A Novel 3D Gait Model for Subject Identification Robust against Carrying and Dressing Variations

Subject identification via the subject’s gait is challenging due to variations in the subject’s carrying and dressing conditions in real-life scenes.This paper proposes a novel targeted 3-dimensional(3D)gait model(3DGait)represented by a set of interpretable 3DGait descriptors based on a 3D parametric body model.The 3DGait descriptors are utilised as invariant gait features in the 3DGait recognition method to address object carrying and dressing.The 3DGait recognitionmethod involves 2-dimensional(2D)to 3DGaitdata learningbasedon3Dvirtual samples,a semantic gait parameter estimation Long Short Time Memory(LSTM)network(3D-SGPE-LSTM),a feature fusion deep model based on a multi-set canonical correlation analysis,and SoftMax recognition network.First,a sensory experiment based on 3D body shape and pose deformation with 3D virtual dressing is used to fit 3DGait onto the given 2D gait images.3Dinterpretable semantic parameters control the 3D morphing and dressing involved.Similarity degree measurement determines the semantic descriptors of 2D gait images of subjects with various shapes,poses and styles.Second,using the 2D gait images as input and the subjects’corresponding 3D semantic descriptors as output,an end-to-end 3D-SGPE-LSTM is constructed and trained.Third,body shape,pose and external gait factors(3D-eFactors)are estimated using the 3D-SGPE-LSTM model to create a set of interpretable gait descriptors to represent the 3DGait Model,i.e.,3D intrinsic semantic shape descriptor(3DShape);3D skeleton-based gait pose descriptor(3D-Pose)and 3D dressing with other 3D-eFators.Finally,the 3D-Shape and 3D-Pose descriptors are coupled to a unified pattern space by learning prior knowledge from the 3D-eFators.Practical research on CASIA B,CMU MoBo,TUM GAID and GPJATK databases shows that 3DGait is robust against object carrying and dressing variations,especially under multi-cross variations.

Smart Gait:A Gait Optimization Framework for Hexapod Robots

The current gait planning for legged robots is mostly based on human presets,which cannot match the flexible characteristics of natural mammals.This paper proposes a gait optimization framework for hexapod robots called Smart Gait.Smart Gait contains three modules:swing leg trajectory optimization,gait period&duty optimization,and gait sequence optimization.The full dynamics of a single leg,and the centroid dynamics of the overall robot are considered in the respective modules.The Smart Gait not only helps the robot to decrease the energy consumption when in locomotion,mostly,it enables the hexapod robot to determine its gait pattern transitions based on its current state,instead of repeating the formalistic clock-set step cycles.Our Smart Gait framework allows the hexapod robot to behave nimbly as a living animal when in 3D movements for the first time.The Smart Gait framework combines offline and online optimizations without any fussy data-driven training procedures,and it can run efficiently on board in real-time after deployment.Various experiments are carried out on the hexapod robot LittleStrong.The results show that the energy consumption is reduced by 15.9%when in locomotion.Adaptive gait patterns can be generated spontaneously both in regular and challenge environments,and when facing external interferences.

Human Gait Recognition for Biometrics Application Based on Deep Learning Fusion Assisted Framework

The demand for a non-contact biometric approach for candidate identification has grown over the past ten years.Based on the most important biometric application,human gait analysis is a significant research topic in computer vision.Researchers have paid a lot of attention to gait recognition,specifically the identification of people based on their walking patterns,due to its potential to correctly identify people far away.Gait recognition systems have been used in a variety of applications,including security,medical examinations,identity management,and access control.These systems require a complex combination of technical,operational,and definitional considerations.The employment of gait recognition techniques and technologies has produced a number of beneficial and well-liked applications.Thiswork proposes a novel deep learning-based framework for human gait classification in video sequences.This framework’smain challenge is improving the accuracy of accuracy gait classification under varying conditions,such as carrying a bag and changing clothes.The proposed method’s first step is selecting two pre-trained deep learningmodels and training fromscratch using deep transfer learning.Next,deepmodels have been trained using static hyperparameters;however,the learning rate is calculated using the particle swarmoptimization(PSO)algorithm.Then,the best features are selected from both trained models using the Harris Hawks controlled Sine-Cosine optimization algorithm.This algorithm chooses the best features,combined in a novel correlation-based fusion technique.Finally,the fused best features are categorized using medium,bi-layer,and tri-layered neural networks.On the publicly accessible dataset known as the CASIA-B dataset,the experimental process of the suggested technique was carried out,and an improved accuracy of 94.14% was achieved.The achieved accuracy of the proposed method is improved by the recent state-of-the-art techniques that show the significance of this work.

