CN107077242A - 通过使用时空触摸图案来提高触摸屏事件分析的准确性的方法 - Google Patents
通过使用时空触摸图案来提高触摸屏事件分析的准确性的方法 Download PDFInfo
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Abstract
一种对触摸屏事件进行分类的方法,所述方法使用在短时间段上的触摸事件的已知非随机图案来增大分析此类事件的准确性。所述方法利用了在一个触摸事件之后,某些动作比其他动作更可能发生的事实。因此,如果触摸事件被分类为敲击并且之后在500ms之内在类似位置发生新的事件,但是分类置信度低(例如,60%指甲、40%指关节),则分类器可以增加指关节类别的权重,因为这个触摸序列更加有可能。可以使用关于随后触摸事件的可能性的知识来对序列类别进行偏置,从而增加具体事件的权重。
Description
发明人:Julia Schwarz(朱莉娅·施华兹)
Chris Harrison(克里斯·哈里森)
版权声明
本专利文献的公开内容的一部分包含受到版权保护的材料。版权所有者并不反对任何人对本专利文献或专利公开内容按照其在专利商标局专利文献或记录中所呈现地进行复制再现、但是保留所有版权权利。
技术领域
本发明总体上涉及触摸屏技术领域,并且更具体地涉及使用触摸事件的已知非随机图案来提高触摸屏事件分析的准确性。
背景
背景部分中所讨论的主题不应仅仅由于其在背景部分中有所提及而被假定为现有技术。类似地,背景部分中所提及的问题或者与背景部分的主题相关联的问题不应被假定为先前已在现有技术中被认识到。背景部分的主题仅仅代表不同的途径,其中的和其自身的途径也可能是发明。
典型地,当今的各种电子设备由用户通过与触摸屏的交互进行操作。此特征具体地是最近一代智能电话的特性。通常,触摸屏显示屏响应手指接触来激活所述显示器,以便进一步处理。还可以使用工具(如触笔)、手的其他部位(如手掌)和手指的不同部位来进行触摸。在这些系统中的许多系统中,触摸类型分类准确率不是100%。简单地说,有时输入类型被混淆。因此,需要帮助减少误差的机制。
发明内容
本发明实施例包括一种基于源自每个触摸事件的特征的表征来分析触摸屏事件的方法。所述方法包括:在触敏表面上检测触摸事件;使用检测每个此类触摸事件的至少一个传感器生成振动声学波形信号;将所述波形信号转换成至少一个域信号;从所述域信号中提取区分特征;以及通过采用时空事件数据对所述触摸事件的分析进行加权,来对所述所述特征进行分类以便分析所述域信号。
通过查看下文的附图、详细描述和权利要求书可以了解本发明的其他方面和优点。
附图说明
所包括的附图仅用于展示的目的、并且仅用于提供所公开技术的可能结构和过程步骤的示例。这些附图决不限制本领域技术人员在不脱离本公开的精神和范围的情况下可以对实施例做出的形式和细节上的任何更改。
图1是用于分析触摸屏事件的计算系统的框图;
图2示出了仅依赖于分类置信度水平的具有两个触摸事件(在100ms之内)的快速序列的一个示例;
图3图形地展示了从关于图2的这两个触摸快速序列的四种不同的可能解释的时空历史数据获得的事件序列权重;
图4展示了将分类置信度水平与事件序列权重进行组合将如何改变分析的可能结果;并且
图5是本发明的方法的示例性实施例的流程图。
具体实施方式
本发明的实施例利用了人类和用户界面将产生事件的非随机图案的事实。简单地说,在一个触摸事件之后,某些动作比其他动作更可能发生。例如,如果系统看到了手掌触摸而所述手掌触摸然后消失(大概从屏幕上抬起)并且接着在300ms内在相同位置看到新的触摸事件,则更可能的是用户重新放下了其手掌、而不是将手指高速移动到了其之前的手掌位置的下方。动作的另一个示例是用指关节两次触摸(即,“两次敲击”)。这些通常以快速序列——“敲击敲击"——通常在500ms之内并且在屏的相同区域内发生。因此,如果触摸事件被分类为敲击并且之后在500ms之内在类似位置发生新的事件,但是分类置信度低(例如,60%指甲、40%指关节),则分类器可以增加指关节类别的权重,因为这个触摸序列更加有可能。简单地说,可以使用关于随后触摸事件的可能性的知识来对序列类别进行偏置,从而增加具体事件的权重。此外,多个事件不一定是相同的触摸类型。例如,如果两次敲击事件之后往往是用指尖猛击而很少有第三次敲击,如果发生了第三次事件(满足必须的时空标准),则分类器将把指尖猛击加权为更可能的类别。可以在一定时间段上响应于用户的实际序列来“学习”这样的时间图案,从而为每个独立用户创建个性化触摸序列历史。
