的准确度。图5显示了以接受者操作特征曲线下面积(AUC)表示的模型的准确度。
在一些实施方式中,与小的偏移误差相关联的电极不变性因子可以例如在训练阶段期间被提供作为推理模型的输入。这种推理模型在本文中也被称为增强推理模型。例如,可以通过扩展一批训练数据并为训练数据批中的每个样本创建新维度来实现增强推理模型。新维度包括相同特征向量的具有不同电极旋转的不同版本的串联(concatenation)。例如,给定维度为384的特征向量,可以通过向增强推理模型提供维度为3x384的特征张量作为输入来设置±1个电极的不变性。这样的特征张量包括额外的维度,该维度包含具有三个旋转偏移[-1,0,1]的相同特征向量。然后,增强推理模型可以从第一输入密集层开始,并且可以通过跨额外维度池化来减少这个额外维度,例如,通过在额外维度上进行最大激活。因此,增强推理模型为三个偏移中的每一个应用相同的一组系数(对于第一密集层),然后为每个滤波器选择三个建议的偏移上的最大激活。图6示出了基线模型和增强推理模型之间的相关性。应该理解的是,基线模型对偏移误差更敏感,因为它在未校准(即偏移!=0)时会急剧降低性能。图7示出了使用被设置为±1、±2和±3的电极不变性训练的增强推理模型与基线模型的相关性。
在另一个实施例中,自动校准过程包括经由跨不同用户及他们的前臂和/或手腕的一个或更多个公共参考点数字地对齐手环位置(例如,旋转、前臂上的定位和/或取向),使得系统可以识别每个电极的虚拟参考点,并且可以一致地表示前臂和/或手腕的相同部分,而不考虑手环位置。例如,这可以通过应用电极排列(例如,根据应用于前臂或应用于左前臂或右前臂的手环取向颠倒电极的顺序)并将圆形旋转应用于电极来实现。图8示意性地示出了单个EMG电极“0”在第一位置810和第二位置820处的能量图的可视化,其中EMG电极旋转了三个位置。本文所公开的系统和方法可利用此类能量图中的差异以实现如本文所述的自动校准结果。
在一些实施例中,用户不需要执行特定的校准手势来配准手环位置。相反,可以训练自动校准模型来识别和预测生成一组给定EMG信号或模式的特定电极排列。数据增强技术可以在以受监督机器学习方式获得的数据集上实现。例如,可以从一组用户收集离线EMG数据,这些用户执行一组标准手势或姿势,包括在四个主要方向上的各种捏合和手腕运动,以基于与那些手势或姿势相关联的收集和处理的EMG数据配准手环旋转。通过这个过程,系统可以生成标签来训练模型(例如,自动校准模型)以识别不同的模拟手环旋转和/或取向(例如,数据增强)。训练自动校准模型的手势或姿势的数量是可变的,但可以包括大约八种手势。在这个实施例中,自动校准任务可以看作是一个分类任务,收集和标记的数据越多,电极的预测偏移的精度越高。在此离线训练期间,可以旋转手环,并且可以在每个旋转位置从用户收集额外数据。例如,如果手环有16个电极通道,则可以在16个不同取向中的每一个处从同一用户或跨多个用户收集数据。一些实施例采用自动校准模型来预测可穿戴系统被旋转到多个(例如,16个)位置之一。
图9示出了根据一些实施例的用于自动校准模型的离线训练的流程图的示例。在动作910中,当一个或更多个用户以不同的手环取向执行多个手势或姿势时感测EMG数据。如上所讨论的,可以使用任何合适数量的手势或姿势(例如,8种手势或姿势)。然后该过程进行到动作912,其中为每个手势或姿势提取模板,其中每个模板对应于感测到的EMG信号的时空模式。然后该过程进行到动作914,其中提取的模板用于生成校准的EMG数据。任何给定的电极通道都可以被指定为“0”通道电极,在“0”通道电极处,系统在默认情况下被设置为处理和解释EMG输入以预测手状态配置。