Personalized Lower Limb Gait Reconstruction Modeling Based on RFA-ProMP

Personalized gait curves are generated to enhance patient adaptability to gait trajectories used for passive training in the early stage of rehabilitation for hemiplegic patients.The article utilizes the random forest algorithm to construct a gait parameter model,which maps the relationship between parameters such as height,weight,age,gender,and gait speed,achieving prediction of key points on the gait curve.To enhance prediction accuracy,an attention mechanism is introduced into the algorithm to focus more on the main features.Meanwhile,to ensure high similarity between the reconstructed gait curve and the normal one,probabilistic motion primitives(ProMP)are used to learn the probability distribution of normal gait data and construct a gait trajectorymodel.Finally,using the specified step speed as input,select a reference gait trajectory from the learned trajectory,and reconstruct the curve of the reference trajectoryusing the gait keypoints predictedby the parametermodel toobtain the final curve.Simulation results demonstrate that the method proposed in this paper achieves 98%and 96%curve correlations when generating personalized lower limb gait curves for different patients,respectively,indicating its suitability for such tasks.

Trajectory of Walking Function in Late-Stage Older Individuals Managed with a Regular Exercise Program: A 5-Year Longitudinal Tracking with an IoT Gait Analysis System Using Accelerometers

Purpose: This study focused on maintaining and improving the walking function of late-stage older individuals while longitudinally tracking the effects of regular exercise programs in a day-care service specialized for preventive care over 5 years, using detailed gait function measurements with an accelerometer-based system. Methods: Seventy individuals (17 male and 53 female) of a daycare service in Tokyo participated in a weekly exercise program, meeting 1 - 2 times. The average age of the participants at the start of the program was 81.4 years. Gait function, including gait speed, stride length, root mean square (RMS) of acceleration, gait cycle time and its standard deviation, and left-right difference in stance time, was evaluated every 6 months. Results: Gait speed and stride length improved considerably within six months of starting the exercise program, confirming an initial improvement in gait function. This suggests that regular exercise programs can maintain or improve gait function even age groups that predictably have a gradual decline in gait ability due to enhanced age. In the long term, many indicators tended to approach baseline values. However, the exercise program seemingly counteracts age-related changes in gait function and maintains a certain level of function. Conclusions: While a decline in gait ability with aging is inevitable, establishing appropriate exercise habits in late-stage older individuals may contribute to long-term maintenance of gait function.

Multisensory mechanisms of gait and balance in Parkinson’s disease:an integrative review

Understanding the neural underpinning of human gait and balance is one of the most pertinent challenges for 21st-century translational neuroscience due to the profound impact that falls and mobility disturbances have on our aging population.Posture and gait control does not happen automatically,as previously believed,but rather requires continuous involvement of central nervous mechanisms.To effectively exert control over the body,the brain must integrate multiple streams of sensory information,including visual,vestibular,and somatosensory signals.The mechanisms which underpin the integration of these multisensory signals are the principal topic of the present work.Existing multisensory integration theories focus on how failure of cognitive processes thought to be involved in multisensory integration leads to falls in older adults.Insufficient emphasis,however,has been placed on specific contributions of individual sensory modalities to multisensory integration processes and cross-modal interactions that occur between the sensory modalities in relation to gait and balance.In the present work,we review the contributions of somatosensory,visual,and vestibular modalities,along with their multisensory intersections to gait and balance in older adults and patients with Parkinson’s disease.We also review evidence of vestibular contributions to multisensory temporal binding windows,previously shown to be highly pertinent to fall risk in older adults.Lastly,we relate multisensory vestibular mechanisms to potential neural substrates,both at the level of neurobiology(concerning positron emission tomography imaging)and at the level of electrophysiology(concerning electroencephalography).We hope that this integrative review,drawing influence across multiple subdisciplines of neuroscience,paves the way for novel research directions and therapeutic neuromodulatory approaches,to improve the lives of older adults and patients with neurodegenerative diseases.

