[Objectives]To investigate the clinical efficacy of acupoint injection of nerve growth factors combined with task-oriented training for patients with post-stroke hemiplegic gait,and quantitatively evaluate the outcome...[Objectives]To investigate the clinical efficacy of acupoint injection of nerve growth factors combined with task-oriented training for patients with post-stroke hemiplegic gait,and quantitatively evaluate the outcomes using the Gait Watch analysis system.[Methods]A total of 90 patients with post-stroke hemiplegia,who were hospitalized at the Rehabilitation Center of Taihe Hospital between January 2023 and December 2023,were selected for this study.The participants were randomly assigned to three groups:the task-oriented rehabilitation training group(control group I,n=30),the ordinary acupuncture combined with task-oriented training group(control group II,n=30),and the acupoint injection combined with task-oriented training group(observation group,n=30).Each group underwent treatment for 4 weeks.The Gait Watch analysis system was employed to assess the spatiotemporal gait parameters of the patients prior to treatment,as well as 2 weeks post treatment and 4 weeks post treatment.The efficacy of the treatment was subsequently analyzed.[Results]After 4 weeks of treatment,the spatiotemporal gait parameters,specifically step length,step speed,step frequency,percentage of the standing phase,and percentage of the swinging phase,exhibited significant improvement in the observation group compared to those before treatment(P<0.05).Furthermore,the degree of improvement in the observation group was superior to that observed in both control group I and control group II,with the differences reaching statistical significance(P<0.05).[Conclusions]Acupoint injection combined with task-oriented training has been shown to significantly enhance gait function in patients with post-stroke hemiplegia.The Gait Watch analysis system offers an accurate and objective quantitative assessment,making it a valuable tool for clinical application and promotion.展开更多
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 ...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.展开更多
In recent years,gait-based emotion recognition has been widely applied in the field of computer vision.However,existing gait emotion recognition methods typically rely on complete human skeleton data,and their accurac...In recent years,gait-based emotion recognition has been widely applied in the field of computer vision.However,existing gait emotion recognition methods typically rely on complete human skeleton data,and their accuracy significantly declines when the data is occluded.To enhance the accuracy of gait emotion recognition under occlusion,this paper proposes a Multi-scale Suppression Graph ConvolutionalNetwork(MS-GCN).TheMS-GCN consists of three main components:Joint Interpolation Module(JI Moudle),Multi-scale Temporal Convolution Network(MS-TCN),and Suppression Graph Convolutional Network(SGCN).The JI Module completes the spatially occluded skeletal joints using the(K-Nearest Neighbors)KNN interpolation method.The MS-TCN employs convolutional kernels of various sizes to comprehensively capture the emotional information embedded in the gait,compensating for the temporal occlusion of gait information.The SGCN extracts more non-prominent human gait features by suppressing the extraction of key body part features,thereby reducing the negative impact of occlusion on emotion recognition results.The proposed method is evaluated on two comprehensive datasets:Emotion-Gait,containing 4227 real gaits from sources like BML,ICT-Pollick,and ELMD,and 1000 synthetic gaits generated using STEP-Gen technology,and ELMB,consisting of 3924 gaits,with 1835 labeled with emotions such as“Happy,”“Sad,”“Angry,”and“Neutral.”On the standard datasets Emotion-Gait and ELMB,the proposed method achieved accuracies of 0.900 and 0.896,respectively,attaining performance comparable to other state-ofthe-artmethods.Furthermore,on occlusion datasets,the proposedmethod significantly mitigates the performance degradation caused by occlusion compared to other methods,the accuracy is significantly higher than that of other methods.展开更多
BACKGROUND Older adults with mild cognitive impairment(MCI)often show motor dysfunction,including slower gait and impaired handwriting.While gait and handwriting parameters are promising for MCI screening,their combin...BACKGROUND Older adults with mild cognitive impairment(MCI)often show motor dysfunction,including slower gait and impaired handwriting.While gait and handwriting parameters are promising for MCI screening,their combined potential to distinguish MCI from cognitively normal adults is unclear.AIM To assess gait and handwriting differences and their potential for screening MCI in older adults.METHODS Ninety-five participants,including 34 with MCI and 61 cognitively normal controls,were assessed for gait using the GAITRite^(R)system and handwriting with a dot-matrix pen.Five machine learning models were developed to assess the discriminative power of gait and handwriting data for MCI screening.RESULTS Compared to the cognitively normal group,the MCI group had slower gait velocity(Z=-2.911,P=0.004),shorter stride and step lengths(t=-3.005,P=0.003;t=2.863,P=0.005),and longer cycle,standing,and double support times(t=-2.274,P=0.025;t=-2.376,P=0.018;t=-2.717,P=0.007).They also had reduced cadence(t=2.060,P=0.042)and increased double support time variability(Z=-2.614,P=0.009).In handwriting,the MCI group showed lower average pressure(all tasks:Z=-2.135,P=0.033)and decreased accuracy(graphic task:Z=-2.447,P=0.014;Chinese character task:Z=-3.078,P=0.002).In the graphic task,they demonstrated longer time in air(Z=-2.865,P=0.004),reduced X-axis maximum velocities(Z=-3.237,P=0.001),and lower accelerations(X-axis:Z=-2.880,P=0.004;Y-axis:Z=-1.987,P=0.047)and maximum accelerations(X-axis:Z=-3.998,P<0.001;Y-axis:Z=-2.050,P=0.040).The multimodal analysis achieved the highest accuracy(74.4%)with the Gradient Boosting Classifier.CONCLUSION Integrating gait and handwriting kinematics parameters provides a viable method for distinguishing MCI,potentially supporting large-scale screening,especially in resource-limited settings.展开更多
