基于足底压力分布的步行行为感知关键技术研究
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摘要
基于人工智能的行为感知可以极大的拓展人类自身对于环境行为信息的加工和应用能力,它的研究可以推动环境智能化和服务人性化的发展,也可以推动公共场所安全监控与预警的智能化和自动化。行为感知研究是一个多学科交叉的领域,涉及到数据采集、机器学习、计算机视觉以及运动生物力学等学科,是一个非常广泛和复杂的课题,因此最近几十年来倍受广大研究者的关注。
行为感知从信息获取的方式来分,可以分为：基于视觉的方法和基于非视觉的方法。基于视觉的方法由于各种成像设备的普及,因此得到了很好的发展,但是视觉传感器易受遮挡、光照条件、视角、以及作用距离等因素的影响,同时基于视觉的行为感知还容易造成隐私浸入问题；而基于非视觉方法是则是一个很好的补充,近年来各种惯性传感器以及磁传感器纷纷被引入到行为感知领域,但是这类传感器通常都需要安装在被感知对象身上,这会引起一定程度的不舒适感。
本文从非视觉、非侵入、全天候行为感知系统的构建出发,以步行行为作为研究对象,以大面积柔性压力敏感地板系统为行为信息的载体,以行为感知中的目标分割、目标跟踪以及目标识别等要素为主线,来开展基于足底压力分布的行为感知研究,在以下几个主要方向进行了有益的探索：
第一、足底压力图像的采集、分析以及足迹分割研究。基于柔性力敏地板系统的足底压力图像采集系统具有高分辨率、高采样率、大面积的特点,因此在设计上采用模块化组网的方式,以此解决数据传输、面积拓展等问题。单帧压力图像由各个力敏传感点的瞬态压力值组成,与其它类型的图像一样,当中不仅存在高斯噪声、脉冲噪声,同时非常小的传感点间距以及其它一些工艺问题共同造成一类与时间相关的噪音,另外高采样频率使得去噪算法所能允许的执行时间有限,因此本文在前人工作的基础上,通过引入开关中值滤波算法来对噪音进行预检测,然后通过二维FIFO来实现时间立体窗内的均值滤波,通过上述改进,算法的执行时间大大缩短。足迹分割是步行行为感知研究的基础,本文针对足底压力图像中压力点离散分布的特点,提出以密度聚类的方式来实现不同数据区域块的划分,接着利用形状描述子来约束不同区域块的组合,最终实现不同足迹的分割。
第二、基于足底压力分布的步态识别研究。身份识别是行为感知系统中一个重要的子系统,基于足底动力学的步态识别具有隐蔽性强、作用距离远等优良特性,因此近年来发展很快。针对足底压力区域分布存在个体差异的现象,本文在传统基于地面反作用力(GRF)的步态识别工作基础之上,提取GRF驼峰形时间曲线上的关键特征点,然后以这几个特征点所对应时刻的足底压力分布来构建时空HOG (Histograms of Oriented Gradients)特征,这一特征不仅刻画了足底压力分布的区域特征,同时也体现了GRF曲线的时间变化特点。基于上述特征,最后采用基于RBF核函数的SVM作为分类器来实现步态识别。
第三、多行人足迹跟踪研究。针对人体步行运动的马尔科夫特性,本文采用对非线性、非高斯运动信号具有较好跟踪效果的粒子滤波器作为主要的跟踪框架,同时根据步行运动的基本特点,提出一个用于状态预测的运动模型,并针对停留等特殊行为单独建模,然后在跟踪过程中根据状态观测信息进行动态切换,最后以足迹分割产生的足印作为观察状态,并在贝叶斯决策思想的指导下,实现脚印轨迹的跟踪。
最后是基于运动学信息的异常步态检测研究工作。人的步行运动是一个精确而复杂的过程,其运动方式由中枢神经系统和反馈机制之间的动态相互作用所决定。一些老年疾病以及一些神经性疾病都会导致上述过程发生问题,其直接表现就是异常步态。本文以智能家居下的步行行为感知为前提条件,来实现日常生活中的步态监测,在算法研究过程中,以步态的运动学信息为基础,通过HMM来构建运动学参数的描述模型,最后通过HMM相似度测度来实现步态的分类。
综上所述,本文以构建面向智能监控、人机交互的应用系统为导向,针对步行行为感知研究的几个关键要素,如目标分割、目标跟踪、目标识别等,以足底压力分布为主要信息,开展了一些积极的探索和研究工作,希望相关研究成果能够为这一领域的深入发展提供有益的借鉴和参考。
Behavior perception based on artificial intelligence can considerably extend the capability of human itself in processing and utilizing behavior information of surrounding environment. Behavior perception research can push forward the development of intelligent environment and personalized service, and it can also propel the monitoring and warning of public safety to be more intelligent and automatic. Behavior perception research is a multidisciplinary area, which involves data mining, machine learning, computer vision and sports biomechanics. It is a quite wide and complex research area, which has attracts numerous scholars in recent years.