The Immediate Analgesic Effect and Impact on Gait Function of Transcutaneous Electrical Nerve Stimulation in Late-Stage Elderly Individuals with Knee Pain: Examination of Gait Function Using an IoT-Based Gait Analysis Device

Purpose: This study verified the effects of transcutaneous electrical nerve stimulation (TENS), which can be worn during walking and exercise, in elderly individuals with late-stage knee pain who exercise regularly. Methods: Thirty-two late-stage elderly individuals were evaluated for knee pain during rest, walking, and program exercises, with and without TENS. Gait analysis was performed using an IoT-based gait analysis device to examine the effects of TENS-induced analgesia on gait. Results: TENS significantly reduced knee pain during rest, walking, and programmed exercises, with the greatest analgesic effect observed during walking. The greater the knee pain without TENS, the more significant the analgesic effect of TENS. A comparison of gait parameters revealed a significant difference only in the gait cycle time, with a trend towards faster walking with TENS;however, the effect was limited. Conclusion: TENS effectively relieves knee pain in late-stage elderly individuals and can be safely applied during exercise. Pain management using TENS provides important insights into the implementation of exercise therapy in this age group.

融合轮廓增强和注意力机制的改进GaitSet步态识别方法

针对传统基于轮廓的步态识别方法受限于输入特征及模型特征提取的能力,从而导致识别准确率不高的问题,提出一种融合轮廓增强和注意力机制的改进GaitSet步态识别方法。首先通过预处理获取行人的轮廓图,求得其均值,合成步态GEI能量图,将其作为神经网络模型的输入特征,增强了人体外观的表示。其次在提取特征的过程中引入注意力机制,增强模型的特征提取能力,从而提高步态识别的精度。最后在CASIA-B和OU-MVLP数据集上进行实验,所提方法的平均Rank-1准确率分别为87.7%和88.1%。特别是在最复杂的穿大衣行走条件下,相较于GaitSetv2算法,准确率提升了6.7%,表明所提出方法具有更强的准确性。此外,所提方法几乎没有增加额外的参数量、计算复杂度和推理时间,说明其各模块的快速性。

Analyzing the Combination Effects of Repetitive Transcranial Magnetic Stimulation and Motor Control Training on Balance Function and Gait in Patients with Stroke-Induced Hemiplegia

Objective:To analyze the effects of repetitive transcranial magnetic stimulation combined with motor control training on the treatment of stroke-induced hemiplegia,specifically focusing on the impact on patients’balance function and gait.Methods:Fifty-two cases of hemiplegic stroke patients were randomly divided into two groups,26 in the control group and 26 in the observation group,using computer-generated random grouping.All participants underwent conventional treatment and rehabilitation training.In addition to these,the control group received repetitive transcranial magnetic pseudo-stimulation therapy+motor control training,while the observation group received repetitive transcranial magnetic stimulation therapy+motor control training.The balance function and gait parameters of both groups were compared before and after the interventions and assessed the satisfaction of the interventions in both groups.Results:Before the invention,there were no significant differences in balance function scores and each gait parameter between the two groups(P>0.05).However,after the intervention,the observation group showed higher balance function scores compared to the control group(P<0.05).The observation group also exhibited higher step speed and step frequency,longer step length,and a higher overall satisfaction level with the intervention compared to the control group(P<0.05).Conclusion:The combination of repetitive transcranial magnetic stimulation and motor control training in the treatment of stroke-induced hemiplegia has demonstrated positive effects.It not only improves the patient’s balance function and gait but also contributes to overall physical rehabilitation.

