CN105426713A - 用于基于触摸事件分析来区分触摸屏使用者的方法和装置 - Google Patents
用于基于触摸事件分析来区分触摸屏使用者的方法和装置 Download PDFInfo
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Abstract
本发明的某些实施例包括一种用于基于源自触摸事件声学和机械冲击的特征的表征来区分触摸屏使用者的方法,并且包括在触敏表面上检测触摸事件、使用检测此类触摸事件的至少一个传感器生成振动声学波形信号、将该波形信号转换成至少一个域信号、从所述域信号中提取多个区别特征、以及对所述多个特征进行分类从而将该域信号的特征与特定使用者相关联。
Description
版权声明
本专利文献的披露的一部分包含受到版权保护的材料。版权所有者不反对任何人对本专利文献或本专利披露的复制,由于其出现在专利和商标局专利文件或记录中,但无论如何在其他方面保留所有版权。
技术领域
本发明总体上涉及触摸屏技术领域,并且更具体而言涉及对触摸屏机械冲击特征和声学特征的分析来在不同使用者之间进行区分。
背景
背景部分中所讨论的主题不应该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方法,在其中和其自身也可能是发明。
以下参照所披露的技术进行详细描述。描述了优选实现方式以说明所披露的技术,而不是限制其范围,该范围由权利要求书限定。本领域普通技术人员将认识到描述的多种等效变化。
典型地,当今的各种电子设备由使用者通过与触摸屏的交互而操作。某些此类设备(如触敏计算机屏幕)被设计成通常在不同时间由多个使用者操作。其他此类设备(如智能手机)通常与单个使用者相关联,如果另一使用者获得对此类单使用者设备的未授权访问,则该单个使用者的隐私会受到严重危险。
对于多个使用者操作的设备,通常有必要为计算机提供一种输入从而标识正在操作***的特定使用者,这样使得呈现适当的使用者相关程序,以便由正确的使用者进行进一步处理。
对于单个使用者操作的设备,向未授权使用者暴露详细信息会加剧对授权使用者的隐私的损害,并且因此通常使用密码、指纹读取器和进入码来阻止未授权使用者的访问。
不幸的是,这样的使用者ID输入、密码、指纹读取、进入码和其他此类额外ID或安全操作费时且不方便,并且降低了使用此类设备的愉悦性和效率。
简要概述
对于某些实施例,用于区分触摸屏使用者的方法包括检测来自至少一个使用者的触摸事件、生成与该触摸事件相关的振动声学波形信号、将该振动声学波形信号转换成与该振动声学波形信号不同的至少一个转换后的波形信号、从转换后的波形信号中提取区别特征、以及使用所提取的区别特征将该触摸事件与特定使用者相关联。
对于某些实施例,用于区分触摸屏使用者的装置包括被配置成用于检测来自至少一个使用者的触摸事件的触敏表面、被配置成用于响应于该触摸事件的发生而生成振动声学波形信号的传感器、被配置成用于将该波形信号转换成与该振动声学波形信号不同的至少一个转换后的波形信号的转换器、被配置成用于从转换后的波形信号中提取区别特征的特征提取器、以及被配置成用于使用该提取器所提取的区别特征将该振动声学波形信号与特定使用者相关联的分类单元。
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。
附图简要描述
所包括的附图仅为说明目的,并且仅用于提供用于所披露技术的可能结构和过程步骤的示例。这些附图决不会限制本领域技术人员在不脱离本披露的精神和范围的情况下对实施例做出的形式和细节上的任何修改。
图1是根据本发明的示例性实施例的用于基于振动声学信号的分类来区分触摸屏使用者的计算***的框图;
图2A是根据本发明的示例性实施例的示出了由第一使用者的触摸屏事件所生成的时域信号的第一使用者的波形信号的曲线图;
图2B是根据本发明的示例性实施例的包括图2A的波形信号的频谱的第一使用者的转换后的波形信号;
图3A是根据本发明的示例性实施例的示出了由第二使用者的触摸屏事件所生成的时域信号的第二使用者的波形信号的曲线图;
图3B是根据本发明的示例性实施例的包括图3A的波形信号的频谱的第二使用者的转换后的波形信号;以及
图4是根据本发明的示例性实施例的展示了可被执行用于区分触摸屏使用者的过程的流程图。
详细描述
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披露。因此,对于本领域技术人员而言将明显的是可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。在其他实例中,为了避免不必要地模糊本披露,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,在附图中以说明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披露,但应理解的是,这些示例不是限制性的,这样使得在不脱离本披露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,包括如过程、装置、***、设备、方法、计算机可读介质(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)、或如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。
