Design of patient-specific gait modifications for knee osteoarthritis rehabilitation

Abstract-Gait modification is a nonsurgical approach for reducing the external knee adduction torque in patients with knee osteoarthritis (OA). The magnitude of the first adduction torque peak in particular is strongly associated with knee OA progression. While toeing out has been shown to reduce the second peak, no clinically realistic gait modifications have been identified that effectively reduce both peaks simultaneously. This study predicts novel patient-specific gait modifications that achieve this goal without changing the foot path. The modified gait motion was designed for a single patient with knee OA using dynamic optimization of a patient-specific, full-body gait model. The cost function minimized the knee adduction torque subject to constraints limiting how much the new gait motion could deviate from the patient's normal gait motion. The optimizations predicted a "medial-thrust" gait pattern that reduced the first adduction torque peak between 32% and 54% and the second peak between 34% and 56%. The new motion involved three synergistic kinematic changes: slightly decreased pelvis obliquity, slightly increased leg flexion, and slightly increased pelvis axial rotation. After gait retraining, the patient achieved adduction torque reductions of 39% to 50% in the first peak and 37% to 55% in the second one. These reductions are comparable to those reported after high tibial osteotomy surgery. The associated kinematic changes were consistent with the predictions except for pelvis obliquity, which showed little change. This study demonstrates that it is feasible to design novel patient-specific gait modifications with potential clinical benefit using dynamic optimization of patient-specific, full-body gait models. Further investigation is needed to assess the extent to which similar gait modifications may be effective for other patients with knee OA.     

Computational assessment of combinations of gait modifications for knee osteoarthritis rehabilitation

Gait modification is a noninvasive strategy for reducing the external knee adduction torque in patients with medial compartment knee osteoarthritis. Recently, a novel “medial thrust” gait pattern characterized by knee medialization during stance phase has been shown to reduce both adduction torque peaks significantly. While changes in footpath (i.e., toe out angle and stance width) also affect the adduction torque peaks, the extent to which footpath changes may alter the effectiveness of medial thrust gait is unknown. This study used a validated patient-specific computational model to investigate this issue. A dynamic optimization framework that accurately predicted adduction torque changes caused by knee medialization or footpath alteration for a specific patient was modified to predict the simultaneous effect of both factors. Medial thrust gait optimizations were then performed for the same patient using imposed footpath alterations consisting of all possible combinations of three toe out angles (nominal $pm$ $hbox{15}^{circ}$) and three stance widths (nominal $pm$ 50 mm). Overall, predicted adduction torque reductions produced by medial thrust gait were relatively insensitive to footpath alterations. The 32%–34% reduction in both peaks achieved with the nominal footpath was augmented by at most 9% and reduced by at most 3% for the altered footpaths. When combined with knee medialization, footpath alterations would likely have only a secondary effect on knee adduction torque reductions for this particular patient.      

Mobile ankle and knee perturbator

A mobile ankle and knee perturbator has been developed. It consists of a functional joint with an integrated clutch. Four Bowden wires connect the joint to a powerful motor and a double pneumatic cylinder. When needed during any time of the gait cycle, it is possible to impose an ankle rotation by engaging the clutch and rotating the ankle or knee joint with a predefined displacement. The system is designed to investigate electrophysiological and biomechanical features of the human ankle or knee joint during gait.     

Ambulatory estimation of knee-joint kinematics in anatomical coordinate system using accelerometers and magnetometers

Knee-joint kinematics analysis using an optimal sensor set and a reliable algorithm would be useful in the gait analysis. An original approach for ambulatory estimation of knee-joint angles in anatomical coordinate system is presented, which is composed of a physical-sensor-difference-based algorithm and virtual-sensor-difference-based algorithm. To test the approach, a wearable monitoring system composed of accelerometers and magnetometers was developed and evaluated on lower limb. The flexion/extension (f/e), abduction/adduction (a/a), and inversion/extension (i/e) rotation angles of the knee joint in the anatomical joint coordinate system were estimated. In this method, since there is no integration of angular acceleration or angular velocity, the result is not distorted by offset and drift. The three knee-joint angles within the anatomical coordinate system are independent of the orders, which must be considered when Euler angles are used. Besides, since there are no physical sensors implanted in the knee joint based on the virtual-sensor-difference-based algorithm, it is feasible to analyze knee-joint kinematics with less numbers and types of sensors than those mentioned in some others methods. Compared with results from the reference system, the developed wearable sensor system is available to do gait analysis with fewer sensors and high degree of accuracy.     