MMRGait-1.0:多视角多穿着条件下的雷达时频谱图步态识别数据集

步态识别作为一种生物识别技术,在实际生活中通常被认为是一项检索任务。然而,受限于现有雷达步态识别数据集的规模,目前的研究主要针对分类任务且局限于单一行走视角和相同穿着条件,这限制了基于雷达的步态识别在实际场景中的应用。该文公开了一个多视角多穿着条件下的雷达步态识别数据集,该数据集使用毫米波雷达采集了121位受试者在多种穿着条件下沿不同视角行走的时频谱图数据,每位受试者共采集8个视角,每个视角采集10组,其中6组为正常穿着,2组为穿大衣,2组为挎包。同时,该文提出一种基于检索任务的雷达步态识别方法,并在公布数据集上进行了实验,实验结果可以作为基准性能指标,方便更多学者在此数据集上开展进一步研究。

Compressive Measurement Identification of Linear Time-Invariant System Application in DC Motor

In traditional system identification (SI), actual values of system parameters are concealed in the input and output data;hence, it is necessary to apply estimation methods to determine the parameters. In signal processing, a signal with N elements must be sampled at least N times. Thus, most SI methods use N or more sample data to identify a model with N parameters;however, this can be improved by a new sampling theory called compressive sensing (CS). Based on CS, an SI method called compressive measurement identification (CMI) is proposed for reducing the data needed for estimation, by measuring the parameters using a series of linear measurements, rather than the measurements in sequence. In addition, the accuracy of the measurement process is guaranteed by a criterion called the restrict isometric principle. Simulations demonstrate the accuracy and robustness of CMI in an underdetermined case. Further, the dynamic process of a DC motor is identified experimentally, establishing that CMI can shorten the identification process and increase the prediction accuracy.

Perturbation Analysis of Continuous-Time Linear Time-Invariant Systems

In this paper, we consider the perturbation analysis of linear time-invariant systems, which arise from the linear optimal control in continuous-time. We provide a method to compute condition numbers of continuous-time linear time-invariant systems. It solves the perturbed linear time-invariant systems via Riccati differential equations and continuous-time algebraic Riccati equations in finite and infinite time horizons. We derive the explicit expressions of measuring the perturbation bounds of condition numbers with respect to the solution of the linear time-invariant systems. Furthermore, condition numbers and their upper bounds of Riccati differential equations and continuous-time algebraic Riccati equations are also discussed. Numerical simulations show the sharpness of the perturbation bounds computed via the proposed methods.

Assessment of hindlimb motor recovery affer severe thoracic spinal cord injury in rats: classification of CatWalk XT■ gait analysis parameters

Assessment of locomotion recovery in preclinical studies of experimental spinal cord injury remains challenging. We studied the CatWalk XT■gait analysis for evaluating hindlimb functional recovery in a widely used and clinically relevant thoracic contusion/compression spinal cord injury model in rats. Rats were randomly assigned to either a T9 spinal cord injury or sham laminectomy. Locomotion recovery was assessed using the Basso, Beattie, and Bresnahan open field rating scale and the CatWalk XT■gait analysis. To determine the potential bias from weight changes, corrected hindlimb(H) values(divided by the unaffected forelimb(F) values) were calculated. Six weeks after injury, cyst formation, astrogliosis, and the deposition of chondroitin sulfate glycosaminoglycans were assessed by immunohistochemistry staining. Compared with the baseline, a significant spontaneous recovery could be observed in the CatWalk XT■parameters max intensity, mean intensity, max intensity at%, and max contact mean intensity from 4 weeks after injury onwards. Of note, corrected values(H/F) of CatWalk XT■parameters showed a significantly less vulnerability to the weight changes than absolute values, specifically in static parameters. The corrected CatWalk XT■parameters were positively correlated with the Basso, Beattie, and Bresnahan rating scale scores, cyst formation, the immunointensity of astrogliosis and chondroitin sulfate glycosaminoglycan deposition. The CatWalk XT■gait analysis and especially its static parameters, therefore, seem to be highly useful in assessing spontaneous recovery of hindlimb function after severe thoracic spinal cord injury. Because many CatWalk XT■parameters of the hindlimbs seem to be affected by body weight changes, using their corrected values might be a valuable option to improve this dependency.