Biometric characteristics are playing a vital role in security for the last few years.Human gait classification in video sequences is an important biometrics attribute and is used for security purposes.A new framework...Biometric characteristics are playing a vital role in security for the last few years.Human gait classification in video sequences is an important biometrics attribute and is used for security purposes.A new framework for human gait classification in video sequences using deep learning(DL)fusion assisted and posterior probability-based moth flames optimization(MFO)is proposed.In the first step,the video frames are resized and finetuned by two pre-trained lightweight DL models,EfficientNetB0 and MobileNetV2.Both models are selected based on the top-5 accuracy and less number of parameters.Later,both models are trained through deep transfer learning and extracted deep features fused using a voting scheme.In the last step,the authors develop a posterior probabilitybased MFO feature selection algorithm to select the best features.The selected features are classified using several supervised learning methods.The CASIA-B publicly available dataset has been employed for the experimental process.On this dataset,the authors selected six angles such as 0°,18°,90°,108°,162°,and 180°and obtained an average accuracy of 96.9%,95.7%,86.8%,90.0%,95.1%,and 99.7%.Results demonstrate comparable improvement in accuracy and significantly minimize the computational time with recent state-of-the-art techniques.展开更多
In quadrupeds,the cervical and lumbar circuits work together to achieve the speed-dependent gait expression.While most studies have focused on how local lumbar circuits regulate limb coordination and gaits,relatively ...In quadrupeds,the cervical and lumbar circuits work together to achieve the speed-dependent gait expression.While most studies have focused on how local lumbar circuits regulate limb coordination and gaits,relatively few studies are known about cervical circuits and even less about locomotor gaits.We use the previously published models by Danner et al.(DANNER,S.M.,SHEVTSOVA,N.A.,FRIGON,A.,and RYBAK,I.A.Computational modeling of spinal circuits controlling limb coordination and gaits in quadrupeds.e Life,6,e31050(2017))as a basis,and modify it by proposing an asymmetric organization of cervical and lumbar circuits.First,the model reproduces the typical speed-dependent gait expression in mice and more biologically appropriate locomotor parameters,including the gallop gait,locomotor frequencies,and limb coordination of the forelimbs.Then,the model replicates the locomotor features regulated by the M-current.The walk frequency increases with the M-current without affecting the interlimb coordination or gaits.Furthermore,the model reveals the interaction mechanism between the brainstem drive and ionic currents in regulating quadrupedal locomotion.Finally,the model demonstrates the dynamical properties of locomotor gaits.Trot and bound are identified as attractor gaits,walk as a semi-attractor gait,and gallop as a transitional gait,with predictable transitions between these gaits.The model suggests that cervical-lumbar circuits are asymmetrically recruited during quadrupedal locomotion,thereby providing new insights into the neural control of speed-dependent gait expression.展开更多
Chakouyi(CKY)horses from the Qinghai-Xizang Plateau are well known for their unique lateral gaits and high-altitude adaptation,but genetic mechanisms underlying these phenotypes remain unclear.This study presents a co...Chakouyi(CKY)horses from the Qinghai-Xizang Plateau are well known for their unique lateral gaits and high-altitude adaptation,but genetic mechanisms underlying these phenotypes remain unclear.This study presents a comparison of 60 newly resequenced genomes of gaited CKY horses with 139 public genomes from 19 horse breeds.Population structure analyses(admixture,PCA,and neighbor-joining tree)reveal a close genetic relationship between CKY and other highland breeds(Tibetan and Chaidamu horses).Compared with other Chinese breeds,CKY horses present reduced nucleotide diversity(θπ)and lower inbreeding(FROHcoefficient),suggesting possible selective pressures.A key region on chromosome 23(Chr23:22.3-22.6 Mb)is associated with the lateral gaits and harbors a highly prevalent nonsense mutation(Chr 23:22,391,254 C>A,Ser301STOP)in the DMRT3 gene,with an 88%homozygosity rate,which is strongly correlated with the distinctive gait of CKY horses.Furthermore,selection signals reveal that the EPAS1 gene is related to high-altitude adaptation,and the CAT gene contributes to altitude resilience in CKY horses.These findings suggest that preserving genetic diversity is essential for maintaining the unique gaits and high-altitude adaptations of CKY horses.展开更多