在动作916中,根据收集数据时臂环的旋转度,为数据收集期间的每个手环取向分配偏移值。通过这种方式,针对特定手势和/或姿势收集和处理的每个标记数据集可以进一步用取决于手环取向的偏移值标记,并且可以为手环生成相关联的模拟位置。已分配给手势和/或姿势的校准EMG信号的标记数据和基于特定电极通道取向识别的EMG信号可以在动作920中输入到自动校准模型中以训练模型。在一些实施例中,自动校准模型是神经网络(例如LSTM),并且自动校准模型被训练以基于接收到的EMG信号数据来预测臂环的取向。一旦经过适当训练,该过程进行到动作922,其中可以为任何用户实现自动校准模型,以基于从用户接收到的EMG数据预测手环的取向和/或旋转偏移(例如,相对于“0”通道)以及基于先前收集的用户EMG数据预测手环定位和/或取向的模拟。在一些实施例中,模型可以被训练以使得它可以在+/-1个电极通道的误差内预测旋转度或偏移。
在自动校准模型经过适当训练以在+/-1个电极通道的误差内预测手环的取向后,自动校准模型可以“在线”使用,以在用户执行一种或更多种运动活动或预期的运动活动期间预测任何给定用户上的手环取向。
与需要用户执行特定手势或姿势以进行校准的技术相比,一些实施例使用自动校准模型来自动检测可穿戴设备在身体部位上的位置,该自动校准模型无需用户执行特定的校准手势或姿势。这样的实施例允许在用户穿戴设备期间设备的连续或周期性自动校准,而无需用户停止他们正在做的事情并执行特定的校准手势或姿势。图10示出了根据一些实施例的训练过的自动校准模型的在线使用的流程图。在动作1010中,从穿戴可穿戴设备(例如,图1中所示的可穿戴设备)的用户感测EMG数据。在动作1012中,将感测到的EMG数据提供给训练过的自动校准模型(例如,在图9中的过程的动作920中训练的自动校准模型)。然后图10的过程进行到动作1014,其中模型的输出用于通过例如将EMG信号或信号模式分类到多个(例如,16个)旋转位置之一来预测可穿戴设备的位置和/或取向。然后该过程进行到动作1016,其中基于自动校准模型的预测输出的校正被应用于感测的EMG数据以生成校准的EMG数据。在其他实施例中,感测的EMG数据不被校正,并且感测的EMG数据作为输入被提供给在检测到的取向和/或位置处训练的选定推理模型或作为输入被提供给修改过的模型,以解释感测到的EMG数据并准确输出手状态配置、姿势和/或手势。
如上所述,在一些实施例中,自动校准模型可以被实现为LSTM神经网络。在某些实施例中,需要解决旋转偏移的+/-1个电极通道错误率(参见图6,其示出了对于+/-1个电极偏移同样表现良好的增强模型,但基线模型对于+/-1个电极偏移表现不佳)。这可以通过在神经网络内实现池化层来完成,如图10的动作1020示意性所示。例如,池化层可以是最大池化层,其中最大池化层可以被配置为确定“0”电极通道的位置(例如,基于三个电极位置中每一个的概率计算),因此系统可以虚拟地将特定电极通道标记为自动校准模型最初识别的一组可能的三个通道中的“0”参考通道。应当理解,可以使用本领域公知的其他类型的池化层,例如平均或均值池化层。
在某些实施例中,手环或者在特定时间段内连续地或者在离散时间点检测和分析EMG信号或EMG信号模式。在任一情况下,在这个定义的时间段内或在离散时间点,自动校准模型可用于确定手环在用户身体部位(例如前臂)上的取向和/或定位。在某些实施例中,手环最初在用户穿戴手环时和/或响应于在手环的初始放置之后检测到手环移动来检测和处理EMG信号或EMG信号模式。例如,系统可以识别可穿戴设备的至少一部分(例如,一个或更多个EMG传感器)已经移动,例如围绕用户的手腕或前臂旋转和/或沿着其平移,并且响应于确定可穿戴设备的至少一部分(例如,手环)已经移动,系统可以通过根据本文描述的一种或更多种自动校准技术确定可穿戴设备的当前位置和/或取向来执行自动校准。通过在识别可穿戴设备移动后自动重启校准,可以高效及时地进行重新校准。
