CN106095099B - 一种用户行为动作检测识别方法 - Google Patents

一种用户行为动作检测识别方法 Download PDF

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CN106095099B
CN106095099B CN201610410039.XA CN201610410039A CN106095099B CN 106095099 B CN106095099 B CN 106095099B CN 201610410039 A CN201610410039 A CN 201610410039A CN 106095099 B CN106095099 B CN 106095099B
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田峰
刘薇
陈建新
周亮
杨震
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Nanjing Post and Telecommunication University
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    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
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    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
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    • G06F3/0346—Pointing devices displaced or positioned by the user, e.g. mice, trackballs, pens or joysticks; Accessories therefor with detection of the device orientation or free movement in a 3D space, e.g. 3D mice, 6-DOF [six degrees of freedom] pointers using gyroscopes, accelerometers or tilt-sensors

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Abstract

本发明涉及一种用户行为动作检测识别方法，通过三轴加速度传感器实现了三维加速度的数据采集，并利用小波变换滤波算法对原始数据进行滤波处理，提高了可靠性。在此基础上，本发明利用主成分分析法有效的优化了数据集，同时比较了四种不同的模式识别分类算法和三种分类模型寻优算法，最终获得最优参数寻优算法与最佳分类器，实现针对用户行为动作的检测，实现了用户行为动作的有效识别，并针对异常行为进行报警；并且实验结果证明，该方法具有准确度高，相对计算复杂度低和实时性好等优点。