所披露的实施例可以包括用于基于源自触摸事件声学和机械冲击的特征的表征来区分触摸屏使用者的方法,并且包括在触敏表面上检测触摸事件、使用检测此类触摸事件的至少一个传感器生成振动声学波形信号、将该波形信号转换成至少一个域信号、从所述域信号中提取多个区别特征、以及对所述多个特征进行分类从而将该域信号的特征与特定使用者相关联。
所披露的实施例可以包括用于区分触摸屏设备的触摸屏使用者的***。这些***可以包括处理器和一个或多个所存储的指令序列,当由该处理器执行时,这些指令致使该处理器在触敏表面上检测触摸事件、使用检测此类触摸事件的至少一个传感器生成振动声学波形信号、将该波形信号转换成至少一个域信号、从所述域信号中提取多个区别特征、以及对所述多个特征进行分类从而将该域信号的特征与特定使用者相关联。
所披露的实施例可以包括用于区分触摸屏设备的触摸屏使用者的装置。该装置可以包括用于检测来自至少一个使用者的触摸事件的触敏表面、从此类触摸事件生成振动声学波形信号的至少一个传感器、用于将该波形信号转换成至少一个域信号的转换器、用于从所述域信号中提取多个区别特征的特征提取器、以及使用所述提取器的这些区别特征将该域信号的特征与特定使用者相关联的分类单元。
所披露的实施例可以包括具有用于提供社交信息的一个或多个指令序列的机器可读介质,当由一个或多个处理器执行时,这些指令致使该一个或多个处理器在触敏表面上检测触摸事件、使用检测此类触摸事件的至少一个传感器生成振动声学波形信号、将该波形信号转换成至少一个域信号、从所述域信号中提取多个区别特征、以及对所述多个特征进行分类从而将该域信号的特征与特定使用者相关联。该域信号可以是时域信号或频域信号。
一般情况下,当使用者触摸配备有触摸屏的计算***(例如,智能电话)的触摸屏(即,物理冲击)时,机械力被施加到触摸屏上(导致在触摸屏上并穿过其传播的机械力)以及任何接触组件(例如,设备底盘、电子主板、外壳)上。这些机械振动可以被多种传感器(包括冲击传感器、振动传感器、加速度计、应变计、或声学传感器如电容传声器、压电传声器、MEMS传声器等)中的至少一个所捕获。
一旦传感器已经捕获了与机械振动相关联的振动声学信号,该信号可以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、零交叉、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)。
许多触摸屏技术能够将触摸事件的若干方面(如形状、大小、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。进一步地,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,使用者触摸触摸屏的方式也会是有区别的(例如,哪个手指、手指的哪一部分、有多平、有多用力)。因此,振动声学特征和触摸特征包含可以为不同使用者的特性的属性。
因此,可以在配置有触摸屏的计算***中包括分类器,该分类器在接收到触摸事件后确定哪个使用者正在操作计算***或该使用者是否已被授权或是否具有任何个性化特征。关于标识哪个使用者正在操作设备,任何单个事件可能并不能产生足够的置信度。因此,直到达到足够水平的置信度时分类器才可以保留结论,或者可以在预定时期或事件时(例如,10次触摸后、2分钟后、当进入特权应用时)强迫做出最佳猜测。
对于某些实施例,分类器可以包括被配置成用于不断对振动声学数据进行采样并将其存入缓冲区的感应***。缓冲区可以具有许多时长,如,例如,50毫秒。分类器可与被配置成用于等待触摸事件发生的触摸屏(或触敏表面)相耦合。任何数量的触摸技术对触摸屏都是可能的。当触摸屏检测到触摸事件时,它触发转换、特征提取和分类过程。
当检测到该触摸事件时,从振动声学缓冲区检索数据。因为触摸屏可能有某些时延,所以通常有必要在缓冲区中向后查看以找到与触摸冲击相对应的振动声学波形(例如,如果触摸屏具有20ms的时延,可能有必要在缓冲区中向后查看20ms以找到相对应的振动声学事件)。可以将缓冲区的全部或部分保存并传递到下一个过程。
来自传感器的波形是振动声学信号的时域表示。转换期间,将该信号转换成其他形式。这包括对波形进行滤波并变换成其他形式,包括频域表示。提取期间,对触摸屏控制器数据和振动声学数据进行分析,并提取表征不同使用者的特征。对于振动声学数据,为信号的全部表示计算特征。然后,将这些特征传递给分类器,该分类器使用该信息以使用者对触摸事件进行标记(除了触敏表面报告的任何因素之外,例如,X/Y位置、长/短轴、压力等)。然后,可以将增强的触摸事件传递给操作***(OS)或最终使用者应用,从而基于触摸事件来关联使用者。