可以以任何合适的方式检测可穿戴设备在用户上的移动。在一些实施例中,可穿戴设备的移动的检测是基于在由多个神经肌肉传感器感测的神经肌肉信号中识别的一个或更多个检测到的移动伪影。在包括至少一个惯性测量单元(IMU)传感器的其他实施例中,可至少部分地基于由至少一个IMU传感器感测到的至少一个信号来检测可穿戴设备的移动。在其他实施例中,可穿戴设备的移动的检测可以至少部分地基于来自多种类型的传感器(例如,至少一个EMG传感器和至少一个IMU传感器)和/或包括在可穿戴设备上或在可穿戴设备外部的传感器(例如,至少一个相机传感器)的信号。
使用本文所述的技术,系统能够基于一个或更多个采样率或频率在不到10秒内校准臂环的取向和/或位置。在其他实施例中,由于手环移动后EMG电极的稳定时间(例如,来自皮肤-电极相互作用)可能导致最初降低的信号质量,可能需要更长的时间来获得手环位置的可靠估计。例如,系统可以在检测到手环移动时启动自动校准序列,并且该序列可以运行大约30秒以获得对手环取向和/或位置的更可靠预测。可以使用更短的时间段来运行自动校准序列,前提是可以减少稳定时间,并且在自动校准序列期间几乎没有检测到手环移动。在某些实施例中,自动校准序列可以在检测到任何手环移动时启动并重新运行,无论该移动多么轻微,以最大化与EMG信号或EMG信号模式相关联的预测的准确度。
在其他实施例中,本文描述的技术可用于识别用户身体部位上的特定位置,例如,手环在用户前臂上有多靠上,因为它与手环和用户的肘关节或腕关节之间的距离有关。类似于上面描述的实施例(例如,在图9中),其中当用户在手环的不同取向下执行不同手势和/或姿势时,从标记的EMG信号或EMG信号模式数据生成和训练通用离线模型,在其他实施例中,可以从用户收集额外数据,该用户在手环的不同或相同取向下但是在用户身体部位上的不同位置(例如,沿着前臂的部分)处执行手势和/或姿势。通过这种方式,可以训练一个或更多个通用模型来预测手环的特定取向(例如,旋转),以及其在用户身体部位上的定位(例如,相对于参考点如肘关节或腕关节)。考虑到EMG信号或EMG信号模式的潜在可变性,这些实施例可用于根据手环在用户上的特定位置更好地预测用户手状态配置。考虑到神经解剖学限制和特定用户神经解剖学,分析用户身体部位更远或更近点(例如,沿着前臂或手腕的不同部分)上的EMG信号可以是有益的。在某些实施例中,自动校准模型可以检测手环在用户前臂上的特定定位,并生成到界面的输入信号以指示用户根据所需的特定结果将手环重新定位在更远或更近的位置(例如,将手环移到手臂上更靠上的位置,以便可以收集相对更多的EMG数据,或者移动到手臂上更靠下的位置,因为待使用的某些输入模型是根据当手环更靠近手腕时从用户收集的数据进行训练的)。
虽然上面已经描述了各种实施例,但是应当理解,它们仅通过示例而非限制的方式呈现。在上述方法和/或示意图指示以特定顺序发生的某些事件和/或流型(flowpattern)的情况下,可以修改某些事件和/或流型的顺序。虽然已经具体示出和描述了实施例,但是应当理解,可以在形式和细节上进行各种改变。此外,某些步骤可以在可能的情况下在并行过程中同时执行,也可以如上所述按顺序执行。尽管各种实施例已被描述为具有特定特征和/或组件的组合,但其他实施例也可能具有来自本文描述的任何实施例的任何特征和/或组件的任意组合或子组合。此外,虽然各种实施例被描述为具有与特定计算设备相关联的特定实体,但在其他实施例中,不同的实体可以与其他和/或不同的计算设备相关联。
