基于统计聚类方法的儿童下肢肌电信号周期识别

为运用肌电信号分析髋脱位儿童和正常儿童的差异,提出一种基于统计的聚类方法,识别步态中下肢肌电信号的周期起始时刻。使用非参数贝叶斯模型将肌电信号序列聚类为状态序列,并通过k均值聚类算法将该状态序列标记为肌肉活跃和不活跃两种状态,将肌肉活跃状态的起始时刻作为肌电信号周期的起始位置,并且利用窗函数方法提高预测准确性。实验结果表明,该方法对于预测正常儿童周期起始位置的识别误差较小,平均值为2.15%,并且在5%的置信度水平下与SampEN、SNEO和IP等检测算法相比具有较高的预测准确率。     

基于峰-谷分段积分算法的行走步态周期识别

将表面肌电信号(SEMS)作为信息源,提出一种基于峰-谷分段积分算法的人体行走步态周期识别方法。通过凌阳SPCE061A单片机对SEMS进行实时采集,使用RS232串行口与PC机进行通信,将SEMS的数字量信息传递给PC机。在Visual C++环境下建立步态周期识别界面,采集6位被测者行走时的8个下肢SEMS。实验结果表明,该方法能提高识别结果的准确性和可靠性。     

基于sEMG的行走步态非均匀特性及其对识别的影响

人体生理特性和运动特性是影响步态识别的重要因素。利用实验采集的下肢表面肌电信号,首先对肌电信号进行小波消噪及特征提取,然后构造支持向量机分类器进行分类与识别,并针对步态周期数据的非均匀性（非等时性）特性进行了详细讨论。结果表明,即使在匀速行走条件下,人体步态周期仍然存在一定的非均匀特性,且这一特点会影响步态识别的准确性。这对于进一步研究步态稳定性和步态识别率等具有一定的参考价值。     

GA-BP神经网络在下肢运动步态识别中的应用研究

为了提高下肢表面肌电信号步态识别的准确性,提出了一种基于遗传算法（GA）优化的BP神经网络分类器设计方法。首先,对采集的下肢表面肌电信号进行小波滤波及特征值提取,其次,构造基于GA优化的BP神经网络分类器,然后,以提取的表面肌电信号特征作为输入对分类器进行训练,最后利用训练好的分类器进行测试。实验结果表明,基于GA优化的BP神经网络分类器能成功识别下肢正常行走的五个步态,平均识别率达到98%以上,效果明显优于BP神经网络分类器的识别效果。     

利用多源运动信息的下肢假肢多模式多步态识别研究

运动状态识别对智能下肢假肢的控制非常关键,本文利用下肢表面肌电信号、腿部角度和足底压力信号在运动模式和步态分析中的优势和特点,对下肢假肢的多模式多步态识别进行研究.通过建立下肢运动信息系统,获取下肢多源运动信息.先提取下肢肌电信号的小波包能量作为特征,建立多个HMM对下肢假肢的运动模式进行识别;再根据大小腿和膝关节的角...     

基于肌电信号的无线通讯控制系统

表面肌电信号是肌肉动作产生的一种生物电信号,其中蕴含了与人体动作相关的运动意图信息.因此,通过对肌电信号的分析处理,可以获取人体的动作模式.基于肌电信号的研究在康复医学,假肢控制,生物医疗等方面具有巨大的应用前景.研究基于表面肌电信号的分类识别构建一个无线通讯控制系统,研究内容包括表面肌电信号的实验设计,分析方法介绍,手势动作获取,无线通讯系统搭建及实时界面显示控制系统的构建.通过对整个系统的试验调试,该系统操作简单,具有良好的实时性.该课题的研究工作能够为辅助手臂装置或残障人士轮椅控制提供新的模式.     

表面肌电采集与在线识别系统

设计了一套简易且分辨率高的表面肌电采集与在线识别系统;系统硬件部分包括信号两级放大、带通滤波、精密整流、16位AD转换芯片ADS1120、AVR单片机等部分;软件部分基于JAVA编程,具有实时滤波、显示并存储肌电信号、在线识别手部动作等功能;系统放大增益倍数为100～2 500可调,根据不同被试同一动作的肌电信息,微调放大倍数以减少个体差异;当放大倍数为1 000倍时,识别精度达0.3 μV;此外还设计了训练范式,根据被试的训练数据提取在线识别算法的参数,以提高识别准确率;实验结果表明:该系统具有较好的稳定性,能够准确识别四类手部动作,平均识别率达84.37%。     

基于有监督Kohonen神经网络的步态识别

表面肌电信号随着时间的变化而改变,这将影响运动模式的分类精度.传统人体下肢假肢运动模式的识别算法不能保证在整个肌电控制时间内达到对运动模式的有效识别.为了解决这些问题,本文提取步态初期200ms的信号的特征值,将无监督和有监督的Kohonen神经网络算法应用到大腿截肢者残肢侧的步态识别中,并与传统BP神经网络进行了对比.结果表明,有监督的Kohonen神经网络算法将五种路况下步态的平均识别率提高到88.4%,优于无监督的Kohonen神经网络算法和BP神经网络.     