本部分中描述了根据一个或多个实施例的方法和装置的应用。提供这些示例仅是为了添加上下文并帮助理解本公开。因此,对于本领域技术人员而言将明显的是可以在没有这些具体细节中的某些或全部的情况下实践在此所描述的技术。在其他情形中,为了避免不必要地模糊本公开,未详细描述公知的过程步骤。其他应用也是可能的,这样使得以下示例不应视为对范围或设置的限定或限制。
在以下详细描述中,参考了形成说明书一部分的附图,在附图中以展示的方式示出了具体实施例。尽管足够详细地对这些实施例进行了描述以使得本领域技术人员能够实践本公开,但应理解的是,这些示例不是限制性的,因而在不脱离本公开的精神和范围的情况下可以使用其他实施例并且可以做出改变。
可以用多种方式来实现一个或多个实施例,包括过程、装置、系统、设备、方法、计算机可读介质(例如包含计算机可读指令或计算机程序代码的计算机可读存储介质)、或包括内嵌有计算机可读程序代码的计算机可用介质的计算机程序产品。
所公开的实施例可以包括一种基于源自每个触摸事件的特征的表征来分析触摸屏事件的方法。所述方法包括:在触敏表面上检测触摸事件;使用检测每个此类触摸事件的至少一个传感器来生成振动声学波形信号;将所述波形信号转换成至少一个域信号;从所述域信号中提取区分特征;以及通过采用时空事件数据对所述分析进行加权,来对所述特征进行分类以便分析所述域信号。
所公开的实施例可以包括一种基于源自每个触摸事件的特征的表征来分析触摸屏事件的方法。所述方法包括:在触敏表面上检测触摸事件;使用与所述触摸事件相关联的至少一个传感器来生成振动声学波形信号;将所述波形信号转换成至少一个域信号;从所述域信号中提取区分特征;以及通过采用时空事件数据对所述触摸事件的分析进行加权,来对所述特征进行分类以便分析所述域信号。
所公开的实施例可以包括包含指令的计算机可读介质,所述指令用于对触摸屏设备中的多个触摸事件序列进行分类以便提高确定对触摸屏进行的触摸事件的特性的准确性。处理器执行所述程序指令可以致使所述处理器:分析所述序列中的每个触摸事件以便在一定置信度水平内确定哪个手部位导致了所述触摸事件;确定可能的手部位触摸事件序列的可能性;以及将所述序列中的每个触摸事件的所述置信度水平与所述可能的手部位序列的可能性进行组合以产生关于哪个手部位实际生成了所述序列中的每个触摸事件的最可能结果。所述手部位可以是指尖、指关节、指甲和手掌中的至少一者。用于确定可能的手部位触摸事件序列的可能性的所述指令可以使用在所述多个触摸事件序列上过去的时间来确定所述可能性。用于将所述序列中的每个触摸事件的所述置信度水平与所述可能的手部位序列的可能性进行组合的所述指令可以包括用所述可能性来对所述置信度水平进行加权。所述用于组合的指令可以包括用所述置信度水平乘以所述可能性的指令。
总体上,当使用触摸屏或触敏设备时,在使用工具(如触笔)、手的其他部位(如手掌)和手指的不同部位(即,指垫、指甲、指关节等)与触摸屏接触时,可以发生触摸屏事件。每种此类不同类型的触摸机制产生了不同类型的数字签名。此外,触敏屏设备的每位用户可以拥有他或她自己独特的触摸事件特性,这些特性因解剖学差异例如多肉、手指大小、手指形状、BMI等而产生。无论是不同用户解剖学还是不同触摸机制的结果,可以通过实现不同类型的触摸、降低模糊度、在用户之间进行区分、仅响应有意的触摸事件等等将触摸事件特性的这些差异有利地用于改进触摸屏技术。此类有利使用源自对触摸事件的基于传感器的精细的分析,所述分析结合被设计用来提供在由触摸事件生成的数据中以其他方式被隐藏或不是非常明显的进一步分析特性的一个或多个算法。举例来说,由本申请人在2014年9月11日提交的并且标题为“METHOD AND APPARATUS FOR DIFFERENTIATING TOUCH SCREENUSERS BASED ON TOUCH EVENT ANALYSIS(基于触摸事件分析来区分触摸屏用户的方法和装置)”的未决美国专利申请序列号14/483150中公开了一种这样的装置。此共同未决的申请公开了:当用户触摸触摸屏时,机械力被施加到所述屏幕,导致可以被多种传感器(比如冲击传感器、振动传感器、加速度计、应变计、或声学传感器如传声器)捕获的机械振动。