3-D Gait Identification Utilizing Latent Canonical Covariates Consisting of Gait Features

Biometric gait recognition is a lesser-known but emerging and effective biometric recognition method which enables subjects’walking patterns to be recognized.Existing research in this area has primarily focused on feature analysis through the extraction of individual features,which captures most of the information but fails to capture subtle variations in gait dynamics.Therefore,a novel feature taxonomy and an approach for deriving a relationship between a function of one set of gait features with another set are introduced.The gait features extracted from body halves divided by anatomical planes on vertical,horizontal,and diagonal axes are grouped to form canonical gait covariates.Canonical Correlation Analysis is utilized to measure the strength of association between the canonical covariates of gait.Thus,gait assessment and identification are enhancedwhenmore semantic information is available through CCA-basedmulti-feature fusion.Hence,CarnegieMellon University’s 3D gait database,which contains 32 gait samples taken at different paces,is utilized in analyzing gait characteristics.The performance of Linear Discriminant Analysis,K-Nearest Neighbors,Naive Bayes,Artificial Neural Networks,and Support Vector Machines was improved by a 4%average when the CCA-utilized gait identification approachwas used.Asignificant maximumaccuracy rate of 97.8%was achieved throughCCA-based gait identification.Beyond that,the rate of false identifications and unrecognized gaits went down to half,demonstrating state-of-the-art for gait identification.

Gait Analysis of a Subject with Tarsometatarsal Prosthesis

Introduction: Gait analysis of an adult man after trans-metatarsal unilateral amputation is described. Objective: Instrumental gait analysis of a subject 15 years after trans-metatarsal unilateral amputation in two footwear conditions: while walking barefoot and with prosthesis. Materials and Methods: In a movement analysis laboratory, locomotion studies were carried out at freely chosen walking speed by a 65-year-old subject, obtaining kinematic, kinetic and surface electromyographic data in time and space. Gait analysis was performed using instrumental technologies from a digital eco-system applying walking protocols. Results: When the patient wore the prosthesis, several positive improvements were observed in various aspects of gait. These included enhancements in the base of support, gait speed, and joint range of movements. Additionally, there was a slight improvement in the vertical ground reaction forces pattern, indicating a positive effect of the assistive technology. Furthermore, the use of the prosthesis led to a more organized pattern of muscle activity, which further supports its beneficial impact. However, it is worth noting that some challenges still persisted, particularly regarding stabilizing the body during the double support phase. Despite this difficulty, the overall findings suggest that the use of the prosthesis offers valuable improvements to the patient’s gait dynamics. Conclusions: After conducting a thorough analysis of the parameters related to the gait of a subject who underwent a trans-metatarsal unilateral amputation fifteen years ago, it was found that the use of prosthesis had a positive impact. This study demonstrated important improvements in some kinematic and kinetic parameters, including muscle activation patterns, indicating an increase in comfort and confidence while utilizing the prosthetic device.

Reference values of gait parameters in healthy Chinese university students: A cross-sectional observational study

BACKGROUND Gait is influenced by race,age,and diseases type.Reference values for gait are closely related to numerous health outcomes.To gain a comprehensive understanding of gait patterns,particularly in relation to race-related pathologies and disorders,it is crucial to establish reference values for gait in daily life considering sex and age.Therefore,our objective was to present sex and age-based reference values for gait in daily life,providing a valuable foundation for further research and clinical applications.AIM To establish reference values for lower extremity joint kinematics and kinetics during gait in asymptomatic adult women and men.METHODS Spatiotemporal,kinematics and kinetics parameters were measured in 171 healthy adults(70 males and 101 females)using the computer-aided soft tissue foot model.Full curve statistical parametric mapping was performed using independent and paired-samples t-tests.RESULTS Compared with females,males required more time(cycle time,double-limb support time,stance time,swing time,and stride time),and the differences were statistically significant.In addition,the step and stride lengths of males were longer.Compared to males,female cadence was faster,and statures-per-second and stride-per-minute were higher.There were no statistical differences in speed and stride width between the two groups.After adjusting for height,it was observed that women walked significantly faster than men,and they also had a higher cadence.However,in terms of step length,stride length,and stride width,both genders exhibited similarities.CONCLUSION We established reference values for gait speed and spatiotemporal gait parameters in Chinese university students.This contributes to a valuable database for gait assessment and evaluation of preventive or rehabilitative programs.