图1是根据本发明的实施例的用于基于振动声学信号的分类来区分不同使用者的计算***10的框图。该实施例的计算***10可以具有OS,并且可以运行各种类型的服务或应用(被称为app)。计算***10还可以配备有可允许连接至通信网络的远程通信能力。这种计算***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***10可包括触摸屏100、触摸事件检测器110、振动声学分类器120和OS130。触摸事件检测器110可以与一个或多个传感器相关联。该一个或多个传感器可被安装在计算***10内部的任何数量的位置上,包括但不限于底盘、触摸屏、主板、PCB、显示器、面板和外壳。
当使用者使用手指触摸触摸屏100的表面时,触摸事件在空气中产生振动声学响应并且还在接触表面(例如,触摸屏、外壳、设备底盘)内部产生机械振动。本发明的某些实施例可以利用具有一个或多个传感器(例如,一个用于空中声学,而一个用于机械振动,还可称作结构声学)的振动声学信号的来源两者。可以使用若干传感器类型,包括例如压电弯曲元件、压电薄膜、加速度计(例如,线性可变差动变压器(LVDT)、电位计式、可变磁阻、压电式、压阻式、电容式、伺服(力平衡)、MEMS、位移传感器、速度传感器、振动传感器、陀螺仪、接近传感器、电传声器、水听器、电容传声器、驻极体电容传声器、动态传声器、带式传声器、碳粒传声器、压电传声器、光纤传声器、激光传声器、液体传声器、MEMS传声器。
许多触摸屏计算***内置传声器和加速度计(例如,用于语音和输入感应)。这些可以在不需要附加传感器的情况下使用,或可以与专门的传感器协同工作。
传感器可以捕获波形,波形是振动声学信号的时域表示。信号可被转换成其他形式。这包括对波形进行滤波(例如,卡尔曼滤波、指数移动平均、2kHz高通滤波、一欧罗滤波(OneEurofilter)、Savitzky-Golay滤波)。它还包括变换成其他表示(例如,小波变换、导数),包括频域表示(例如,频谱图、周期图、平均周期图法、傅立叶变换、最小二乘频谱分析、韦氏法、离散余弦变换(DCT)、快速折叠算法)。
以下触摸特征的可用性取决于所使用的触摸屏技术。分类器可以不使用这些特征和使用这些特征的全部或部分。这些特征可以包括触摸接触的位置(2D、或在曲面玻璃或其他非平面几何形状情况下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***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。这可能类似于法向应力——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)、触摸的扫频阻抗(如果使用扫频电容式触摸屏)、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)、手姿势的图像(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)。应注意的是,计算阶段还可以计算短时间段内上述特征中的一个或多个特征的导数,例如,触摸速度和压力速度。
对于某些实施例,信号的时域和频域表示(包括此类表示的一阶、二阶和三阶导数)可用作特征。对于某些实施例,时域和频域表示的滤波后的版本以及此类滤波后的版本的一阶、二阶和三阶导数也可被用作特征。
可在信号的时域和频域表示(包括此类表示的一阶、二阶和三阶导数)上并且进一步地在时域和频域表示的滤波后的版本以及此类滤波后的版本的一阶、二阶和三阶导数上计算以下特征:平均值、标准偏差、标准偏差(经整体振幅归一化)、方差、偏斜度、峰度、和、绝对值和、均方根(RMS)、峰值因数、离差、熵、功率和、质心、变差系数、交叉相关(即,滑动点积)、零交叉、季节性(即,周期性变化)、和DC偏置。可以使用以下方法执行用于一组已知标本信号的模板匹配得分:卷积、逆滤波匹配技术、和平方差(SSD)、动态时间规整、和弹性匹配。
可在频域表示(包括此类表示的一阶、二阶和三阶导数)上并且进一步地在频域表示的滤波后的版本以及此类滤波后的版本的一阶、二阶和三阶导数上计算以下特征:频谱矩心、频谱密度、球谐函数、总平均频谱能量、频带能量比(例如,对于每一倍频程)、对数频谱频带比(例如,对于每一对倍频程以及每一对三倍频程)、附加振动声学特征、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)、和频率相位(例如,如FFT所生成的)。
对于某些实施例,可在整个缓冲区(例如,1ms)的内容上并且还为子区域(例如,围绕波形的波峰、波形的末尾)计算所有上述特征。对于某些实施例,可以将所有上述振动声学特征组合用于形成混合特征,如例如比(例如,零交叉/频谱矩心)或差(零交叉——频谱矩心)。