一旦已经捕获了振动声学信号,所述振动声学信号被转换成一系列特征,例如:平均声功率、标准偏差、方差、偏斜度、峰度、绝对值和、均方根(RMS)、离差、过零点、频谱矩心、频谱密度、基于线性预测的倒谱系数(LPCC)、感知线性预测(PLP)、倒谱系数倒谱系数、梅尔频率倒谱系数(MFCC)、频率相位(例如,如FFT所生成的)。
同时,许多触摸屏技术能够将触摸事件的若干方面(如形状、尺寸、电容、取向、压力等)数字化。后者可以用作区别特征,或者此类特征可源自它们。
由于人手指在其解剖组成方面不同,因此其声学和触摸属性在人与人之间会不同。此外,用户触摸屏幕的方式也会是有区别的(例如哪个手指、手指的哪一部位、有多平坦、有多用力)。因此,振动声学特征和触摸特征包含可以是不同用户和用户双手的不同部位(例如,指尖、指关节和指甲)的特性的属性。
因此,可以提供可以在触摸计算设备上运行的分类器,在接收到触摸事件时,所述分类器猜测哪个用户正在操作所述设备、或者所述用户是否被授权或具有任何个性化特征。可替代地,还可以提供可以在触摸计算设备上运行的分类器,在接收到触摸事件时,所述分类器猜测手指的什么部位曾被用于接触屏幕。
在其一个示例性实施例中,所公开的过程可包括以下操作并且可采用以下部件:
(a)可被配置成用于连续地对振动声学数据进行采样(将其存入缓冲区)的感测系统。此缓冲区可以具有许多时长,比如像,50毫秒;
(b)触敏屏幕可被配置成用于等待触摸事件发生。任何数量的触摸技术可以是有可能的。触敏屏幕的操作可被配置成用于与感测系统并行运行;
(c)当触敏屏幕检测到触摸事件时,它可被配置成用于触发转换、特征提取和分类过程;
(d)对来自振动声学缓冲区中的数据进行检索。因为触摸屏通常有一些时延,所以可能有必要在缓冲区中向后查看以找到对应于触摸冲击的振动声学波形(例如,如果触摸屏具有20毫秒的时延,可能有必要在缓冲区中向后查看20毫秒以找到相应的振动声学事件)。可将缓冲区的全部或部分保存并传递到接下来的操作:
(e)接下来可执行转换操作。来自传感器的波形是振动声学信号的时域表示。除了保存波形之外,信号被转换成其他形式。这包括对波形进行滤波并转换成其他形式,包括频域表示;
(f)接下来可执行特征提取操作,其中,对触摸屏控制器数据和振动声学数据进行分析来提取表征不同用户的特征。对于振动声学数据,为信号的全部表示计算特征;
(g)然后,将这些特征传递给分类单元,所述分类单元使用所述信息以使用户对触摸事件进行标记(除了触敏屏幕报告的任何因素之外,例如X/Y位置、长/短轴、压力等);
(h)然后,可以将增强的触摸事件传递给OS或最终用户应用,从而基于触摸事件来关联使用。
对于某些实施例而言,分类器可被配置成用于使用以下特征中的一个或多个来执行其操作:触摸接触的位置(2D、或在曲面玻璃或其他非平面几何形状的情况下3D)、触摸接触的大小(某些触摸技术提供具有长轴和短轴的触摸接触的椭圆)、触摸接触的旋转、触摸接触的表面面积(例如,以平方毫米或像素为单位)、(某些触摸系统上可获得的)触摸的压力、触摸的切变(“切应力”(在文献中也称为“切向力”)由垂直于触摸屏的表面法线的力向量引起。这类似于法向应力——通常所说的压力——它由平行于表面法线的力向量引起。”)、触摸接触的数量、触摸的电容(如果使用电容式触摸屏)、触摸的扫频电容(如果使用扫频电容式触摸屏)、以及触摸的扫频阻抗(如果使用扫频电容式触摸屏)。计算阶段还可以计算较短时间内上述特征的导数,例如,触摸速度和压力速度。分类器还可以使用的其他特征包括:触摸的形状(某些触摸技术可以提供触摸的实际形状,而不只是圆或椭圆)、以及手势的图像(如由例如光学传感器、具有照相机的漫射受光表面、近距离电容感应所成像的)。