Surveillance systems can take various forms,but gait-based surveillance is emerging as a powerful approach due to its ability to identify individuals without requiring their cooperation.In the existing studies,several...Surveillance systems can take various forms,but gait-based surveillance is emerging as a powerful approach due to its ability to identify individuals without requiring their cooperation.In the existing studies,several approaches have been suggested for gait recognition;nevertheless,the performance of existing systems is often degraded in real-world conditions due to covariate factors such as occlusions,clothing changes,walking speed,and varying camera viewpoints.Furthermore,most existing research focuses on single-person gait recognition;however,counting,tracking,detecting,and recognizing individuals in dual-subject settings with occlusions remains a challenging task.Therefore,this research proposed a variant of an automated gait model for occluded dual-subject walk scenarios.More precisely,in the proposed method,we have designed a deep learning(DL)-based dual-subject gait model(DSG)involving three modules.The first module handles silhouette segmentation,localization,and counting(SLC)using Mask-RCNN with MobileNetV2.The next stage uses a Convolutional block attention module(CBAM)-based Siamese network for frame-level tracking with a modified gallery setting.Following the last,gait recognition based on regionbased deep learning is proposed for dual-subject gait recognition.The proposed method,tested on Shri Mata Vaishno Devi University(SMVDU)-Multi-Gait and Single-Gait datasets,shows strong performance with 94.00%segmentation,58.36%tracking,and 63.04%gait recognition accuracy in dual-subject walk scenarios.展开更多
OBJECTIVE:To compare the changes in gait parameters before and after the treatment of lateral ankle sprain using the rotating-pulling-poking manipulation, and explore the potential bio-mechanical mechanism of this man...OBJECTIVE:To compare the changes in gait parameters before and after the treatment of lateral ankle sprain using the rotating-pulling-poking manipulation, and explore the potential bio-mechanical mechanism of this manipulation. METHODS:Forty patients with lateral ankle sprains were randomly divided into two groups in a 1∶1 ratio using a random number table. The experimental group were treated by rotating-pulling-poking manipulation and elastic bandage external fixation, while the control group were treated by ice compress and elastic bandage external fixation. The treatment courses of the two groups were both 2 weeks. We used a three-dimensional motion capture system for kinematic measurements and a Bertec gait analysis force measurement system for mechanical measurements, and compared the changes in gait parameters between the two groups of patients before and after treatment. RESULTS:Intragroup comparison showed that the support time, swing time, peak of back extension, peak of plantar flexion, peak of toe pedal force, and peak of heel landing force of the affected feet in the experimental group were significantly improved compared to those before treatment(P < 0.05). The swing time of the affected feet in the control group was significantly improved compared to that before treatment(P < 0.05). The inter group comparison showed that the gait speed, stride length, peak of back extension, peak of plantar flexion, peak of toe pedal force, and peak of heel landing force of the affected feet in the experimental group were significantly better than those in the control group(P < 0.05). CONCLUSIONS:The rotating-pulling-poking manipulation can effectively improve the patient's gait and range of motion of the affected ankle joint, and enhance the negative gravity in the vertical direction of the affected foot, and the braking and driving forces in the front and back directions. This may be the potential biomechanical mechanism of the rotating-pulling-poking manipulation for treating lateral ankle sprain.展开更多
Gait recognition,a promising biometric technology,relies on analyzing individuals' walking patterns and offers a non-intrusive and convenient approach to identity verification.However,gait recognition accuracy is ...Gait recognition,a promising biometric technology,relies on analyzing individuals' walking patterns and offers a non-intrusive and convenient approach to identity verification.However,gait recognition accuracy is often compromised by external factors such as changes in viewpoint and attire,which present substantial challenges in practical applications.To enhance gait recognition performance under diverse viewpoints and complex conditions,a global-local part-shift network is proposed in this paper.This framework integrates two novel modules:the part-shift feature extractor and the dynamic feature aggregator.The part-shift feature extractor strategically shifts body parts to capture the intrinsic relationships between non-adjacent regions,enriching the recognition process with both global and local spatial features.The dynamic feature aggregator addresses long-range dependency issues by incorporating multi-range temporal modeling,effectively aggregating information across parts and time steps to achieve a more robust recognition outcome.Comprehensive experiments on the CASIA-B dataset demonstrate that the proposed global-local part-shift network delivers superior performance compared with state-of-the-art methods,highlighting its potential for practical deployment.展开更多