Automatic classification of asymptomatic and osteoarthritis knee gait patterns using kinematic data features and the nearest neighbor classifier

The aim of this work is to develop an automatic computer method to distinguish between asymptomatic (AS) and osteoarthritis (OA) knee gait patterns using 3-D ground reaction force (GRF) measurements. GRF features are first extracted from the force vector variations as a function of time and then classified by the nearest neighbor rule. We investigated two different features: the coefficients of a polynomial expansion and the coefficients of a wavelet decomposition. We also analyzed the impact of each GRF component (vertical, anteroposterior, and medial lateral) on classification. The best discrimination rate (91%) was achieved with the wavelet decomposition using the anteroposterior and the medial lateral components. These results demonstrate the validity of the representation and the classifier for automatic classification of AS and OA knee gait patterns. They also highlight the relevance of the anteroposterior and medial lateral force components in gait pattern classification.     

Accurate measurement of three-dimensional knee replacementkinematics using single-plane fluoroscopy

A simple extension of a previously reported object recognition technique has been used to implement a six-degree-of-freedom position/orientation estimator for the measurement of knee replacement motion from two-dimensional (2-D) fluoroscopic images. Computer modeling studies and controlled mechanical tests were performed to assess the accuracy of the technique. The results indicate that knee rotations can be measured with an accuracy of approximately one degree and that sagittal plane translations can be measured with an accuracy of approximately 0.5 mm. The measurement technique is uniquely well suited for performing dynamic kinematic measurements on individuals with knee replacements, and for performing comparative studies among subjects with different designs of knee replacements     

Model-based estimation of knee stiffness

During natural locomotion, the stiffness of the human knee is modulated continuously and subconsciously according to the demands of activity and terrain. Given modern actuator technology, powered transfemoral prostheses could theoretically provide a similar degree of sophistication and function. However, experimentally quantifying knee stiffness modulation during natural gait is challenging. Alternatively, joint stiffness could be estimated in a less disruptive manner using electromyography (EMG) combined with kinetic and kinematic measurements to estimate muscle force, together with models that relate muscle force to stiffness. Here we present the first step in that process, where we develop such an approach and evaluate it in isometric conditions, where experimental measurements are more feasible. Our EMG-guided modeling approach allows us to consider conditions with antagonistic muscle activation, a phenomenon commonly observed in physiological gait. Our validation shows that model-based estimates of knee joint stiffness coincide well with experimental data obtained using conventional perturbation techniques. We conclude that knee stiffness can be accurately estimated in isometric conditions without applying perturbations, which presents an important step toward our ultimate goal of quantifying knee stiffness during gait.     

Soft tissue artifact compensation in knee kinematics by double anatomical landmark calibration: performance of a novel method during selected motor tasks

The purpose of the present work was to describe and assess the performance on two selected subjects of a new method for the compensation of soft tissue artifact on knee rotations and translations during the execution of step up/down, sit-to-stand/stand-to-sit, and flexion against gravity. Soft tissue artifact has been recognized as the most critical source of error in gait analysis data. Its propagation strongly affects joint angles, in particular those characterized by a small range of motion, such as knee ab/adduction and internal/external rotation. This may be critical in the exploitation of gait analysis data for clinical decisions. The proposed method is based on the flexion/extension angle interpolation of two anatomical landmark calibrations taken at the extremes of motion. Its performance on knee rotation and translations was tested on a kinematics data-set obtained by the synchronous combination of traditional stereophotogrammetry and 3-D fluoroscopy. The newly proposed method was extremely effective on the compensation of soft tissue artifact propagation to knee rotations, in particular mean values of the root mean square error on ab/adduction and internal/external rotation angles decreased from 3.7 degrees and 3.7 degrees to 1.4 degrees and 1.6 degrees, respectively, with respect to single calibration. Mainly, knee translations calculated from stereophotogrammetric data using the proposed compensation method were found to be reliable with respect to the fluoroscopy-based gold standard. The residual mean values of the root mean square error were 2.0, 2.8, and 2.1 mm for anterior/posterior, vertical, and medio/lateral translations, respectively.     

Identifying biometrics in the wild – A time,erosion and neural inspired framework for gait identification

This paper evaluates the performance of contemporary gait identification systems. A time, erosion and neural inspired framework (TEN-FE) for gait identification was proposed to augment the performance of gait identification systems. Performance of TEN-FE framework was evaluated using CASIA and OU-ISIR large population dataset. Proposed framework relies on CNN and Reinforcement Learning to restrict the impact of confounding factors like baggage and bulky clothing on the accuracy of gait identification systems. Difference in gait signature due to time was also considered and normalized. The results observed a clear increase in system’s performance with minimal complexity and least hardware requirements.     