分类引擎可使用任何数量的方法,包括但不限于基本启发法、决策树、支持向量机、随机森林、朴素贝叶斯、弹性匹配、动态时间规整、模板匹配、k均值聚类、K最近邻算法、神经网络、多层感知器、多项逻辑回归、高斯混合模型、和AdaBoost。另外,可以通过例如投票方案组合来自若干不同分类器的结果。
对于某些实施例而言,可以基于一个或多个特征使用不同的分类器。例如,可以采用两个分类器,一个用于处理具有高标准偏差的传感器波形,而另一个分类器用于处理具有低标准偏差的波形。
通过采用这样的分类,触摸屏技术能够分类中哪种类型的触摸事件在发生。例如,一些屏在手指触摸与手掌触摸(通常作为无意输入被拒绝)之间进行区别。替代地,一些系统在手指触摸与用触笔进行的那些触摸之间进行区别。在上述公开的情况下,所述系统可以在例如指尖、指关节、指甲、触笔以及其他实现方式之间进行区别。
图1是根据本发明的实施例的用于基于所生成信号的使用来分析触摸事件的计算系统的框图。所述实施例的计算系统可以具有操作(OS),并且可以运行各种类型的服务或应用(被称为app)。计算系统还可配备有可允许连接至通信网络的远程通信功能。这种计算系统可以包括但不限于桌面计算机(例如,表面计算)、膝上型计算机、桌上计算机、移动计算机、移动互联网设备、移动电话、智能电话、PDA(个人数字助理)、游戏控制台、便携式媒体播放器等。
参照图1,计算系统包括触摸屏100、触摸事件检测器110、分类器120和OS 130。触摸屏100是电子视觉显示器,并且还作为补充或替代键盘、鼠标和或其他类型设备的输入/输出设备。触摸屏100显示一个或多个交互式元件,如用于被设计成用于在计算系统上执行特定功能的服务或应用的图形表示。使用用户的手指部分(包括惯常的指尖)触摸交互式元件致使OS 130激活与适合于所标识的用户的交互式元件有关的应用或服务。手指在其肌肉运动能力及其解剖学组成两方面上为不同的附属器。单个数字包含不同部分,如一个或多个指关节、指尖、指垫和指甲。
当物体敲击特定材料时,振动声学波通过材料或沿着材料的表面向外传播。通常,交互式表面使用刚性材料,如塑料或玻璃,这两者快速地散布并忠实地保持信号。这样,当一根或多根手指触摸或接触触摸屏100的表面时,产生振动声学响应。对应的用户手指的振动声学特性及其对应的独特解剖学特性为每个用户产生独特的响应。
返回参照图1,触摸事件检测器110检测蕴含振动声学信号的触摸事件。例如,可以将触摸事件检测器110安排在触摸屏的后侧,从而使得可以捕获由触摸事件所引起的振动声学信号。触摸事件检测器110可通过由触摸事件而引起的振动声学信号的开始来触发。为了捕获触摸事件及随后的振动声学信号,触摸事件检测器110可以包括一个或多个冲击传感器、振动传感器、加速度计、应变计、或者声学传感器(如电容传声器、压电传声器、MEMS传声器)等。一旦触摸事件检测器110已经捕获了振动声学信号,振动声学分类器120处理振动声学信号来分析激活了触摸屏的触摸事件。
OS 130运行计算系统,这样使得可以符合振动声学信号的分类和相应用户来激活功能。振动声学分类器120包括用于将振动声学信号分割成数字表示的分割单元122;用于将经数字化的振动声学信号转换成电信号的转换单元124;用于从电信号得出一系列特征的特征提取单元126;以及用于使用上述特征对触摸特性进行分类从而分析触摸事件的分类单元128,如下面将进一步描述的。
分割单元122对振动声学信号进行采样,例如,在96kHz的采样率下使用振动声学信号的4096个样本的滑动窗口。然后转换单元124对所采样的取决于时间的振动声学信号执行例如傅立叶变换,以产生具有频域表示的电信号。例如,此窗口的傅立叶变换可以产生2048个频率功率频带。
振动声学分类器120可进一步将此数据下采样成附加向量(即,由十个附加向量组成的存储桶(Bucket)),从而提供不同的混叠。此外,可从振动声学信号计算附加时域特征,如平均绝对振幅、总绝对振幅、绝对振幅的标准偏差、针对分割后的输入信号和傅立叶变换后的信号两者的质心、和过零点。