Freezing of gait is a significant and debilitating motor symptom often observed in individuals with Parkinson's disease.Resting-state functional magnetic resonance imaging,along with its multi-level feature indice...Freezing of gait is a significant and debilitating motor symptom often observed in individuals with Parkinson's disease.Resting-state functional magnetic resonance imaging,along with its multi-level feature indices,has provided a fresh perspective and valuable insight into the study of freezing of gait in Parkinson's disease.It has been revealed that Parkinson's disease is accompanied by widespread irregularities in inherent brain network activity.However,the effective integration of the multi-level indices of resting-state functional magnetic resonance imaging into clinical settings for the diagnosis of freezing of gait in Parkinson's disease remains a challenge.Although previous studies have demonstrated that radiomics can extract optimal features as biomarkers to identify or predict diseases,a knowledge gap still exists in the field of freezing of gait in Parkinson's disease.This cross-sectional study aimed to evaluate the ability of radiomics features based on multi-level indices of resting-state functional magnetic resonance imaging,along with clinical features,to distinguish between Parkinson's disease patients with and without freezing of gait.We recruited 28 patients with Parkinson's disease who had freezing of gait(15 men and 13 women,average age 63 years)and 30 patients with Parkinson's disease who had no freezing of gait(16 men and 14 women,average age 64 years).Magnetic resonance imaging scans were obtained using a 3.0T scanner to extract the mean amplitude of low-frequency fluctuations,mean regional homogeneity,and degree centrality.Neurological and clinical characteristics were also evaluated.We used the least absolute shrinkage and selection operator algorithm to extract features and established feedforward neural network models based solely on resting-state functional magnetic resonance imaging indicators.We then performed predictive analysis of three distinct groups based on resting-state functional magnetic resonance imaging indicators indicators combined with clinical features.Subsequently,we conducted 100 additional five-fold cross-validations to determine the most effective model for each classification task and evaluated the performance of the model using the area under the receiver operating characteristic curve.The results showed that when differentiating patients with Parkinson's disease who had freezing of gait from those who did not have freezing of gait,or from healthy controls,the models using only the mean regional homogeneity values achieved the highest area under the receiver operating characteristic curve values of 0.750(with an accuracy of 70.9%)and 0.759(with an accuracy of 65.3%),respectively.When classifying patients with Parkinson's disease who had freezing of gait from those who had no freezing of gait,the model using the mean amplitude of low-frequency fluctuation values combined with two clinical features achieved the highest area under the receiver operating characteristic curve of 0.847(with an accuracy of 74.3%).The most significant features for patients with Parkinson's disease who had freezing of gait were amplitude of low-frequency fluctuation alterations in the left parahippocampal gyrus and two clinical characteristics:Montreal Cognitive Assessment and Hamilton Depression Scale scores.Our findings suggest that radiomics features derived from resting-state functional magnetic resonance imaging indices and clinical information can serve as valuable indices for the identification of freezing of gait in Parkinson's disease.展开更多
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(3...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.展开更多
Quadruped animals in the nature realize high energy efficiency locomotion by automatically changing their gait at different speeds.Inspired by this character,an efficient adaptive diagonal gait locomotion controller i...Quadruped animals in the nature realize high energy efficiency locomotion by automatically changing their gait at different speeds.Inspired by this character,an efficient adaptive diagonal gait locomotion controller is designed for quadruped robot.A unique gait planning method is proposed in this paper.As the speed of robot varies,the gait cycle time and the proportion of stance and swing phase of each leg are adjusted to form a variety of gaits.The optimal joint torque is calculated by the controller combined with Virtual Model Control(VMC)and Whole-Body Control(WBC)to realize the desired motion.The gait and step frequency of the robot can automatically adapt to the change of speed.Several experiments are done with a quadruped robot made by our laboratory to verify that the gait can change automatically from slow-trotting to flying-trot during the period when speed is from 0 to 4 m/s.The ratio of swing phase is from less than 0.5 to more than 0.5 to realize the running motion with four feet off the ground.Experiments have shown that the controller can indeed consume less energy when robot runs at a wide range of speeds comparing to the basic controller.展开更多
基金Supported by Hospital-level Project of Shiyan Taihe Hospital(2019JJXM117).