According to different methods of information acquisition, behavior perception can be divided into two categories:method by visual information and method by non-visual information. The visual method has got widely developed because of the popularization of different image sensing devices. However, visual behavior perception system is susceptible to many different factors, such as occlusion, lighting condition, view angle and distance; furthermore visual method may cause privacy invasion problem. On the other hand, non-visual method is a good complement to visual method. In recent years, different inertial and magnetic sensors have been introduced into the area of behavior perception, however, one major drawback for those sensors is that they have to be installed on the body of human beings, which may cause some degree of uncomfortable feelings.
This thesis starts from the construction of a non-visual, non-invasive and day-and-night behavior perception system. In this study, ambulation behavior was taken as major research objective; a large flexible force sensitive floor system was chosen as main carrier of behavior information; key elements in behavior perception system such as object segmentation, tracking and recognition were deemed as main themes. More specifically, the following sub-subjects were explored in this study:
Firstly, investigation of how to sample, analyze and segment footprints for plantar pressure image. The plantar pressure sampling system based on flexible force sensitive floor has the characteristics of high resolution, high sampling rate and large area. Therefore, a module-based method is developed to build a network to solve data transfer and area-extension problem. Single plantar pressure image is composed of instant pressure value of every pressure sensitive sensors in the array, it has gaussian and impulse noises just existed in other kinds of images and also have a kind of time-relevance noise which are caused by some kind of manufacturing problems of flexible sensors and little distance between sensor elements. Also high sampling rate determines that the execution time allowed for denoise algorithm is very short. Therefore in this study, starting from a previous algorithm, a switch-median algorithm was adopted to perform prefiltering of noise, and then a two-dimension FIFO technique is utilized to realize fast execution of average filtering in time-relevant three-dimension filter windows. By such measures, the overall execution time of denoise algorithm has been reduced to a great extent. Footprint segmentation is the prerequisite of ambulation behavior perception research, to address the discrete distribution feature of data points, a density-based algorithm is utilized to realize the division of different data points into different groups, and then under the constraint of footprint shape descriptors, different data groups are combined to realize the segmentation of different footprints.
Secondly, study of gait recognition based on plantar pressure distribution. As an important subsystem of behavior perception, gait recognition based on plantar kinetics has the characteristics of good concealment and long action distance. Therefore it has been developed quickly in recent years. As a biomechanic finding, plantar pressure distribution demonstrates somewhat personalized localization feature. According to this kind of phenomenon, a novel gait recognition method is proposed. In this method, several key points on the hump-shape GRF curve are firstly extracted and then the plantar pressure image corresponding to the moment of such points are utilized to construct spatio-temporal HOG feature, which not only describes the localization feature of plantar pressure distribution, but also embodies the time-variant feature of GRF curve. Based on such STHOG feature, a RBF-kernel based SVM algorithm was applied to realize the gait recognition in the end.
Thirdly, research of a multi-footprint tracking algorithm. Based on the markov feature of ambulation behavior of human beings, the particle filter which has good tracking performance for non-linear and non-gausian signal has been adopted to be the main tracking framework in this study. At the same time, according to the basic feature of ambulation behavior, an action mathematical model for state prediction is proposed and for specific actions such as stop, turn back, a separate model is built for it correspondingly. Then during tracking process, model switching is performed based on observing information. Finally, footprints generated during segmentation stage were used as observation state, and footprint tracking is realized under the guidance of bayesian decision rule.
Finally, study of detection algorithm for abnormal gait based on gait kinematics. Ambulation behavior of human beings is a complex and accurate process, which is determined by the dynamic interaction of central nervous system and feedback system of muscle. Many neurogenic and age-related diseases can cause such process to be problematic and a direct demonstration of such problems is abnormal gait. This study took ambulation behavior perception under intelligent dwelling house as the prerequisite to realize the daily gait monitoring of general public. The proposed algorithm utilized HMM to represent gait kinematics, and the gait classification is realized by HMM similarity measurement.