Characterisation of gait cycle from accelerometer data

Commercial intelligent above-knee prostheses capture data for identifying the different stages of gait. Typical signals are the knee angle and forces applied on the prosthetic side. A different approach using accelerometer data for characterising the gait cycle is presented. Seven events are identified from the data captured on a healthy person walking on level terrain.     

基于MIMU的自适应步态检测算法

行人步态检测精度对个人导航系统至关重要。针对当前行人自主导航系统中常规步态检测算法不能适用于多种运动状态下的步态检测问题,提出了一种基于微惯性测量单元（MIMU）的自适应步态检测算法。该算法首先利用加速度计三轴模值方差、单轴方差差别和波形相位识别4种不同的行走状态,包括前进、快跑、后退和横向行走,然后针对不同的行走状态设置自适应阈值,实现各类运动状态下的自适应步态检测。利用实验室自主研发的MIMU固定在腰部脊椎位置进行实验验证,数据显示,前进行走和快跑步态检测精度可达99%,后退和横向行走步态检测精度可达93%。实验证明,自适应步态检测算法适用于个人导航系统。     

A gait rehabilitation strategy inspired by an iterative learning algorithm

Robotic gait rehabilitation devices enable efficient and convenient gait rehabilitation by mimicking the functions of physical therapists. In manual gait rehabilitation training, physical therapists have patients practice and memorize normal gait patterns by applying assistive torque to the patient’s joint once the patient’s gait deviates from the normal gait. Thus, one of the most important factors in robotic gait rehabilitation devices is to determine the assistive torque to the patient’s joint during rehabilitation training. In this paper, the gait rehabilitation strategy inspired by an iterative learning algorithm is proposed, which uses the repetitive characteristic of gait motions. In the proposed strategy, the assistive joint torque in the current stride is calculated based on the information from previous strides. Simulation results and experimental results using an active knee orthosis are presented, which verify that the proposed strategy can be used to calculate appropriate assistive joint torque to excise the desired motions for rehabilitation.     

Support vector machines for automated gait classification

Ageing influences gait patterns causing constant threats to control of locomotor balance. Automated recognition of gait changes has many advantages including, early identification of at-risk gait and monitoring the progress of treatment outcomes. In this paper, we apply an artificial intelligence technique [support vector machines (SVM)] for the automatic recognition of young-old gait types from their respective gait-patterns. Minimum foot clearance (MFC) data of 30 young and 28 elderly participants were analyzed using a PEAK-2D motion analysis system during a 20-min continuous walk on a treadmill at self-selected walking speed. Gait features extracted from individual MFC histogram-plot and Poincaré-plot images were used to train the SVM. Cross-validation test results indicate that the generalization performance of the SVM was on average 83.3% (+/-2.9) to recognize young and elderly gait patterns, compared to a neural network's accuracy of 75.0+/-5.0%. A "hill-climbing" feature selection algorithm demonstrated that a small subset (3-5) of gait features extracted from MFC plots could differentiate the gait patterns with 90% accuracy. Performance of the gait classifier was evaluated using areas under the receiver operating characteristic plots. Improved performance of the classifier was evident when trained with reduced number of selected good features and with radial basis function kernel. These results suggest that SVMs can function as an efficient gait classifier for recognition of young and elderly gait patterns, and has the potential for wider applications in gait identification for falls-risk minimization in the elderly.     

Long-term unrestrained measurement of stride length and walkingvelocity utilizing a piezoelectric gyroscope

Long-term monitoring of stride length and walking velocity is considered to provide useful information for making decisions on treatment of patients with gait disabilities. The purpose of this study was to develop a device with the following design criteria: lightweight, easy attachment, little hindrance to the natural gait pattern, sufficient memory to record for one day, and practicality in clinical use. The prototype consists of a piezoelectric gyroscope, which detects angular velocity of the thigh of one leg in the sagittal plane, and a microprocessor-based maximum/minimum detector/data logger of a cyclic analog signal associated with the gait cycle. The accuracy of the device was evaluated in 20 normal subjects, seven above-the-knee (A/K) amputees, and ten hemiplegic patients, and relative accuracy within ±15% was obtained, except for two special cases     

一种基于关节角度的步态识别新方法

基于行走运动的关节角度变化包含更丰富的个体识别信息的观点,提出利用下肢关节角度进行步态识别的新方法。依据人体解剖学的先验知识,通过对下肢运动分析定位盆骨、左右膝、左右踝关节点,提取相邻关节点连线与竖直线的夹角作为运动关节角度。识别时,考虑到NN,KNN等传统步态分类器分类能力较弱的缺点,采用针对小样本问题具有很好分类效果的支持向量机对步态特征向量进行分类。CASIA步态数据库上的仿真结果证明该方法具有较高的识别性能。     