本文描述的系统和方法旨在由软件(存储在存储器中和/或在硬件上执行)、硬件或其组合来执行。硬件模块可以包括例如通用处理器、现场可编程门阵列(FPGA)和/或专用集成电路(ASIC)。软件模块(在硬件上执行)可以用多种软件语言(例如计算机代码)表示,包括Unix实用程序、C、C++、JavaTM、JavaScript、Ruby、SQL、Python、Fortran、R编程语言/软件环境、Visual BasicTM和其他面向对象的、过程性的或其他的编程语言和开发工具。计算机代码的示例包括但不限于微代码或微指令、机器指令(例如由编译器生成)、用于生成Web服务的代码以及包含由计算机执行的更高级别指令的文件。计算机代码的其他示例包括但不限于控制信号、加密代码和压缩代码。本文描述的每个设备可以包括一个或更多个如上所述的处理器。
本文描述的一些实施例涉及具有其上有用于执行各种计算机实现的操作的指令或计算机代码的非暂时性计算机可读介质(也可以称为非暂时性处理器可读介质或存储器)的设备.计算机可读介质(或处理器可读介质)在其本身不包括瞬态传播信号(例如,在诸如空间或电缆之类的传输介质上携带信息的传播电磁波)的意义上是非瞬态的。媒体和计算机代码(也可以称为代码)可以是为一个或一些特定目的而设计和构建。非暂时性计算机可读介质的示例包括但不限于:磁存储介质,例如硬盘、软盘和磁带;光存储介质,例如光盘/数字视频光盘(CD/DVD)、光盘只读存储器(CD-ROM)和全息设备;磁光存储介质,例如光盘;载波信号处理模块;以及专门配置为存储和执行程序代码的硬件设备,例如专用集成电路(ASIC)、可编程逻辑设备(PLD)、只读存储器(ROM)和随机存取存储器(RAM)设备。本文描述的其他实施例涉及一种计算机程序产品,其可以包括例如本文讨论的指令和/或计算机代码。
处理器可执行指令可以是多种形式,例如由一个或多个计算设备执行的程序模块,并且可以包括执行特定任务或实现特定数据类型的例程、程序、对象、组件、数据结构等,以及可以针对各种实施例适当地组合和/或分配功能。数据结构可以以多种合适的形式存储在处理器可读介质中。为简单示意,可以将数据结构显示为具有与数据结构中的位置相关的字段。这种关系同样可以通过为字段分配处理器可读介质中的存储位置来实现,该存储位置传达字段之间的关系。然而,任何合适的机制/工具都可以用于在数据结构的字段中的信息之间建立关系,包括通过使用指针、标签或其他在数据元素之间建立关系的机制/工具。
各种公开的概念可以体现为一种或多种方法,且已经提供了该方法的示例。作为特定方法的一部分执行的动作可以以任何合适的方式进行排序。因此,可以构造实施例,其中动作以不同于图示/讨论的顺序执行,可以包括同时执行一些动作,即使在说明性实施例中显示为顺序动作。流程图的使用并不意味着限制所执行操作的顺序。本文描述的主题有时示出包含在不同的其他组件内或与不同的其他组件连接的不同组件。应当理解,这样描述的架构仅仅是示例性的,并且实际上可以实现许多其他架构,其实现相同的功能。从概念上讲,实现相同功能的任意组件排列都是有效的“关联”,从而实现期望的功能。因此,本文中组合以实现特定功能的任意两个组件可以被视为彼此“关联”,从而实现期望的功能,而与架构或中间组件无关。同样,任意两个如此关联的组件也可以被视为彼此“可操作地连接”或“可操作地耦合”以实现期望的功能,并且任意两个能够如此关联的组件也可以被视为彼此“可操作地可耦合”以实现期望的功能。“可操作地可耦合”的具体示例包括但不限于物理可延展和/或物理交互组件、和/或无线可交互和/或无线交互组件、和/或逻辑交互和/或逻辑可交互组件。
本文定义和使用的所有定义应理解为控制字典定义、通过引用并入的文件中的定义和/或所定义术语的普通含义。
在本文说明书和权利要求中使用的不定冠词“一个”,除非明确指出相反意义,否则应理解为表示“至少一个”。