Description

一种用户行为动作检测识别方法
技术领域
本发明涉及一种用户行为动作检测识别方法，属于无线体域网(Wireless BodyArea Networks,WBAN)技术领域。
背景技术
利用可穿戴设备识别人体活动是当前研究热点，利用一个或多个可穿戴设备佩戴在人体部位监测，识别的行为主要包括日常活动识别(如站起、步行、跑步或坐下的等基本动作)体育运动、老年人跌倒监测或婴幼儿病症状况等。可穿戴式无线传感设备是一种基于移动计算的新概念***，这种新概念大概是从上世纪60年代源起。从90年代以后，随着科技的发展，集成电路大规模地已迅猛的速度发展，也表明对可穿戴设备的研究进入了一个全新的时代。可穿戴设备的应用领域非常广泛，它可以简单的理解为无线传感器局域网，即无线传感局域网在实验个体上的实现。基于此可以明确的说，在不同的领域都需要有可穿戴设备的需求。在日常生活中，其实大多数的人们已经同时携带了很多个无线传感设备了，如果将这样庞大的网络终端群体利用起来，组成一个庞大的无线传感网，将带来不可估计的方便效用。对于一些危险的工作行业和娱乐项目，如勘探、登山、救援等等，如果在参与者身上佩戴一些可穿戴设备，可以及时获取信息，减少牺牲。
MEMS微型机械体系，主要在超精密仪器研究领域上使用，一般都在微米级别。MEMS能将多个传感器集成于一体，或者，MEMS会形成微传感器的阵列，即使它们的功能和敏感方向都不同。甚至把多种功能的器件集成在一起，形成微机电***元件。再将微机电***元件通过一定的功能组成复杂的微***。通过将微型机电***集成了微执行器、微传感器以及微电子器件，能够达到高可靠性和稳定性。MEMS传感器的主要特点是有较小的体积，较低的成本，较好的可靠性，较低的功耗，在数字化、智能化方面表现优势，能够达到较大的测量范围。随着电子科技的进步，各种新兴的原理。结构、材料和工艺即将随着MEMS传感器的优化而涌出，可以说MEMS传感器的发展前景大好。
当前的网络模型适合用来构建可穿戴式无线网络的技术主要有Bluetooth和ZigBee等。这两种无线技术均属于IEEE802.15家族，工作于ISM频段。无线传感器网络(Wireless Sensor Network,WSN)是由监测区域内大量的传感器节点组成，通过无线通信方式形成的一个多跳的自组织网络***，其目的是协作地感知、采集和处理网络覆盖区域中感知对象的信息，并发送给观察者。本方法进行数据采集模块的操作平台，即Tiny OS操作***，用于向Shimmer平台烧录程序和数据采集。
模式识别(Pattern Recognition,PR)，也叫行为识别，是一种基于计算机计算，通过数学算法研究，使计算机能够实现对样本的识别和分析，达到能够自主处理和判断。其中“模式”指的是实验环境信息等。随着计算机发展和人工智能的快速发展，人类对模式识别算法所进行融合和优化，使得构建的模型能达到更高的识别分类精度和泛化能力。模式识别关于处理问题的性质，以及解决问题的方法等方面，又被分为了有监督模式识别(Supervised-Classification)分类和无监督模式识别(Unsupervised-Classification)分类。这两者的主要区别在于两者用来做实验的样本是否提前先知道。无监督模式识别，主要的特征就是，不是将实验样本的描述特征量和进行识别分类的判断属性，进行统一关联，来进行分类模型的建设。比如主成份分析法(Principal Component Analysis，PCA)，它是通过在实验样本的特征量集合当中，找出差别最大的超平面，进行属性分类。这种识别方法就被称作无监督的模式识别分类。显然，有监督的模式识别需要提前知道样本所属的类别，也就表示它可能需要大量提前知道分类类别属性的样本来支持构建模，可是在实际运用中，这是很难达到的。这就引出本发明对无监督的模式识别分类方法的深入研究有很大的意义了。
如今对人工智能研究的不断扩大，然而大多是基于单个或少数实验对象进行多传感节点检测，较少考虑其实用性、推广性和在非实验室场合的运用。对于一些特殊应用领域如公共场合、监狱***。对于低功耗、带宽受限的传感网中，多节点、多监测对象，同时同步地进行行为识别，则需要对传感***和分类算法的能效性进行优化。
发明内容
本发明所要解决的技术问题是提供一种采用全新设计架构，能够在低功耗、带宽受限的传感网中，对多节点、多对象进行定位，以及对生理特征、行为异常进行判断，同时满足低成本、易部署、高速率通信等要求的用户行为动作检测识别方法。
本发明为了解决上述技术问题采用以下技术方案：本发明设计了一种用户行为动作检测识别方法，基于穿戴在目标用户身上的三轴加速度传感器，实现目标用户行为动作的识别，包括如下步骤：
步骤001.目标用户分别执行预设时长的N类指定行为动作，同时，通过三轴加速度传感器，按预设采集时刻频率，采集分别位于X轴、Y轴、Z轴上的加速度，构成校正采集数据，并且获得校正采集数据分别与N类指定行为动作的分类对应关系，构成对应校正验证数据，然后进入步骤002；
步骤002.针对校正采集数据进行去噪处理，然后进入步骤003；
步骤003.针对校正采集数据，按时间序列顺序、预设分组数据长度进行分组，并且按时间序列顺序，使得相邻分组之间具有预设百分比的数据重叠，获得校正采集数据所对应的各个采集数据组，并根据校正采集数据分别与N类指定行为动作的分类对应关系，获得校正采集数据所对应各个采集数据组分别与N类指定行为动作的分类对应关系，更新对应的校正验证数据，然后进入步骤004；
步骤004.分别针对校正采集数据所对应的各个采集数据组，获得采集数据组所对应的均值与方差，作为该采集数据组均值特征和方差特征，即获得校正采集数据所对应各个采集数据组的均值特征和方差特征，然后进入步骤005；