Human Personality Assessment Based on Gait Pattern Recognition Using Smartphone Sensors

Human personality assessment using gait pattern recognition is one of the most recent and exciting research domains.Gait is a person’s identity that can reflect reliable information about his mood,emotions,and substantial personality traits under scrutiny.This research focuses on recognizing key personality traits,including neuroticism,extraversion,openness to experience,agreeableness,and conscientiousness,in line with the bigfive model of personality.We inferred personality traits based on the gait pattern recognition of individuals utilizing built-in smartphone sensors.For experimentation,we collected a novel dataset of 22 participants using an android application and further segmented it into six data chunks for a critical evaluation.After data pre-processing,we extracted selected features from each data segment and then applied four multiclass machine learning algorithms for training and classifying the dataset corresponding to the users’Big-Five Personality Traits Profiles(BFPT).Experimental results and performance evaluation of the classifiers revealed the efficacy of the proposed scheme for all big-five traits.

A Three-Dimensional Real-Time Gait-Based Age Detection System Using Machine Learning

Human biometric analysis has gotten much attention due to itswidespread use in different research areas, such as security, surveillance,health, human identification, and classification. Human gait is one of the keyhuman traits that can identify and classify humans based on their age, gender,and ethnicity. Different approaches have been proposed for the estimation ofhuman age based on gait so far. However, challenges are there, for which anefficient, low-cost technique or algorithm is needed. In this paper, we proposea three-dimensional real-time gait-based age detection system using a machinelearning approach. The proposed system consists of training and testingphases. The proposed training phase consists of gait features extraction usingthe Microsoft Kinect (MS Kinect) controller, dataset generation based onjoints’ position, pre-processing of gait features, feature selection by calculatingthe Standard error and Standard deviation of the arithmetic mean and bestmodel selection using R2 and adjusted R2 techniques. T-test and ANOVAtechniques show that nine joints (right shoulder, right elbow, right hand, leftknee, right knee, right ankle, left ankle, left, and right foot) are statisticallysignificant at a 5% level of significance for age estimation. The proposedtesting phase correctly predicts the age of a walking person using the resultsobtained from the training phase. The proposed approach is evaluated on thedata that is experimentally recorded from the user in a real-time scenario.Fifty (50) volunteers of different ages participated in the experimental study.Using the limited features, the proposed method estimates the age with 98.0%accuracy on experimental images acquired in real-time via a classical generallinear regression model.

Gait Image Classification Using Deep Learning Models for Medical Diagnosis

Gait refers to a person’s particular movements and stance while moving around.Although each person’s gait is unique and made up of a variety of tiny limb orientations and body positions,they all have common characteristics that help to define normalcy.Swiftly identifying such characteristics that are difficult to spot by the naked eye,can help in monitoring the elderly who require constant care and support.Analyzing silhouettes is the easiest way to assess and make any necessary adjustments for a smooth gait.It also becomes an important aspect of decision-making while analyzing and monitoring the progress of a patient during medical diagnosis.Gait images made publicly available by the Chinese Academy of Sciences(CASIA)Gait Database was used in this study.After evaluating using the CASIA B and C datasets,this paper proposes a Convolutional Neural Network(CNN)and a CNN Long Short-TermMemory Network(CNN-LSTM)model for classifying the gait silhouette images.Transfer learningmodels such as MobileNetV2,InceptionV3,Visual Geometry Group(VGG)networks such as VGG16 and VGG19,Residual Networks(ResNet)like the ResNet9 and ResNet50,were used to compare the efficacy of the proposed models.CNN proved to be the best by achieving the highest accuracy of 94.29%.This was followed by ResNet9 and CNN-LSTM,which arrived at 93.30%and 87.25%accuracy,respectively.

The minimal clinically important difference for gait speed in significant unilateral vestibular hypofunction after vestibular rehabilitation

Gait speed is a valid measure of both physical function and vestibular health.Vestibular rehabilitation is useful to improve gait speed for patients with vestibular hypofunction,yet there is little data to indicate how changes in gait speed reflect changes in patient-reported health outcomes.We determined the minimal clinically important difference in the gait speed of patients with unilateral vestibular hypofunction,mostly due to deafferentation surgery,as anchored to the Dizziness Handicap Index and the Activities Balance Confidence scale,validated using regression analysis,change difference,receiveroperator characteristic curve,and average change methods.After six weeks of vestibular rehabilitation,a change in gait speed from 0.20 to 0.34 m/s with 95%confidence was required for the patients to perceive a significant reduction in perception of dizziness and improved balance confidence.