高斯径向基函数重构特征对表面肌电信号识别

针对在不同动作模式下对表面肌电信号提取的特征信息总是有较大差异,而相同动作模式下提取的特征信息较为接近这一特点,提出了高斯径向基函数重构算法对肌电信号进行识别。该算法在对表面肌电信号提取特征信息后,用高斯径向基函数对特征矢量进行重构,使得重构的特征矢量的空间分布存在很大差异而直接进行识别。用该重构算法对提取的AR系数重构,然后进行识别,平均识别率为97.2%;对小波系数重构,平均识别率为99%。     

基于实时意图识别的外骨骼机器人步态切换

根据外骨骼穿戴者的实时运动意图,切换外骨骼机器人的行走步态,是外骨骼机器人研究热点问题之一.针对外骨骼机器人能够根据穿戴者的运动意图控制步态切换问题,提出采集人体表面肌电信号进行解码,通过BP神经网络识别穿戴者的运动意图,实现外骨骼进行步态规划,利用关节角度与足底压力的相关性以及重心轨迹,对所规划的步态的稳定性进行分析,依据不同的运动意图完成步态进行实时切换,验证了基于实时意图识别控制的外骨骼步态切换的可行性.     

基于外侧腓肠肌sEMG的帕金森神经调控步态量化测评方法

帕金森(Parkinson's disease,PD)患者脑深部电刺激(deep brain stimulation,DBS)神经调控治疗迫切需要客观、量化的测评手段和分析方法.针对这一临床需求,本文提出一种基于外侧腓肠肌(lateral gastrocnemius,LG)表面肌电(surface electromyo...     

欠驱动双足机器人动态步态规划方法研究

以欠驱动双足机器人为对象研究其周期稳定的动态步态规划方法。首先建立欠驱动双足机器人的混杂动力学模型,然后采用时不变步态规划策略对机器人步态进行规划,并研究周期步态的收敛条件。步态参数直接决定周期步态的稳定性,采用遗传算法,以能耗最优为目标,以限制条件为约束对步态参数进行选择和优化。最后通过虚拟样机对机器人的行走过程进行动力学仿真。实验表明规划步态收敛于稳定的极限环,实现了高速动态步行,该规划方法是可行的。     

基于矢状面质心角动量的人类行走步态周期阶段划分方法

认知人类的步行机理是双足机器人开发的重要基础.在人类行走过程中,外力力矩是影响行走稳定性的决定性因素,步态与外力力矩的相互作用是人类步行机理研究中的关键问题.尽管质心角动量可反映人体受到的外力力矩变化,但会随步态的演化呈现不同的变化规律.以人类自然行走步态为研究目标,通过准确获取人体行走过程中实时运动信息与质心角动量的变化,根据人体行走过程中的外力力矩与质心角动量的角度对人体步态进行力学分析,并结合人体行走过程中的足地关系与矢状面质心角动量变化规律,得出角动量特征点与步态特征点在时间上具有高度一致性的结论,最终实现基于矢状面质心角动量的人类步态周期阶段的精准划分.研究结果对于认知人类步行机理,指导行走康复医疗和双足机器人研发具有重要意义.     

Improved gait recognition by gait dynamics normalization

Potential sources for gait biometrics can be seen to derive from two aspects: gait shape and gait dynamics. We show that improved gait recognition can be achieved after normalization of dynamics and focusing on the shape information. We normalize for gait dynamics using a generic walking model, as captured by a population Hidden Markov Model (pHMM) defined for a set of individuals. The states of this pHMM represent gait stances over one gait cycle and the observations are the silhouettes of the corresponding gait stances. For each sequence, we first use Viterbi decoding of the gait dynamics to arrive at one dynamics-normalized, averaged, gait cycle of fixed length. The distance between two sequences is the distance between the two corresponding dynamics-normalized gait cycles, which we quantify by the sum of the distances between the corresponding gait stances. Distances between two silhouettes from the same generic gait stance are computed in the linear discriminant analysis space so as to maximize the discrimination between persons, while minimizing the variations of the same subject under different conditions. The distance computation is constructed so that it is invariant to dilations and erosions of the silhouettes. This helps us handle variations in silhouette shape that can occur with changing imaging conditions. We present results on three different, publicly available, data sets. First, we consider the HumanlD Gait Challenge data set, which is the largest gait benchmarking data set that is available (122 subjects), exercising five different factors, i.e., viewpoint, shoe, surface, carrying condition, and time. We significantly improve the performance across the hard experiments involving surface change and briefcase carrying conditions. Second, we also show improved performance on the UMD gait data set that exercises time variations for 55 subjects. Third, on the CMU Mobo data set, we show results for matching across different walking speeds. It is worth noting that there was no separate training for the UMD and CMU data sets.     