步骤005.针对校正采集数据所对应的各个采集数据组，按预设比例划分为两部分，获得校正采集数据对应第一部分的各个采集数据组，以及获得校正采集数据对应第二部分的各个采集数据组，并且校正采集数据所对应的两部分均包括N类指定行为动作所对应的各个采集数据组；然后进入步骤006；
步骤006.根据采集数据组的均值特征和方差特征，分别采用预设各个分类器，针对校正采集数据所对应第一部分的各个采集数据组进行分类，获得该各个分类器的分类结果，然后根据该各个采集数据组所对应的校正验证数据，分别获得该各个分类器的分类结果准确率，选择其中最大分类结果准确率所对应的分类器，作为最佳分类器，进入步骤007；
步骤007.采用预设各个参数寻优算法，分别针对最佳分类器进行参数寻优，获得各个参数寻优算法所分别对应的最佳分类器；根据采集数据组的均值特征和方差特征，分别采用该各个参数寻优算法所对应的最佳分类器，针对校正采集数据所对应第二部分的各个采集数据组进行分类，获得该各个分类器的分类结果，然后根据该各个采集数据组所对应的校正验证数据，分别获得该各个分类器的分类结果准确率，选择其中最大分类结果准确率所对应参数寻优算法对应的最佳分类器，将该参数寻优算法作为最优参数寻优算法，同时根据校正验证数据，获得该最大分类结果准确率下，N类指定行为动作分别所对应的分类结果范围，作为目标用户行为动作分类标准，然后进入步骤008；
步骤008.针对目标用户，通过三轴加速度传感器，实时采集分别位于X轴、Y轴、Z轴上的加速度，构成实际采集数据，再针对该实际采集数据进行去噪处理，接着按照步骤003的方法，针对实际采集数据进行分组，获得实际采集数据所对应的各个采集数据组，并分别获得该各个采集数据组的均值特征和方差特征，然后进入步骤009；
步骤009.根据实际采集数据所对应各个采集数据组的均值特征和方差特征，采用经过最优参数寻优算法进行参数寻优的最佳分类器，针对实际采集数据所对应各个采集数据组进行分类，获得分类结果，然后目标用户行为动作分类标准，判别实际采集数据中各个采集数据组所对应的行为动作，即获得目标用户的行为动作。
作为本发明的一种优选技术方案：所述步骤002中，采用小波变换滤波算法，针对校正采集数据进行去噪处理，以及所述步骤008中，采用小波变换滤波算法，针对实际采集数据进行去噪处理。
作为本发明的一种优选技术方案：所述步骤006中的预设各个分类器包括决策树分类器、朴素贝叶斯分类器、k-近邻分类器、支持向量机分类器。
作为本发明的一种优选技术方案：所述步骤006和步骤007之间，还包括步骤6-7如下所示，执行完步骤006之后进入步骤6-7，执行完步骤6-7之后进入步骤007；
步骤6-7.针对校正采集数据所对应第二部分的各个采集数据组依次进行归一化处理、盒线法处理，以及采用主成分分析法进行降维处理，然后进入步骤007。
作为本发明的一种优选技术方案：所述步骤007中的预设各个参数寻优算法包括网格寻优算法、遗传寻优算法、粒子群寻优算法。
作为本发明的一种优选技术方案：所述三轴加速度传感器为MEMS三轴加速度传感器SHIMMER。
本发明所述一种用户行为动作检测识别方法采用以上技术方案与现有技术相比，具有以下技术效果：本发明所设计的用户行为动作检测识别方法，在没有可提供这方面人体行为数据库的情况下，通过对分类算法的分析和优化，获得能够通过少量样本反应大多数样本特性的机器学习算法，实现用户行为动作的识别，并且所设计方法中，在基本分类模型的基础上增加了优化算法，自适应的选择分类模型的参数，对于不同的用户和不同分类行为做不同的调整，使该发明具有很广的应用，使得分类精确度较高，可以满足用户对异常行为监测的需求，不仅如此，所设计采用的三轴加速度传感器，对使用环境没有任何要求，弥补了视频传感器对监测环境固定的缺陷，可用于完全陌生的环境，且无需对异构传感器进行数据融合，不仅降低设备成本，也进一步降低网络通信数据量和计算复杂度，实际应用价值较高。
附图说明
图1是本发明所设计用户行为动作检测识别方法的架构原理示意图；
图2a至图2d是本发明设计实施例中通过三轴加速度传感器针对四类指定行为动作的加速度信号波形图；
图3是本发明设计实施例中四类指定行为动作采用小波变换滤波算法去噪前后的信号波形对比图；
图4是本发明设计实施例中四个分类器的分类结果准确率对比示意图；
图5是本发明设计实施例中校正采集数据所对应第二部分的各个采集数据组的高维特征向量的盒线图；
图6是本发明设计实施例中采用主成分分析法进行降维处理的帕累托分布图；
图7a和图7b是本发明设计实施例中采用网格寻优算法针对最佳分类器进行参数寻优的SVC结果2D示意图和3D示意图；
图8是本发明设计实施例中采用遗传寻优算法针对最佳分类器的参数寻优结果和各进化代数适应度曲线示意图；
图9是本发明设计实施例中采用粒子群寻优算法针对最佳分类器的参数寻优结果和各进化代数适应度曲线示意图。
具体实施方式
下面结合说明书附图对本发明的具体实施方式作进一步详细的说明。
如今对人工智能的研究不断扩大，然而大多是基于单个或少数实验对象进行多传感节点监测，较少考虑其实用性、推广性和在非实验室场合的运用。对于多节点、多监测对象同时监测并且行为识别的应用中，惯性传感器的效果优于其他类型传感器，并且对用户所在环境没有任何要求。至于一些特殊应用领域，本发明所设计的用户行为动作检测识别方法尤其适用于一些特殊场景，如公共场所、监狱管理***。在低功耗方面，该方法是基于无线传感器技术、低功耗无线通信技术和模式识别技术，根据所使用低功耗的MEMS无线传感器Shimmer传感平台采集数据，对数据进行处理分析，具有简便舒适佩戴的优势。该方法通过对单个测量单元采集数据，并进行数据预处理，避免多传感节点大量测量数据传输的能量需求。由于只需在右手手腕佩戴单个可穿戴无线惯性传感器，该方法对用户的佩戴要求很低，对用户的生活不会造成很大的影响。对于低功耗、带宽受限的传感网中，多节点、多监测对象同时同步行为识别，则需要对传感***和分类算法的能效性进行优化。