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。
对于某些实施例,可以例如通过投票方案组合来自若干不同分类器的结果。还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。
触摸屏100是电子视觉显示器,并且还作为提供补充或替代键盘、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***上执行特定功能的服务或应用的图形表示。使用使用者的手指部分(包括惯常的指尖)触摸交互式元件致使OS130激活与适合于所标识的使用者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。由于骨密度、胖瘦感、皮肤质量、BMI等的差异,不同使用者的手指具有不同的振动声学属性。单个数字包含不同部分,如一个或多个指节、指尖、指垫和指甲。在激活触摸屏时倾向于使用相同手指的使用者可增加正确使用者标识的可能性。
指尖包括手指末端的手掌方面上的多肉块以及直到远端指间关节的手指侧面。它还包括手指的最顶端(即,远节指骨的远端尖端)。然而,指甲可能不被作为指尖定义部分包括在实施例中,因为这是独特的解剖学特征和区域。
指甲可能包括角蛋白(或人工指甲材料)的所有部分、覆盖手指的末端指骨的背侧方面的喇叭状包层。指关节可以包括围绕人手指的多骨关节的直接区域,包括拇指关节、以及主要和次要指节两者。多骨区域可以在围绕掌指关节和指间关节的1cm半径范围内。
当对象敲击某种材料时,振动声学波通过该材料或沿着该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。对应的使用者手指的振动声学特性及其对应的独特解剖学特性为每个使用者产生独特的响应。
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区分哪个使用者激活了触摸屏。
OS130运行计算***,这样使得可以符合振动声学信号的分类和相应使用者来激活功能。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每个使用者进行分类从而区分多个使用者的分类单元128。
分割单元122可以被配置成用于对振动声学信号进行采样,例如,在96kHz的采样率时使用振动声学信号的4096个样本的滑动窗口。转换单元124可以被配置成用于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可以被配置成用于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、和零交叉。
特征提取单元126可以被配置成用于从振动声学信号的频域表示计算一系列特征,如冲击波形的基本频率。分类单元128可以被配置成用于使用这些特征来对振动声学信号进行分类从而区分什么使用者生成了触摸事件,这样使得计算***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使用者有关的功能。为了帮助分类操作,使用者可以为振动声学分类器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。
图2A和图2B展示了示例性波形信号以及第一使用者的触摸事件的相应的频谱。图3A和图3B展示了示例性波形信号以及不同于第一使用者的第二使用者的触摸事件的相应的频谱。对于以不同冲击动作激活触摸屏的不同使用者来说,如图2A和图2B所示的与第一使用者和与第二使用者相关联的波形的差异与图3A和3B相比是典型的。
图4是根据本发明的示例性实施例的展示了可被执行用于区分触摸屏使用者的过程的流程图。如框402中所示,当使用者触摸触摸屏时,检测到触摸事件。触摸事件蕴含振动声学信号。在框404,基于在框402中的触摸事件的检测生成波形。在框406,将在框404中所生成的波形转换成另一形式。在框408,提取区别特征。在框410,将所提取的特征与特定使用者相关联。在常见架构中,其中有用于执行或帮助执行图4中所示的操作的算法的数据存储设备包括存储指令的主体(例如,软件)的机器可读介质。这些指令还可以在执行期间完全或至少部分地驻留在主机设备(例如,蜂窝电话、信息亭、或膝上型计算机)的存储器内或处理器内(例如,在处理器的高速缓冲存储器内)。存储器和处理器还构成机器可读介质。
本披露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,本披露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(如由编译器所产生的)和文件(其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 (21)
1.一种用于基于源自触摸事件声学和机械冲击的特征的表征来区分触摸屏使用者的装置;该装置包括:
一个触敏表面,该触敏表面用于检测来自至少一个使用者的一个触摸事件;
至少一个传感器,该传感器从此类触摸事件生成一个振动声学波形信号;
一个转换器,该转换器用于将该波形信号转换成至少一个域信号;
一个特征提取器,该特征提取器用于从所述域信号中提取多个区别特征;以及
一个分类单元,该分类单元使用所述提取器的这些区别特征将该域信号的特征与一个特定使用者相关联。
2.如权利要求1所述的装置,其中,所述至少一个传感器检测一个触摸事件的声学效果。
3.如权利要求1所述的装置,其中,所述至少一个传感器检测一个触摸事件的机械冲击效果。
4.如权利要求1所述的装置,其中,所述域信号是一个时域信号。
5.如权利要求1所述的装置,其中,所述域信号是一个频域信号。
6.如权利要求1所述的装置,其中,所述多个区别特征至少部分地取决于用于创建一个触摸事件的一根手指的物理特性。
7.如权利要求1所述的装置,其中,所述多个区别特征至少部分地取决于用于创建一个触摸事件的冲击的大小。
8.如权利要求1所述的装置,其中,所述至少一个传感器检测由所述触摸事件所发起的多个机械振动。
9.如权利要求1所述的装置,其中,所述至少一个传感器包括取自下组传感器的一个传感器,该组传感器由以下各项组成:冲击传感器、振动传感器、加速度计、应变计、压电设备和声学传感器。
10.如权利要求1所述的装置,其中,基于取自下组的一个特性的至少一个计算提取所述多个区别特征,该组由以下各项组成:平均值、标准偏差、方差、偏斜度、峰度、和、均方根、峰值因数、离差、熵、功率和、质心、变差系数、交叉相关、零交叉、季节性、DC偏置、频谱矩心、频谱密度、球谐函数、频谱能量、频带能量比、谱带比、倒谱系数和快速傅立叶变换内容。
11.一种用于基于源自触摸事件声学和机械冲击的特征的表征来区分触摸屏使用者的计算机实现的方法;该方法包括以下步骤:
在一个触敏表面上检测一个触摸事件;
使用检测此类触摸事件的至少一个传感器生成一个振动声学波形信号;
将该波形信号转换成至少一个域信号;
从所述域信号中提取多个区别特征;以及
对这些特征进行分类从而将该域信号的特征与一个特定使用者相关联。
12.如权利要求11所述的方法,其中,所述生成步骤使用所述至少一个传感器用于检测一个触摸事件的声学效果。
13.如权利要求11所述的方法,其中,所述生成步骤使用所述至少一个传感器用于检测一个触摸事件的机械冲击效果。
14.如权利要求11所述的方法,其中,所述域信号是一个时域信号。
15.如权利要求11所述的方法,其中,所述域信号是一个频域信号。
16.如权利要求11所述的方法,其中,所述多个区别特征至少部分地取决于用于创建一个触摸事件的一根手指的物理特性。
17.如权利要求11所述的方法,其中,所述多个区别特征至少部分地取决于用于创建一个触摸事件的冲击的大小。
18.如权利要求11所述的方法,其中,所述至少一个传感器检测由所述触摸事件所发起的多个机械振动。
19.如权利要求11所述的方法,其中,所述至少一个传感器包括取自下组传感器的一个传感器,该组传感器由以下各项组成:冲击传感器、振动传感器、加速度计、应变计、压电设备和声学传感器。
20.如权利要求11所述的方法,其中,基于取自下组的一个特性的至少一个计算提取所述多个区别特征,该组由以下各项组成:平均值、标准偏差、方差、偏斜度、峰度、和、均方根、峰值因数、离差、熵、功率和、质心、变差系数、交叉相关、零交叉、季节性、DC偏置、频谱矩心、频谱密度、球谐函数、频谱能量、频带能量比、谱带比、倒谱系数和快速傅立叶变换内容。
21.一种计算机可读介质,该计算机可读介质包含用于使用所检测的多个触摸屏事件将多个特定使用者与多个此类事件相关联的多个指令,其中,由一个处理器执行这些程序指令引起该处理器执行以下步骤:
使用检测此类触摸事件的至少一个传感器生成一个振动声学波形信号;
将该波形信号转换成至少一个域信号;
从所述转换后的波形信号中提取多个区别特征;以及
对这些特征进行分类从而将该域信号的特征与一个特定使用者相关联。
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EP3191924B1 (en) | 2020-07-08 |
US10599251B2 (en) | 2020-03-24 |
EP3191924A1 (en) | 2017-07-19 |
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US20160077615A1 (en) | 2016-03-17 |
EP3191924A4 (en) | 2018-04-18 |
US20170024055A1 (en) | 2017-01-26 |
WO2016040010A1 (en) | 2016-03-17 |
CN105426713B (zh) | 2018-10-16 |
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