在本文说明书和权利要求中使用的短语“和/或”应理解为表示元素中的“一个或两个”,这些元素被这样结合,即,在某些情况下并存和在其他情况下分别存在。用“和/或”列出的多个元素应以相同的方式解释,即,“一个或多个”这样结合的元素。除了由“和/或”子句具体标识的元素之外,可选地,可以存在与那些具体标识的元素相关或不相关其他元素。因此,作为非限制性示例,当与诸如“包括”之类的开放式语言结合使用时,对“A和/或B”的引用,在一个实施例中,可以仅指A(可选地包括除B之外的元素);在另一个实施例中,可以仅指B(可选地包括除A之外的元素);在又一实施例中,可以指A和B两者(可选地包括其他元素);等等。
在本文说明书和权利要求中使用的“或”应理解为与如上定义的“和/或”具有相同的含义。例如,当分离列表中的项目时,“或”或“和/或”应被解释为包括性的,即包括多个或列表中元素中的至少一个或多个,以及可选地,其他未列出的项目。只有明确指出具有相反意义的术语,例如“仅其中之一”或“恰好是其中之一”,或者,当在权利要求中使用“由……组成”时,其将指代包括多个或列表中元素中的恰好一个。一般而言,当前面有排他性术语时,例如“要么”、“其中之一”、“仅其中之一”或“恰好其中之一”,本文使用的术语“或”仅应解释为表示排他性的替代方案(即“一个或另一个但不是两个”)。当在权利要求中使用“基本上由……组成”时,应具有专利法领域中的普通含义。
在本文说明书和权利要求中使用的短语“至少一个”,在引用一个或更多个元素的列表时,应理解为表示选自元素列表中的任意一个或多个元素中的至少一个元素,但不一定包括元素列表中具体列出的每个元素中的至少一个,并且不排除元素列表中元素的任意组合。该定义还允许除了在短语“至少一个”所指的元素列表中具体标识的元素之外的与那些具体标识的元素相关或不相关的元素可以可选地存在。因此,作为非限制性示例,“A和B中的至少一个”(或等效地,“A或B中的至少一个”,或等效地“A和/或B中的至少一个”),在一个实施例中,可以指至少一个,可选地包括多于一个、A、不包括B(并且可选地包括除B之外的元素);在另一实施例中,可以指至少一个,可选地包括多于一个、B、不包括A(并且可选地包括除A之外的元素);在又一实施例中,可以指至少一个,可选地包括多于一个、A以及至少一个,并且可选地包括多于一个、B(并且可选地包括其他元素);等等。
在权利要求以及上述说明书中,所有过渡短语,例如“包括”、“携带”、“具有”、“包含”、“涉及”、“持有”、以及“由……组成”等应被理解为是开放式的,即意味着包括但不限于。根据美国专利局专利审查程序手册第2111.03节的规定,只有过渡短语“由……组成”和“基本上由……组成”应分别是封闭或半封闭的过渡短语。
Claims (37)
1.一种用于校准被配置为穿戴在用户的手腕或前臂上的可穿戴设备的位置和/或取向的系统,所述系统包括:
多个传感器,其布置在所述可穿戴设备上,其中所述多个传感器被配置为连续感测来自所述用户的多个神经肌肉信号;以及
至少一个计算机处理器,其被编程为:
提供所述多个神经肌肉信号和/或从所述多个神经肌肉信号导出的信号作为一个或更多个训练过的自动校准模型的输入;
至少部分地基于所述一个或更多个训练过的自动校准模型的输出,确定所述可穿戴设备在所述用户上的当前位置和/或取向;以及
至少部分地基于所述可穿戴设备在所述用户上的当前位置和/或取向以及所述多个神经肌肉信号来生成控制信号。
2.根据权利要求1所述的系统,其中所述至少一个计算机处理器被编程为确定所述可穿戴设备在所述用户上的当前位置和/或取向,而无需所述用户在感测所述多个神经肌肉信号期间执行特定姿势或手势。
3.根据权利要求1所述的系统,其中所述至少一个计算机处理器被编程为在将处理过的神经肌肉信号提供给所述一个或更多个自动校准模型之前处理感测到的多个神经肌肉信号。