In summary, this thesis takes the construction of intelligent monitoring and human-computer interaction as research direction. To address several key components of behavior perception such as object segmentation, tracking and recognition, some useful exploration and jobs are performed based on plantar pressure distribution information. We hope our job can be a beneficial hint and reference to a wide and deep exploration of research area for ambulation behavior perception.
行为感知从信息获取的方式来分,可以分为：基于视觉的方法和基于非视觉的方法。基于视觉的方法由于各种成像设备的普及,因此得到了很好的发展,但是视觉传感器易受遮挡、光照条件、视角、以及作用距离等因素的影响,同时基于视觉的行为感知还容易造成隐私浸入问题；而基于非视觉方法是则是一个很好的补充,近年来各种惯性传感器以及磁传感器纷纷被引入到行为感知领域,但是这类传感器通常都需要安装在被感知对象身上,这会引起一定程度的不舒适感。
本文从非视觉、非侵入、全天候行为感知系统的构建出发,以步行行为作为研究对象,以大面积柔性压力敏感地板系统为行为信息的载体,以行为感知中的目标分割、目标跟踪以及目标识别等要素为主线,来开展基于足底压力分布的行为感知研究,在以下几个主要方向进行了有益的探索：
第一、足底压力图像的采集、分析以及足迹分割研究。基于柔性力敏地板系统的足底压力图像采集系统具有高分辨率、高采样率、大面积的特点,因此在设计上采用模块化组网的方式,以此解决数据传输、面积拓展等问题。单帧压力图像由各个力敏传感点的瞬态压力值组成,与其它类型的图像一样,当中不仅存在高斯噪声、脉冲噪声,同时非常小的传感点间距以及其它一些工艺问题共同造成一类与时间相关的噪音,另外高采样频率使得去噪算法所能允许的执行时间有限,因此本文在前人工作的基础上,通过引入开关中值滤波算法来对噪音进行预检测,然后通过二维FIFO来实现时间立体窗内的均值滤波,通过上述改进,算法的执行时间大大缩短。足迹分割是步行行为感知研究的基础,本文针对足底压力图像中压力点离散分布的特点,提出以密度聚类的方式来实现不同数据区域块的划分,接着利用形状描述子来约束不同区域块的组合,最终实现不同足迹的分割。
第二、基于足底压力分布的步态识别研究。身份识别是行为感知系统中一个重要的子系统,基于足底动力学的步态识别具有隐蔽性强、作用距离远等优良特性,因此近年来发展很快。针对足底压力区域分布存在个体差异的现象,本文在传统基于地面反作用力(GRF)的步态识别工作基础之上,提取GRF驼峰形时间曲线上的关键特征点,然后以这几个特征点所对应时刻的足底压力分布来构建时空HOG (Histograms of Oriented Gradients)特征,这一特征不仅刻画了足底压力分布的区域特征,同时也体现了GRF曲线的时间变化特点。基于上述特征,最后采用基于RBF核函数的SVM作为分类器来实现步态识别。
第三、多行人足迹跟踪研究。针对人体步行运动的马尔科夫特性,本文采用对非线性、非高斯运动信号具有较好跟踪效果的粒子滤波器作为主要的跟踪框架,同时根据步行运动的基本特点,提出一个用于状态预测的运动模型,并针对停留等特殊行为单独建模,然后在跟踪过程中根据状态观测信息进行动态切换,最后以足迹分割产生的足印作为观察状态,并在贝叶斯决策思想的指导下,实现脚印轨迹的跟踪。
最后是基于运动学信息的异常步态检测研究工作。人的步行运动是一个精确而复杂的过程,其运动方式由中枢神经系统和反馈机制之间的动态相互作用所决定。一些老年疾病以及一些神经性疾病都会导致上述过程发生问题,其直接表现就是异常步态。本文以智能家居下的步行行为感知为前提条件,来实现日常生活中的步态监测,在算法研究过程中,以步态的运动学信息为基础,通过HMM来构建运动学参数的描述模型,最后通过HMM相似度测度来实现步态的分类。
综上所述,本文以构建面向智能监控、人机交互的应用系统为导向,针对步行行为感知研究的几个关键要素,如目标分割、目标跟踪、目标识别等,以足底压力分布为主要信息,开展了一些积极的探索和研究工作,希望相关研究成果能够为这一领域的深入发展提供有益的借鉴和参考。
Behavior perception based on artificial intelligence can considerably extend the capability of human itself in processing and utilizing behavior information of surrounding environment. Behavior perception research can push forward the development of intelligent environment and personalized service, and it can also propel the monitoring and warning of public safety to be more intelligent and automatic. Behavior perception research is a multidisciplinary area, which involves data mining, machine learning, computer vision and sports biomechanics. It is a quite wide and complex research area, which has attracts numerous scholars in recent years.