本发明属于模式识别技术领域。首先，利用小波变换(Wavelet Transform,WT)滤波算法对原始数据进行滤波处理，并通过主成分分析法(Principal Component Analysis,PCA)对特征向量集进行空间降维；然后，通过对多种分类算法，如决策树(Decision Tree,DT)分类算法、朴素贝叶斯(Bayes,NB)分类算法、k-近邻(K-Nearest Neighbors,KNN)算法、支持向量机(Support Vector Machine,SVM)进行特点分析和实际运算，选出最适合该应用的分类算法；最后，对分类算法进行进一步优化，降低数据集维数，减小计算复杂度，通过对比网格寻优法(Grid Search technique)、遗传算法(Genetic Algorithm,GA)、粒子群算法(Particle Swarm Optimization,PSO)对基础分类模型进行参数寻优，结果证明该方法具有准确度高，相对计算复杂度低，实时性好等优点。
本发明所设计一种用户行为动作检测识别方法，在实际应用过程当中，基于穿戴在目标用户身上的MEMS三轴加速度传感器SHIMMER，实现目标用户行为动作的识别，具体实施例中，可以将MEMS三轴加速度传感器SHIMMER佩戴于手腕上，并设计通过无线通信方式发送到PC机的串口，实现后续的数据处理，无线通信方式具体可以设计包括Bluetooth和ZigBee两种方式，其中，Bluetooth传送，是通过Bluetooth无线通信将佩戴者手腕上的传感器信息发送给电脑内置Bluetooth或者外置Bluetooth适配器，直接将数据传给电脑；ZigBee传送，是通过802.15.4无线电通信将佩戴者手腕上的传感器信息发送给基站(接收端)，接收端再通过Dock配件与电脑进行串行通信，将数据传送给电脑；其中具体应用过程，如图1所示，包括如下步骤：
步骤001.目标用户分别执行预设时长的N类指定行为动作，同时，通过MEMS三轴加速度传感器SHIMMER，按预设采集时刻频率，采集分别位于X轴、Y轴、Z轴上的加速度，构成校正采集数据，并且获得校正采集数据分别与N类指定行为动作的分类对应关系，构成对应校正验证数据，然后进入步骤002。
在具体实施例中，上述步骤001中，所述N类指定行为动作，可以设定为击打、脚踢、行走和坐着四类指定行为动作，这四类指定行为动作是生活中最具代表性的人体行为动作；针对上述四类指定行为动作，通过MEMS三轴加速度传感器SHIMMER，按预设采集时刻频率，采集分别位于X轴、Y轴、Z轴上的加速度，如图2a至图2d所示。
步骤002.采用小波变换滤波算法，针对校正采集数据进行去噪处理，如图3所示，在具体实施例中，针对击打、脚踢、行走和坐着四类指定行为动作，采用小波变换滤波算法去噪前后的信号波形对比图，然后进入步骤003。其中，将信号映射到小波域，根据噪声和噪声的小波系数在不同尺度上具有不同的性质和机理，对包含噪声的信号的小波系数进行处理；尽管在很大程度上小波变换滤波算法的去噪，在一定程度上可以看成是低通滤波，但是由于在小波变换在去噪之后还能成功地保留信号特征，所以在这一点上又优于传统的低通滤波器。
步骤003.针对校正采集数据，按时间序列顺序、预设分组数据长度进行分组，并且按时间序列顺序，使得相邻分组之间具有预设百分比的数据重叠，获得校正采集数据所对应的各个采集数据组，并根据校正采集数据分别与N类指定行为动作的分类对应关系，获得校正采集数据所对应各个采集数据组分别与N类指定行为动作的分类对应关系，更新对应的校正验证数据，然后进入步骤004。
具体实施例中，各个采集数据组的长度为64个样本，即0.5s长度(采样频率100HZ)，并且使得相邻分组之间具有50％的数据重叠，这样保证了每个窗口包括完整的行为周期的情况下，又不因为窗口太大噪声计算量大，时间空间消耗过大。
步骤004.分别针对校正采集数据所对应的各个采集数据组，获得采集数据组所对应的均值与方差，作为该采集数据组均值特征和方差特征，即获得校正采集数据所对应各个采集数据组的均值特征和方差特征，然后进入步骤005。
步骤005.针对校正采集数据所对应的各个采集数据组，按预设比例划分为两部分，获得校正采集数据对应第一部分的各个采集数据组，以及获得校正采集数据对应第二部分的各个采集数据组，并且校正采集数据所对应的两部分均包括N类指定行为动作所对应的各个采集数据组；然后进入步骤006。
步骤006.根据采集数据组的均值特征和方差特征，分别采用预设各个分类器，针对校正采集数据所对应第一部分的各个采集数据组进行分类，获得该各个分类器的分类结果，然后根据该各个采集数据组所对应的校正验证数据，分别获得该各个分类器的分类结果准确率，选择其中最大分类结果准确率所对应的分类器，作为最佳分类器，进入步骤6-7。
在具体实施例中，上述步骤006中，所述预设各个分类器包括决策树分类器(Decision Tree,DT)、朴素贝叶斯分类器(Bayes,NB)、k-近邻分类器(K-NearestNeighbors,KNN)、支持向量机分类器(Support Vector Machine,SVM)，即分别采用决策树分类器(Decision Tree,DT)、朴素贝叶斯分类器(Bayes,NB)、k-近邻分类器(K-Nearest Neighbors,KNN)、支持向量机分类器(Support Vector Machine,SVM)，针对校正采集数据所对应第一部分的各个采集数据组进行分类，获得该各个分类器的分类结果，如图4所示，可见具体实施例中，支持向量机分类器(Support Vector Machine,SVM)的效果最佳，因此，在此具体实施例中，将支持向量机分类器(Support Vector Machine,SVM)作为最佳分类器。