Gait Generation and Stabilization for Nearly Passive Dynamic Walking Using Auto‐distributed Impulses

We propose a state feedback control design via linearization for flexible walking on flat ground. First, we generate nearly passive limit cycles, being stable or not, using impulsive toe‐off actuations. The term ‘nearly passive’ means that the dynamics is completely passive almost everywhere except at the toe‐off moment. A feature of our gait generation method is that walking gaits are characterized only by amounts of supplied energy, and we observe that other variables, including input torques, are auto‐balanced via our method. After gait generation, we design a feedback controller considering robustness and input saturation. As a result, each limit cycle can be matched with its respective controller classified only by energy levels. We have verified that walking speeds monotonically increase by adding more energy, and the ankle joint plays a significant role in compass‐gait walking. Finally, instead of applying impulsive torques, we discuss a practical issue regarding realistic control inputs that ensure stable gait transitions as energy levels are elevated.     

Real-time gait subphase detection using an EMG signal graph matching (ESGM) algorithm based on EMG signals

This study presents a gait subphase recognition method using an electromyogram (EMG) with a signal graph matching (ESGM) algorithm. Existing pattern recognition and machine learning using EMG signals has several innate problems in gait subphase detection. With respect to time domain features, their feature values may be analogous because two different gait steps may have similar muscle activation. In addition, the current gait subphase might not be recognized until the next gait subphase passes because the window size needed for feature extraction is larger than the period of the gait subphase. The ESGM algorithm is a new approach that compares reference EMG signals and input EMG signals according to time variance to solve these problems and considers variations of physiological muscle activity. We also determined all the elements of the ESGM algorithm using kinematic gait analysis and optimized the algorithm using experiments. Therefore, the ESGM algorithm reflects better timing characteristics of EMG signals than the time domain feature extraction algorithm. In addition, it can provide real-time and user-adaptive recognition of the gait subphase by using only EMG signals. Experimental results show that the average accuracy of the proposed method is 13% better than existing methods and the average detection latency of the proposed method was 5.5 times lower than existing methods.     

隐马尔可夫模型在步态识别中的应用研究

提出了基于高斯混合输出的连续隐马尔可夫模型的步态识别方法。首先,利用k-均值聚类法对步态序列建立初始的高斯混合模型,然后采用Baum-Welch算法对初始连续隐马尔可夫模型参数不断训练求精,在训练过程中对所存在的问题做适当的改进,解决了算法的溢出问题,最后用最前向算法进行识别;利用CASIA数据库对该算法进行验证,取得了较高的识别率,并对视角变化有一定的鲁棒性。     

A comparison of gait characteristics between Korean and Western people for establishing Korean gait reference data

It is important to analyze the characteristics of normal gait in clinical and biomechanical aspects. Although gait characteristics can be varied by anthropometric, racial and cultural factors, gait studies have primarily been undertaken in Western countries. The present study conducted a gait analysis for Korean people and compared the gait characteristics with those of Western people. A total of 32 Koreans subjects (20 males and 12 females) participated in the gait experiment and their spatio-temporal and kinematic/kinetic characteristics were analyzed. The comparison of the gait characteristics between Korean and Western people revealed that the stride length and walking speed of Korean subjects were significantly lower than those observed in Western studies by 7–25% and 14–42%, respectively. The knee abduction moment of the Korean subjects was larger than those of Western people, while the other moments (hip moment in the sagittal and frontal plane, knee and ankle moment in the sagittal plane) were smaller than those of Western people. There were also differences in ranges of motion between gait studies; however, most motion patterns and excursions were similar.

Relevance to industry

Gait reference data are important to determine the nature and severity of gait disturbances in injured individuals and to evaluate the effect of clinical treatment. The unique gait characteristics of Koreans identified in the present study will provide valuable information on gait parameters for the Korean population.     

基于不变矩的步态识别

提出了一种利用不变矩进行步态识别的方法。该方法把二维人体空间轮廓信号变换为一维不变矩信号,把人体的步态序列变换为不变矩矢量,对不变矩矢量进行规格化,然后根据规格化不变矩矢量进行步态识别。实验中,本文的方法取得很好的效果。