According to different methods of information acquisition, behavior perception can be divided into two categories:method by visual information and method by non-visual information. The visual method has got widely developed because of the popularization of different image sensing devices. However, visual behavior perception system is susceptible to many different factors, such as occlusion, lighting condition, view angle and distance; furthermore visual method may cause privacy invasion problem. On the other hand, non-visual method is a good complement to visual method. In recent years, different inertial and magnetic sensors have been introduced into the area of behavior perception, however, one major drawback for those sensors is that they have to be installed on the body of human beings, which may cause some degree of uncomfortable feelings.
This thesis starts from the construction of a non-visual, non-invasive and day-and-night behavior perception system. In this study, ambulation behavior was taken as major research objective; a large flexible force sensitive floor system was chosen as main carrier of behavior information; key elements in behavior perception system such as object segmentation, tracking and recognition were deemed as main themes. More specifically, the following sub-subjects were explored in this study:
Firstly, investigation of how to sample, analyze and segment footprints for plantar pressure image. The plantar pressure sampling system based on flexible force sensitive floor has the characteristics of high resolution, high sampling rate and large area. Therefore, a module-based method is developed to build a network to solve data transfer and area-extension problem. Single plantar pressure image is composed of instant pressure value of every pressure sensitive sensors in the array, it has gaussian and impulse noises just existed in other kinds of images and also have a kind of time-relevance noise which are caused by some kind of manufacturing problems of flexible sensors and little distance between sensor elements. Also high sampling rate determines that the execution time allowed for denoise algorithm is very short. Therefore in this study, starting from a previous algorithm, a switch-median algorithm was adopted to perform prefiltering of noise, and then a two-dimension FIFO technique is utilized to realize fast execution of average filtering in time-relevant three-dimension filter windows. By such measures, the overall execution time of denoise algorithm has been reduced to a great extent. Footprint segmentation is the prerequisite of ambulation behavior perception research, to address the discrete distribution feature of data points, a density-based algorithm is utilized to realize the division of different data points into different groups, and then under the constraint of footprint shape descriptors, different data groups are combined to realize the segmentation of different footprints.
Secondly, study of gait recognition based on plantar pressure distribution. As an important subsystem of behavior perception, gait recognition based on plantar kinetics has the characteristics of good concealment and long action distance. Therefore it has been developed quickly in recent years. As a biomechanic finding, plantar pressure distribution demonstrates somewhat personalized localization feature. According to this kind of phenomenon, a novel gait recognition method is proposed. In this method, several key points on the hump-shape GRF curve are firstly extracted and then the plantar pressure image corresponding to the moment of such points are utilized to construct spatio-temporal HOG feature, which not only describes the localization feature of plantar pressure distribution, but also embodies the time-variant feature of GRF curve. Based on such STHOG feature, a RBF-kernel based SVM algorithm was applied to realize the gait recognition in the end.
Thirdly, research of a multi-footprint tracking algorithm. Based on the markov feature of ambulation behavior of human beings, the particle filter which has good tracking performance for non-linear and non-gausian signal has been adopted to be the main tracking framework in this study. At the same time, according to the basic feature of ambulation behavior, an action mathematical model for state prediction is proposed and for specific actions such as stop, turn back, a separate model is built for it correspondingly. Then during tracking process, model switching is performed based on observing information. Finally, footprints generated during segmentation stage were used as observation state, and footprint tracking is realized under the guidance of bayesian decision rule.
Finally, study of detection algorithm for abnormal gait based on gait kinematics. Ambulation behavior of human beings is a complex and accurate process, which is determined by the dynamic interaction of central nervous system and feedback system of muscle. Many neurogenic and age-related diseases can cause such process to be problematic and a direct demonstration of such problems is abnormal gait. This study took ambulation behavior perception under intelligent dwelling house as the prerequisite to realize the daily gait monitoring of general public. The proposed algorithm utilized HMM to represent gait kinematics, and the gait classification is realized by HMM similarity measurement.
In summary, this thesis takes the construction of intelligent monitoring and human-computer interaction as research direction. To address several key components of behavior perception such as object segmentation, tracking and recognition, some useful exploration and jobs are performed based on plantar pressure distribution information. We hope our job can be a beneficial hint and reference to a wide and deep exploration of research area for ambulation behavior perception.
引文
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