步骤6-7.针对校正采集数据所对应第二部分的各个采集数据组依次进行归一化处理、盒线法处理，以及采用主成分分析法(Principal Component Analysis,PCA)进行降维处理，然后进入步骤007。其中，如图5所示，具体实施例中校正采集数据所对应第二部分的各个采集数据组的高维特征向量的盒线图，并且如图6所示，可以从中看出，只要空间为10维的特征向量就可以基本达到高于95％的分类准确率，由此可以得出对维数进行有规则的减少可以达到几乎同样的效果，但是时间复杂度和算法复杂度、能源空间利用率就大大减少了，因此，针对校正采集数据所对应第二部分的各个采集数据组，采用主成分分析法进行降维处理的帕累托分布图。
步骤007.采用预设各个参数寻优算法，分别针对最佳分类器进行参数寻优，获得各个参数寻优算法所分别对应的最佳分类器；根据采集数据组的均值特征和方差特征，分别采用该各个参数寻优算法所对应的最佳分类器，针对校正采集数据所对应第二部分的各个采集数据组进行分类，获得该各个分类器的分类结果，然后根据该各个采集数据组所对应的校正验证数据，分别获得该各个分类器的分类结果准确率，选择其中最大分类结果准确率所对应参数寻优算法对应的最佳分类器，将该参数寻优算法作为最优参数寻优算法，同时根据校正验证数据，获得该最大分类结果准确率下，N类指定行为动作分别所对应的分类结果范围，作为目标用户行为动作分类标准，然后进入步骤008。
在具体实施例中，上述步骤007中，所述预设各个参数寻优算法包括网格寻优算法(Grid Search technique)、遗传寻优算法(Genetic Algorithm,GA)、粒子群寻优算法(Particle Swarm Optimization,PSO)，即分别采用网格寻优算法(Grid Searchtechnique)、遗传寻优算法(Genetic Algorithm,GA)、粒子群寻优算法(Particle SwarmOptimization,PSO)，分别针对最佳分类器进行参数寻优，获得各个参数寻优算法所分别对应的最佳分类器，其中，采用网格寻优算法(Grid Search technique)针对最佳分类器进行参数寻优如下，且所获寻优结果如图7a和图7b所示。
运行时间105.126012seconds；
交叉验证的准确率＝94.4202％；
类别标签为1,-1；
支持向量数目276；
所占训练集样本数目比例24.0628％(276/1147)；
整体分类准确率＝97.646％(1120/1147)；
第1类分类准确率＝96.9805％(546/563)；
第-1类分类准确率＝98.2877％(574/584)；
均方误差＝2.0322；
相关系数＝-1.#IND；
Accuracy＝97.646％(1120/1147)。
采用遗传寻优算法(Genetic Algorithm,GA)针对最佳分类器进行参数寻优如下，且所获寻优结果如图8所示。
运行时间262.875843seconds；
交叉验证的准确率＝94.4202％；
类别标签为1,-1；
支持向量数目192；
所占训练集样本数目比例16.7393％(192/1147)；
整体分类准确率＝97.1229％(1114/1147)；
第1类分类准确率＝97.1581％(547/563)；
第-1类分类准确率＝97.089％(567/584)；
均方误差＝5.08856(regression)；
相关系数＝-1.#IND；
Accuracy＝93.75％(360/384)(classification)。
采用粒子群寻优算法(Particle Swarm Optimization,PSO)针对最佳分类器进行参数寻优如下，且所获寻优结果如图9所示。
运行时间781.516576seconds；
支持向量数目213；
所占训练集样本数目比例18.5702％(213/1147)；
整体分类准确率＝97.4717％(1118/1147)；
第1类分类准确率＝96.9805％(546/563)；
第-1类分类准确率＝97.9452％(572/584)；
Accuracy＝97.4717％(1118/1147)。
步骤008.针对目标用户，通过MEMS三轴加速度传感器SHIMMER，实时采集分别位于X轴、Y轴、Z轴上的加速度，构成实际采集数据，再采用小波变换滤波算法，针对实际采集数据进行去噪处理，接着按照步骤003的方法，针对实际采集数据进行分组，获得实际采集数据所对应的各个采集数据组，并分别获得该各个采集数据组的均值特征和方差特征，然后进入步骤009。
步骤009.根据实际采集数据所对应各个采集数据组的均值特征和方差特征，采用经过最优参数寻优算法进行参数寻优的最佳分类器，针对实际采集数据所对应各个采集数据组进行分类，获得分类结果，然后目标用户行为动作分类标准，判别实际采集数据中各个采集数据组所对应的行为动作，即获得目标用户的行为动作。
本发明只使用单个传感器实时检测人体异常行为，并及时警告。再此基础上，通过数据处理和算法优化，提高分类准确率的同时降低时间和空间复杂度，具备较好的分类能力和泛化能力。整体算法兼顾了算法精确度与计算复杂度，能够满足实时性的要求，定位分类准确率达到了97.4％，在基本分类算法的基础上提高了11.98％。在行为异常判断模块，采用的是低功耗无线通信技术和MEMS传感器技术，运用三轴加速度Shimmer无线传感器节点，实现犯人异常行为监测。其中，人体身上的节点就是便携式监测单元，可以监测犯人的行为、生理参数和移动轨迹，后台处理***进行数据汇总、处理、分析、记录、存储和检索等。主要是根据实时人体行为等状况，通过算法判断异常事件发送给后台服务器，根据接收到的数据，利用算法进一步分析，判断异常行为类型，能够及时做出响应。即罪犯行为的判断，依据上述信息，判断罪犯的行为是否异常，并及时告知用户。
上面结合附图对本发明的实施方式作了详细说明，但是本发明并不限于上述实施方式，在本领域普通技术人员所具备的知识范围内，还可以在不脱离本发明宗旨的前提下做出各种变化。