4.根据权利要求1所述的系统,其中所述至少一个被编程的处理器被进一步编程为:
至少部分地基于所述可穿戴设备的当前位置和/或取向以及所述多个神经肌肉信号生成校准的神经肌肉信号,并且
其中生成控制信号包括至少部分地基于所述校准的神经肌肉信号生成控制信号。
5.根据权利要求1所述的系统,其中所述至少一个被编程的处理器被进一步编程为:
至少部分地基于所述可穿戴设备的当前位置和/或取向来选择或修改一个或更多个推理模型;以及
提供所述多个神经肌肉信号作为所选择或修改的一个或更多个推理模型的输入,并且
其中生成控制信号还至少部分地基于所选择或修改的一个或更多个推理模型的输出。
6.根据权利要求1所述的系统,其中用于确定所述可穿戴设备在所述用户上的当前位置和/或取向的所述一个或更多个自动校准模型包括神经网络。
7.根据权利要求6所述的系统,其中所述神经网络是LSTM神经网络。
8.根据权利要求7所述的系统,其中所述LSTM神经网络包括至少一个池化层。
9.根据权利要求8所述的系统,其中所述至少一个池化层包括最大池化层。
10.根据权利要求8所述的系统,其中所述至少一个池化层提供±1个传感器位置的旋转不变性。
11.根据权利要求1所述的系统,其中所述一个或更多个自动校准模型被训练以基于从多个用户获取的神经肌肉信号识别所述可穿戴设备的取向和/或定位。
12.根据权利要求11所述的系统,其中当多个用户中的每一个用户执行多个手势和/或姿势、同时所述可穿戴设备以多种不同配置被定向和/或定位在该用户的前臂和/或手腕上时,获取从所述多个用户获取的所述神经肌肉信号。
13.根据权利要求1所述的系统,其中所述多个神经肌肉传感器包括多个肌电图(EMG))感器。
14.根据权利要求1所述的系统,其中所述至少一个计算机处理器还被编程为:
识别所述可穿戴设备的至少一部分已经在所述用户的手腕或前臂上移动;以及
响应于识别所述可穿戴设备的至少一部分已经在所述用户的手腕或前臂上移动,确定所述可穿戴设备在所述用户上的当前位置和/或取向。
15.根据权利要求14所述的系统,其中识别所述可穿戴设备的至少一部分已经在所述用户的手腕或前臂上移动包括确定所述可穿戴设备已经围绕所述用户的手腕或前臂旋转和/或沿所述用户的手腕或前臂平移。
16.根据权利要求14所述的系统,其中识别所述可穿戴设备的至少一部分已经在所述用户的手腕或前臂上移动包括检测感测到的多个神经肌肉信号中的一个或更多个移动伪影。
17.根据权利要求14所述的系统,还包括至少一个惯性测量单元(IMU)传感器,并且其中识别所述可穿戴设备的至少一部分已经在所述用户的手腕或前臂上移动是至少部分地基于由所述至少一个IMU传感器感测到的至少一个信号。
18.根据权利要求1所述的系统,其中确定可穿戴设备的当前位置和/或取向包括确定所述可穿戴设备相对于所述可穿戴设备的虚拟参考取向的旋转。
19.根据权利要求1所述的系统,其中所述控制信号是控制虚拟现实系统或增强现实系统的一个或更多个操作的控制信号、或控制虚拟现实系统或增强现实系统内的一个或更多个操作的控制信号。
20.一种用于校准可穿戴手环在用户上的位置和/或取向的方法,所述方法包括:
使用布置在所述可穿戴设备上的多个传感器感测来自所述用户的多个神经肌肉信号;
提供所述多个神经肌肉信号和/或从所述多个神经肌肉信号导出的信号作为一个或更多个训练过的自动校准模型的输入;
至少部分地基于所述一个或更多个训练过的自动校准模型的输出,确定所述可穿戴设备在所述用户上的当前位置和/或取向;以及
至少部分地基于所述可穿戴设备在所述用户上的当前位置和/或取向以及所述多个神经肌肉信号来生成控制信号。
21.根据权利要求20所述的方法,其中确定所述可穿戴设备的当前位置和/或取向包括确定所述可穿戴设备的当前位置和/或取向而无需所述用户在感测所述多个神经肌肉信号期间执行特定姿势或手势。