Application of a neuro-fuzzy network for gait event detection using electromyography in the child with cerebral palsy

An adaptive neuro-fuzzy inference system (ANFIS) with a supervisory control system (SCS) was used to predict the occurrence of gait events using the electromyographic (EMG) activity of lower extremity muscles in the child with cerebral palsy (CP). This is anticipated to form the basis of a control algorithm for the application of electrical stimulation (ES) to leg or ankle muscles in an attempt to improve walking ability. Either surface or percutaneous intramuscular electrodes were used to record the muscle activity from the quadriceps muscles, with concurrent recording of the gait cycle performed using a VICON motion analysis system for validation of the ANFIS with SCS. Using one EMG signal and its derivative from each leg as its inputs, the ANFIS with SCS was able to predict all gait events in seven out of the eight children, with an average absolute time differential between the VICON recording and the ANFIS prediction of less than 30 ms. Overall accuracy in predicting gait events ranged from 98.6% to 95.3% (root mean-squared error between 0.7 and 1.5). Application of the ANFIS with the SCS to the prediction of gait events using EMG data collected two months after the initial data demonstrated comparable results, with no significant differences between gait event detection times. The accuracy rate and robustness of the ANFIS with SCS with two EMG signals suggests its applicability to ES control.     

基于静电信号的人体步伐周期长程相关性研究

人的步伐信号包含着人体身体状态和健康状况等多种重要信息,因此受到越来越多的重视和研究.本文利用人体携带大量电荷这一特性,通过静电探测器对人体踏步过程中的步伐静电信号进行采集,研究人体步伐在时间尺度上的变化规律.论文提出一种自相关算法滤除信号中的噪声和干扰,通过相关系数确定步伐中的同相位点,从而获得精确步伐周期值.通过对步伐周期序列进行分解,得到步伐周期增量绝对值和变化符号两个序列,运用消除趋势波动分析对原始步伐周期序列及分解后的两个新序列进行分析,得到其长程相关性规律.通过对于实验所采集的多名测试对象的数据进行分析,发现对于所有被测人员,其步伐周期的增量绝对值序列均呈现出较强的持续正相关,而其周期变化符号呈现出明显的反相关特性.     

Design of M-G modal space sliding mode control for lower limb exoskeleton robot driven by electrical actuators

Lower limb exoskeleton robot (LLER) can help patients with lower limb paralysis to carry out effective rehabilitation training. However, LLER is a kind of nonlinear system with the strong dynamic coupling between joints and the parameter perturbation following different poses of the robot. They will damage the control performance in the process of trajectory tracking. To solve these problems, a novel control strategy, Mass-Gravity modal space sliding mode control (M-GMSSMC), is proposed. The objective for this paper is to develop a novel decoupling control framework for an electrical actuators driven LLER to track a predefined gait trajectory. The controller design aims to improve trajectory tracking accuracy, reduce dynamic coupling between hip joint and knee joint and weaken the chattering phenomenon of the sliding mode controller. The decoupling condition and the robust stability condition are analyzed in this work. Experimental results validate the correctness of the presented conclusions and show the effectiveness of the proposed M-GMSSMC.     

Relative distance features for gait recognition with Kinect

Gait and static body measurement are important biometric technologies for passive human recognition. Many previous works argue that recognition performance based completely on the gait feature is limited. The reason for this limited performance remains unclear. This study focuses on human recognition with gait feature obtained by Kinect and shows that gait feature can effectively distinguish from different human beings through a novel representation – relative distance-based gait features. Experimental results show that the recognition accuracy with relative distance features reaches up to 85%, which is comparable with that of anthropometric features. The combination of relative distance features and anthropometric features can provide an accuracy of more than 95%. Results indicate that the relative distance feature is quite effective and worthy of further study in more general scenarios (e.g., without Kinect).     

一种基于无源射频技术的用户步态识别及认证方法

步态识别作为一种新兴的生物特征识别技术,具有距离远、难伪造的优点,在智能监控等领域中具有广泛的应用前景。现有的步态识别方法存在着算法计算复杂、用户参与度高、设备开销较大等问题。针对这些问题,文中提出了一种基于无源射频技术的用户步态识别方法。该方法使用了门禁系统中已经广泛部署的无源射频标签,通过标签相位数据计算用户行走动态速度,利用多标签信号互补特性进行信号补偿,提取用户步态频率响应特征。实验结果显示,该方法的用户识别准确率高达91.87%,特征提取及比对的时延仅有0.129 s。在训练数据极少、用户参与度低的情况下,实现了高效率、较准确的用户步态识别及认证。