Claims (6)

1.一种用户行为动作检测识别方法，基于穿戴在目标用户身上的三轴加速度传感器，实现目标用户行为动作的识别，其特征在于，包括如下步骤：
步骤001.目标用户分别执行预设时长的N类指定行为动作，同时，通过三轴加速度传感器，按预设采集时刻频率，采集分别位于X轴、Y轴、Z轴上的加速度，构成校正采集数据，并且获得校正采集数据分别与N类指定行为动作的分类对应关系，构成对应校正验证数据，然后进入步骤002；
步骤002.针对校正采集数据进行去噪处理，然后进入步骤003；
步骤003.针对校正采集数据，按时间序列顺序、预设分组数据长度进行分组，并且按时间序列顺序，使得相邻分组之间具有预设百分比的数据重叠，获得校正采集数据所对应的各个采集数据组，并根据校正采集数据分别与N类指定行为动作的分类对应关系，获得校正采集数据所对应各个采集数据组分别与N类指定行为动作的分类对应关系，更新对应的校正验证数据，然后进入步骤004；
步骤004.分别针对校正采集数据所对应的各个采集数据组，获得采集数据组所对应的均值与方差，作为该采集数据组均值特征和方差特征，即获得校正采集数据所对应各个采集数据组的均值特征和方差特征，然后进入步骤005；
步骤005.针对校正采集数据所对应的各个采集数据组，按预设比例划分为两部分，获得校正采集数据对应第一部分的各个采集数据组，以及获得校正采集数据对应第二部分的各个采集数据组，并且校正采集数据所对应的两部分均包括N类指定行为动作所对应的各个采集数据组；然后进入步骤006；
步骤006.根据采集数据组的均值特征和方差特征，分别采用预设各个分类器，针对校正采集数据所对应第一部分的各个采集数据组进行分类，获得该各个分类器的分类结果，然后根据该各个采集数据组所对应的校正验证数据，分别获得该各个分类器的分类结果准确率，选择其中最大分类结果准确率所对应的分类器，作为最佳分类器，进入步骤007；
步骤007.采用预设各个参数寻优算法，分别针对最佳分类器进行参数寻优，获得各个参数寻优算法所分别对应的最佳分类器；根据采集数据组的均值特征和方差特征，分别采用该各个参数寻优算法所对应的最佳分类器，针对校正采集数据所对应第二部分的各个采集数据组进行分类，获得该各个分类器的分类结果，然后根据该各个采集数据组所对应的校正验证数据，分别获得该各个分类器的分类结果准确率，选择其中最大分类结果准确率所对应参数寻优算法对应的最佳分类器，将该参数寻优算法作为最优参数寻优算法，同时根据校正验证数据，获得该最大分类结果准确率下，N类指定行为动作分别所对应的分类结果范围，作为目标用户行为动作分类标准，然后进入步骤008；
步骤008.针对目标用户，通过三轴加速度传感器，实时采集分别位于X轴、Y轴、Z轴上的加速度，构成实际采集数据，再针对该实际采集数据进行去噪处理，接着按照步骤003的方法，针对实际采集数据进行分组，获得实际采集数据所对应的各个采集数据组，并分别获得该各个采集数据组的均值特征和方差特征，然后进入步骤009；
步骤009.根据实际采集数据所对应各个采集数据组的均值特征和方差特征，采用经过最优参数寻优算法进行参数寻优的最佳分类器，针对实际采集数据所对应各个采集数据组进行分类，获得分类结果，然后目标用户行为动作分类标准，判别实际采集数据中各个采集数据组所对应的行为动作，即获得目标用户的行为动作。
2.根据权利要求1所述一种用户行为动作检测识别方法，其特征在于：所述步骤002中，采用小波变换滤波算法，针对校正采集数据进行去噪处理，以及所述步骤008中，采用小波变换滤波算法，针对实际采集数据进行去噪处理。
3.根据权利要求1所述一种用户行为动作检测识别方法，其特征在于：所述步骤006中的预设各个分类器包括决策树分类器、朴素贝叶斯分类器、k-近邻分类器、支持向量机分类器。
4.根据权利要求1所述一种用户行为动作检测识别方法，其特征在于：所述步骤006和步骤007之间，还包括步骤6-7如下所示，执行完步骤006之后进入步骤6-7，执行完步骤6-7之后进入步骤007；
步骤6-7.针对校正采集数据所对应第二部分的各个采集数据组依次进行归一化处理、盒线法处理，以及采用主成分分析法进行降维处理，然后进入步骤007。
5.根据权利要求1所述一种用户行为动作检测识别方法，其特征在于：所述步骤007中的预设各个参数寻优算法包括网格寻优算法、遗传寻优算法、粒子群寻优算法。
6.根据权利要求1所述一种用户行为动作检测识别方法，其特征在于：所述三轴加速度传感器为MEMS三轴加速度传感器SHIMMER。
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Granted publication date: 20181102