特征提取单元126还可从振动声学信号的频域表示计算一系列特征,例如冲击波形的基本频率。分类单元128使用这些特征来对振动声学信号进行分类从而例如区分什么用户生成了触摸事件,这样使得计算系统可以根据所分类的振动声学信号选择性地激活与所标识的用户有关的功能。为了帮助分类,用户可为振动声学分类器120提供补充训练样本。
对于某些实施例,可以用一种用于特征分类的支持向量机(SVM)实现分类单元128。SVM是具有相关联的学习算法的监督学习模型,这些学习算法分析数据并识别模式,以用于分类和回归分析。通过采用或利用由与相对短的时间窗口内的具有多个触摸事件的序列相关的历史数据所生成的已知时空图案,可以使得分类过程更准确。举例而言,图2展示了相隔100毫秒发生的两个触摸事件。正常地,如图2中进一步所示,基于85%手指对比15%指关节的分类置信度水平,第一个此类触摸事件将被分类为手指触摸而不是指关节触摸。此外,基于55%指关节对比45%手指的分类置信度水平,第二个此类触摸事件将被分类为指关节事件而不是手指事件。这样的独立触摸事件的置信度水平是作为典型分类过程的示例提供的,因此将产生一种结果,在所述结果中所述分析指示了手指-指关节序列。然而,在本发明的方法中,基于时空数据的历史生成了事件序列权重,并且此类权重可以改变所述分类过程的结果。举例而言,图3中示出了相隔100毫秒的两个触摸事件的四种可能的手指与指关节序列的组合。如其中所示的,基于此类时空数据,可能的手指-手指解释具有45%的可能性。可能的手指-指关节解释具有10%的可能性。可能的指关节-指关节解释具有35%的可能性。最后,可能的指关节-手指解释具有10%的可能性。
这些可能性数值在此是仅用于展示的目的,然而可以看到,在相隔仅100毫秒的两个触摸事件中,更有可能的是这两个事件均来自同一手部位(即,均为手指或均为指关节),简单地是因为用户较不可能在这么短的时间段内改变手部位。另一方面,如果时间段较长(即,>500ms),则在所述较长时间段内改变手部位的更大机会将提高手指-指关节或指关节-手指解释的可能性。
无论如何,一旦访问这些事件序列权重,它们就可以用于通过将分类置信度与此类序列权重进行组合来改变分类。在图4中示出了此类组合的一个示例,例如是通过乘法。在图4的方程中,关于图2的这两个触摸事件序列的每种可能解释,前两个数是分类置信度,并且第三个数是从图3所绘的时空数据得到的可能性。因此,例如在针对图4的手指-手指解释的方程中,前两个数是来自图2的85%(第一手指)和45%(第二手指),并且第三个数是45%(手指-手指的可能性)。
通过执行图4的乘法,可以看到加权后的结果是:对于手指-手指为0.127;对于手指-指关节为0.047;对于指关节-指关节为0.029;并且对于指关节-手指为0.007。因此,使用时空加权,分类的最可能结果是手指-手指而不是手指-指关节,手指-指关节是没有此类加权时的结果。应注意的是,还考虑了不同于简单乘法的加权技术。
图5是在此描述的方法的实施例的流程图表示。如其中所示的,在触摸事件分析步骤144中得到了触摸事件序列140,所述触摸事件分析步骤基于较高置信度水平来选择手部位。在触摸事件之前或与之同时,在步骤142中建立可能手部位触摸序列的可能性的历史,并且在步骤146中将这些数据与步骤144的结果组合以便在步骤148中产生加权后的可能结果。
本公开的这些和其他方面可通过各种类型的硬件、软件、固件等来实现。例如,本公开的某些特征可以至少部分地通过包括用于执行在此所描述的各种操作的程序指令、状态信息等的机器可读介质来实现。程序指令的示例包括机器代码(如由编译器产生)和文件(包含可由计算机使用解释器执行的高级代码)两者。机器可读介质的示例包括但不限于磁介质如硬盘、软盘、和磁带;光介质如CD-ROM盘;磁光介质;以及专门被配置成用于存储和执行程序指令的硬件设备,例如,只读存储器(“ROM”)和随机存取存储器(“RAM”)。
任何上述实施例可单独使用,也可彼此以任何组合来一起使用。虽然各实施例可能已经受到了现有技术各种的可能在本说明书中的一处或多处进行讨论或暗示的不足的激发,但是这些实施例并不一定解决任何这些不足。换言之,不同的实施例可能解决本说明书中可能有所讨论的不同不足。某些实施例可能仅部分地解决了本说明书中可能有所讨论的某些不足或仅某个不足,并且某些实施例可能没有解决任何上述不足。