22.根据权利要求20所述的方法,还包括在将处理过的神经肌肉信号提供给所述一个或更多个自动校准模型之前处理感测到的多个神经肌肉信号。
23.根据权利要求20所述的方法,还包括:
至少部分地基于所述可穿戴设备的当前位置和/或取向以及所述多个神经肌肉信号生成校准的神经肌肉信号,并且
其中生成控制信号包括至少部分地基于所述校准的神经肌肉信号生成控制信号。
24.根据权利要求20所述的方法,还包括:
至少部分地基于所述可穿戴设备的当前位置和/或取向来选择或修改一个或更多个推理模型;以及
提供所述多个神经肌肉信号作为所选择或修改的一个或更多个推理模型的输入,
其中生成控制信号还至少部分地基于所选择或修改的一个或更多个推理模型的输出。
25.根据权利要求20所述的方法,其中用于确定所述可穿戴设备在所述用户上的当前位置和/或取向的所述一个或更多个自动校准模型包括神经网络。
26.根据权利要求20所述的方法,其中所述一个或更多个自动校准模型被训练以基于从多个用户获取的神经肌肉信号识别所述可穿戴设备的取向和/或定位。
27.根据权利要求26所述的方法,其中当多个用户中的每一个用户执行多个手势和/或姿势、同时所述可穿戴设备以多种不同配置被定向和/或定位在该用户的前臂和/或手腕上时,获取从所述多个用户获取的所述神经肌肉信号。
28.根据权利要求20所述的方法,其中确定可穿戴设备的当前位置和/或取向包括确定所述可穿戴设备相对于所述可穿戴设备的虚拟参考取向的旋转。
29.根据权利要求20所述的方法,还包括:
识别所述可穿戴设备的至少一部分已经在所述用户的手腕或前臂上移动;以及
响应于识别所述可穿戴设备的至少一部分已经在所述用户的手腕或前臂上移动,确定所述可穿戴设备在所述用户上的当前位置和/或取向。
30.根据权利要求29所述的方法,其中识别所述可穿戴设备的至少一部分已经在所述用户的手腕或前臂上移动包括确定所述可穿戴设备已经围绕所述用户的手腕或前臂旋转和/或沿所述用户的手腕或前臂平移。
31.根据权利要求29所述的方法,其中识别所述可穿戴设备的至少一部分已经在所述用户的手腕或前臂上移动包括检测感测到的多个神经肌肉信号中的一个或更多个移动伪影。
32.根据权利要求29所述的方法,还包括识别所述可穿戴设备的至少一部分已经在所述用户的手腕或前臂上移动是至少部分地基于由至少一个惯性测量单元(IMU)传感器感测到的至少一个信号。
33.一种用于训练用于可穿戴设备的自动校准的推理模型的方法,所述方法包括:
当多个用户中的每一个用户穿戴所述可穿戴设备并执行多个手势和/或姿势时,接收从所述多个用户感测到的神经肌肉信号,其中对于所述多个手势和/或姿势中的每一个,所述可穿戴设备在所述用户上的取向或位置不同;
在感测相应的神经肌肉信号期间,基于所述可穿戴设备的取向或位置,为所述多个手势和/或姿势中的每一个标记接收到的神经肌肉信号;以及
至少部分基于标记的神经肌肉信号训练一个或更多个自动校准模型。
34.根据权利要求33所述的方法,还包括:
从接收的神经肌肉信号中提取多个模板,其中所述多个模板中的每一个对应所述多个手势和/或姿势之一;以及
至少基于提取的多个模板生成校准的神经肌肉信号,并且
其中训练所述一个或更多个自动校准模型包括至少部分地基于所述校准的神经肌肉信号训练所述一个或更多个自动校准模型。
35.根据权利要求33所述的方法,还包括:
使用不同取向的多个模拟来预测所述可穿戴设备的多个旋转偏移。
36.根据权利要求33所述的方法,其中所述一个或更多个自动校准模型包括神经网络。
37.根据权利要求36所述的方法,其中所述神经网络是LSTM神经网络。
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