License type: Common License

Record date: 20231203

Application publication date: 20161109

Assignee: Jiangsu Dixin Metrology Testing Co.,Ltd.

Assignor: NANJING University OF POSTS AND TELECOMMUNICATIONS

Contract record no.: X2023980049330

Denomination of invention: A method for detecting and recognizing user behavior and actions

Granted publication date: 20181102

License type: Common License

Record date: 20231203

Application publication date: 20161109

Assignee: Nanjing Xinjia Network Technology Co.,Ltd.

Assignor: NANJING University OF POSTS AND TELECOMMUNICATIONS

Contract record no.: X2023980048653

Denomination of invention: A method for detecting and recognizing user behavior and actions

Granted publication date: 20181102

License type: Common License

Record date: 20231130

EE01 Entry into force of recordation of patent licensing contract

Application publication date: 20161109

Assignee: Nanjing yist Packaging Technology Co.,Ltd.

Assignor: NANJING University OF POSTS AND TELECOMMUNICATIONS

Contract record no.: X2023980050260

Denomination of invention: A method for detecting and recognizing user behavior and actions

Granted publication date: 20181102

License type: Common License

Record date: 20231207

EE01 Entry into force of recordation of patent licensing contract
EE01 Entry into force of recordation of patent licensing contract
EE01 Entry into force of recordation of patent licensing contract

Application publication date: 20161109

Assignee: Nanjing Shanyechu Agriculture and Forestry Technology Co.,Ltd.

Assignor: NANJING University OF POSTS AND TELECOMMUNICATIONS

Contract record no.: X2023980051072

Denomination of invention: A method for detecting and recognizing user behavior and actions

Granted publication date: 20181102

License type: Common License

Record date: 20231209

Application publication date: 20161109

Assignee: Nanjing Core Bamboo Technology Co.,Ltd.

Assignor: NANJING University OF POSTS AND TELECOMMUNICATIONS

Contract record no.: X2023980051070

Denomination of invention: A method for detecting and recognizing user behavior and actions

Granted publication date: 20181102

License type: Common License

Record date: 20231209

EE01 Entry into force of recordation of patent licensing contract

Application publication date: 20161109

Assignee: Jiangsu Liebao Network Technology Co.,Ltd.

Assignor: NANJING University OF POSTS AND TELECOMMUNICATIONS

Contract record no.: X2023980052022

Denomination of invention: A method for detecting and recognizing user behavior and actions

Granted publication date: 20181102

License type: Common License

Record date: 20231212

Application publication date: 20161109

Assignee: Jiangsu Chaoxin Technology Co.,Ltd.

Assignor: NANJING University OF POSTS AND TELECOMMUNICATIONS

Contract record no.: X2023980052021

Denomination of invention: A method for detecting and recognizing user behavior and actions

Granted publication date: 20181102

License type: Common License

Record date: 20231212

Application publication date: 20161109

Assignee: Speed Technology Co.,Ltd.

Assignor: NANJING University OF POSTS AND TELECOMMUNICATIONS

Contract record no.: X2023980051704

Denomination of invention: A method for detecting and recognizing user behavior and actions

Granted publication date: 20181102

License type: Common License

Record date: 20231212

Application publication date: 20161109

Assignee: Nanjing Zouma Information Technology Co.,Ltd.

Assignor: NANJING University OF POSTS AND TELECOMMUNICATIONS

Contract record no.: X2023980051703

Denomination of invention: A method for detecting and recognizing user behavior and actions

Granted publication date: 20181102

License type: Common License

Record date: 20231212

Application publication date: 20161109

Assignee: Nanjing Heyue Information Technology Co.,Ltd.

Assignor: NANJING University OF POSTS AND TELECOMMUNICATIONS

Contract record no.: X2023980051698

Denomination of invention: A method for detecting and recognizing user behavior and actions

Granted publication date: 20181102

License type: Common License

Record date: 20231212

EE01 Entry into force of recordation of patent licensing contract
EE01 Entry into force of recordation of patent licensing contract

Application publication date: 20161109

Assignee: Jiangsu Zhongye Information Technology Co.,Ltd.

Assignor: NANJING University OF POSTS AND TELECOMMUNICATIONS

Contract record no.: X2023980052151

Denomination of invention: A method for detecting and recognizing user behavior and actions

Granted publication date: 20181102

License type: Common License

Record date: 20231215

Application publication date: 20161109

Assignee: Hangzhou Yicui Information Technology Co.,Ltd.

Assignor: NANJING University OF POSTS AND TELECOMMUNICATIONS

Contract record no.: X2023980052106

Denomination of invention: A method for detecting and recognizing user behavior and actions

Granted publication date: 20181102

License type: Common License

Record date: 20231215

Application publication date: 20161109

Assignee: NANJING HAIWANG AUTO PARTS CO.,LTD.

Assignor: NANJING University OF POSTS AND TELECOMMUNICATIONS

Contract record no.: X2023980052100

Denomination of invention: A method for detecting and recognizing user behavior and actions

Granted publication date: 20181102

License type: Common License

Record date: 20231215

Application publication date: 20161109

Assignee: Jiangsu Ji'an Medical Equipment Co.,Ltd.

Assignor: NANJING University OF POSTS AND TELECOMMUNICATIONS

Contract record no.: X2023980052095

Denomination of invention: A method for detecting and recognizing user behavior and actions

Granted publication date: 20181102

License type: Common License

Record date: 20231214

Application publication date: 20161109

Assignee: Nanjing yingshixing Big Data Technology Co.,Ltd.

Assignor: NANJING University OF POSTS AND TELECOMMUNICATIONS

Contract record no.: X2023980052092

Denomination of invention: A method for detecting and recognizing user behavior and actions

Granted publication date: 20181102

License type: Common License

Record date: 20231214

Application publication date: 20161109

Assignee: NANJING CHANGJIANG INDUSTRIAL FURNACE TECHNOLOGY Co.,Ltd.