虽然在此已描述了各实施例,但应该理解的是它们仅仅是以举例的方式呈现,而不是限制。因此,本申请的广度和范围不应被在此所描述的任何实施例所限制,而是应当仅根据以下和以后提交的权利要求书及其等同物来限定。
Claims (15)
1.一种基于源自每个触摸事件的特征的表征来分析触摸屏事件的方法,所述方法包括:
在触敏表面上检测触摸事件;
使用与所述触摸事件相关联的至少一个传感器来生成振动声学波形信号;
将所述波形信号转换成至少一个域信号;
从所述域信号中提取区分特征;以及
通过采用时空事件数据对所述触摸事件的分析进行加权,来对所述特征进行分类以便分析所述域信号。
2.如权利要求1所述的方法,其中,所述时空事件数据包括来自选定时间段上的之前多个触摸事件序列的数据。
3.如权利要求1所述的方法,其中,所述时空事件数据包括暗示在选定时间段上在第一多个触摸事件之后的每个触摸事件的可能性的数据。
4.如权利要求1所述的方法,其中,在所述分类之前累积并存储所述时空事件数据。
5.如权利要求1所述的方法,其中,所述分类通过将时空事件可能性乘以分类置信度来对所述分析进行加权。
6.一种对触摸屏设备中的多个触摸事件序列进行分类以便提高确定对触摸屏进行的触摸事件的特性的准确性的方法,所述方法包括:
分析所述序列中的每个触摸事件以便在一定置信度水平内确定哪个手部位导致了所述触摸事件;
确定可能的手部位触摸事件序列的可能性;以及
将所述序列中的每个触摸事件的所述置信度水平与所述可能的手部位序列的可能性进行组合以产生关于哪个手部位实际生成了所述序列中的每个触摸事件的最可能结果。
7.如权利要求6所述的方法,其中,所述手部位可以是指尖、指关节、指甲和手掌中的至少一者。
8.如权利要求6所述的方法,其中,所述确定使用了在所述多个触摸事件序列上过去的时间来确定所述可能性。
9.如权利要求6所述的方法,其中,所述组合包括用所述可能性来对所述置信度水平进行加权。
10.如权利要求6所述的方法,其中,所述组合包括用所述置信度水平乘以所述可能性。
11.一种包含指令的计算机可读介质,所述指令用于对触摸屏设备中的多个触摸事件序列进行分类以便提高确定对触摸屏进行的触摸事件的特性的准确性,其中处理器执行所述程序指令致使所述处理器:
分析所述序列中的每个触摸事件以便在一定置信度水平内确定哪个手部位导致了所述触摸事件;
确定可能的手部位触摸事件序列的可能性:以及
将所述序列中的每个触摸事件的所述置信度水平与所述可能的手部位序列的可能性进行组合以产生关于哪个手部位实际生成了所述序列中的每个触摸事件的最可能结果。
12.如权利要求11所述的计算机可读介质,其中,所述手部位可以是指尖、指关节、指甲和手掌中的至少一者。
13.如权利要求11所述的计算机可读介质,其中,用于确定可能的手部位触摸事件序列的可能性的所述指令使用了在所述多个触摸事件序列上过去的时间来确定所述可能性。
14.如权利要求11所述的计算机可读介质,其中,用于将所述序列中的每个触摸事件的所述置信度水平与所述可能的手部位序列的可能性进行组合的所述指令包括用所述可能性来对所述置信度水平进行加权。
15.如权利要求11所述的计算机可读介质,其中,所述用于组合的指令包括用所述置信度水平乘以所述可能性的指令。
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US10606417B2 (en) | 2020-03-31 |
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WO2016048848A1 (en) | 2016-03-31 |
US11029785B2 (en) | 2021-06-08 |
US20160085324A1 (en) | 2016-03-24 |
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