Assignor: NANJING University OF POSTS AND TELECOMMUNICATIONS

Contract record no.: X2023980052086

Denomination of invention: A method for detecting and recognizing user behavior and actions

Granted publication date: 20181102

License type: Common License

Record date: 20231214

Application publication date: 20161109

Assignee: ZIJIANG FURNACE INDUSTRY NANJING CO.,LTD.

Assignor: NANJING University OF POSTS AND TELECOMMUNICATIONS

Contract record no.: X2023980052079

Denomination of invention: A method for detecting and recognizing user behavior and actions

Granted publication date: 20181102

License type: Common License

Record date: 20231214

Application publication date: 20161109

Assignee: Nanjing Shuhui Technology Co.,Ltd.

Assignor: NANJING University OF POSTS AND TELECOMMUNICATIONS

Contract record no.: X2023980052024

Denomination of invention: A method for detecting and recognizing user behavior and actions

Granted publication date: 20181102

License type: Common License

Record date: 20231213

Application publication date: 20161109

Assignee: Nanjing Qinghong Network Technology Co.,Ltd.

Assignor: NANJING University OF POSTS AND TELECOMMUNICATIONS

Contract record no.: X2023980052023

Denomination of invention: A method for detecting and recognizing user behavior and actions

Granted publication date: 20181102

License type: Common License

Record date: 20231213

Application publication date: 20161109

Assignee: NANJING KEZHIPU EDUCATIONAL TECHNOLOGY Co.,Ltd.

Assignor: NANJING University OF POSTS AND TELECOMMUNICATIONS

Contract record no.: X2023980051911

Denomination of invention: A method for detecting and recognizing user behavior and actions

Granted publication date: 20181102

License type: Common License

Record date: 20231213

Application publication date: 20161109

Assignee: NANJING TIANHUA ZHONGAN COMMUNICATION TECHNOLOGY Co.,Ltd.

Assignor: NANJING University OF POSTS AND TELECOMMUNICATIONS

Contract record no.: X2023980051887

Denomination of invention: A method for detecting and recognizing user behavior and actions

Granted publication date: 20181102

License type: Common License

Record date: 20231213

Application publication date: 20161109

Assignee: Jiangsu Zhengjie Technology Achievement Transformation Group Co.,Ltd.

Assignor: NANJING University OF POSTS AND TELECOMMUNICATIONS

Contract record no.: X2023980051845

Denomination of invention: A method for detecting and recognizing user behavior and actions

Granted publication date: 20181102

License type: Common License

Record date: 20231213

EE01 Entry into force of recordation of patent licensing contract
EE01 Entry into force of recordation of patent licensing contract

Application publication date: 20161109

Assignee: Nanjing Fanyi Intelligent Technology Co.,Ltd.

Assignor: NANJING University OF POSTS AND TELECOMMUNICATIONS

Contract record no.: X2023980053773

Denomination of invention: A method for detecting and recognizing user behavior and actions

Granted publication date: 20181102

License type: Common License

Record date: 20231223

Application publication date: 20161109

Assignee: NANJING HUADONG ELECTRONICS VACUUM MATERIAL Co.,Ltd.

Assignor: NANJING University OF POSTS AND TELECOMMUNICATIONS

Contract record no.: X2023980053414

Denomination of invention: A method for detecting and recognizing user behavior and actions

Granted publication date: 20181102

License type: Common License

Record date: 20231222

Application publication date: 20161109

Assignee: Nanjing Hefeng Operation Management Co.,Ltd.

Assignor: NANJING University OF POSTS AND TELECOMMUNICATIONS

Contract record no.: X2023980053384

Denomination of invention: A method for detecting and recognizing user behavior and actions

Granted publication date: 20181102

License type: Common License

Record date: 20231222

Application publication date: 20161109

Assignee: NANJING DIXIN COORDINATE INFORMATION TECHNOLOGY CO.,LTD.

Assignor: NANJING University OF POSTS AND TELECOMMUNICATIONS

Contract record no.: X2023980053374

Denomination of invention: A method for detecting and recognizing user behavior and actions

Granted publication date: 20181102

License type: Common License

Record date: 20231222

EE01 Entry into force of recordation of patent licensing contract
EE01 Entry into force of recordation of patent licensing contract
EE01 Entry into force of recordation of patent licensing contract

Application publication date: 20161109

Assignee: NANJING CREATCOMM TECHNOLOGY CO.,LTD.

Assignor: NANJING University OF POSTS AND TELECOMMUNICATIONS

Contract record no.: X2023980054276

Denomination of invention: A method for detecting and recognizing user behavior and actions

Granted publication date: 20181102

License type: Common License

Record date: 20231227

Application publication date: 20161109

Assignee: NANJING WOYU ELECTROMECHANICAL CO.,LTD.

Assignor: NANJING University OF POSTS AND TELECOMMUNICATIONS

Contract record no.: X2023980054111

Denomination of invention: A method for detecting and recognizing user behavior and actions

Granted publication date: 20181102

License type: Common License

Record date: 20231227

Application publication date: 20161109

Assignee: NANJING YIZHIHENG SOFTWARE TECHNOLOGY Co.,Ltd.

Assignor: NANJING University OF POSTS AND TELECOMMUNICATIONS

Contract record no.: X2023980054071

Denomination of invention: A method for detecting and recognizing user behavior and actions

Granted publication date: 20181102

License type: Common License

Record date: 20231227