文摘[Objectives]To investigate the clinical efficacy of acupoint injection of nerve growth factors combined with task-oriented training for patients with post-stroke hemiplegic gait,and quantitatively evaluate the outcomes using the Gait Watch analysis system.[Methods]A total of 90 patients with post-stroke hemiplegia,who were hospitalized at the Rehabilitation Center of Taihe Hospital between January 2023 and December 2023,were selected for this study.The participants were randomly assigned to three groups:the task-oriented rehabilitation training group(control group I,n=30),the ordinary acupuncture combined with task-oriented training group(control group II,n=30),and the acupoint injection combined with task-oriented training group(observation group,n=30).Each group underwent treatment for 4 weeks.The Gait Watch analysis system was employed to assess the spatiotemporal gait parameters of the patients prior to treatment,as well as 2 weeks post treatment and 4 weeks post treatment.The efficacy of the treatment was subsequently analyzed.[Results]After 4 weeks of treatment,the spatiotemporal gait parameters,specifically step length,step speed,step frequency,percentage of the standing phase,and percentage of the swinging phase,exhibited significant improvement in the observation group compared to those before treatment(P<0.05).Furthermore,the degree of improvement in the observation group was superior to that observed in both control group I and control group II,with the differences reaching statistical significance(P<0.05).[Conclusions]Acupoint injection combined with task-oriented training has been shown to significantly enhance gait function in patients with post-stroke hemiplegia.The Gait Watch analysis system offers an accurate and objective quantitative assessment,making it a valuable tool for clinical application and promotion.
文摘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.
基金supported by the National Natural Science Foundation of China(62272049,62236006,62172045)the Key Projects of Beijing Union University(ZKZD202301).
文摘In recent years,gait-based emotion recognition has been widely applied in the field of computer vision.However,existing gait emotion recognition methods typically rely on complete human skeleton data,and their accuracy significantly declines when the data is occluded.To enhance the accuracy of gait emotion recognition under occlusion,this paper proposes a Multi-scale Suppression Graph ConvolutionalNetwork(MS-GCN).TheMS-GCN consists of three main components:Joint Interpolation Module(JI Moudle),Multi-scale Temporal Convolution Network(MS-TCN),and Suppression Graph Convolutional Network(SGCN).The JI Module completes the spatially occluded skeletal joints using the(K-Nearest Neighbors)KNN interpolation method.The MS-TCN employs convolutional kernels of various sizes to comprehensively capture the emotional information embedded in the gait,compensating for the temporal occlusion of gait information.The SGCN extracts more non-prominent human gait features by suppressing the extraction of key body part features,thereby reducing the negative impact of occlusion on emotion recognition results.The proposed method is evaluated on two comprehensive datasets:Emotion-Gait,containing 4227 real gaits from sources like BML,ICT-Pollick,and ELMD,and 1000 synthetic gaits generated using STEP-Gen technology,and ELMB,consisting of 3924 gaits,with 1835 labeled with emotions such as“Happy,”“Sad,”“Angry,”and“Neutral.”On the standard datasets Emotion-Gait and ELMB,the proposed method achieved accuracies of 0.900 and 0.896,respectively,attaining performance comparable to other state-ofthe-artmethods.Furthermore,on occlusion datasets,the proposedmethod significantly mitigates the performance degradation caused by occlusion compared to other methods,the accuracy is significantly higher than that of other methods.
基金Supported by National Natural Science Foundation of China,No.72174061 and No.71704053Key Research and Development Program of Zhejiang Province,No.2025C02106+1 种基金China Scholarship Council Foundation,No.202308330251Health Science and Technology Project of Zhejiang Provincial Health Commission,No.2022KY370。
文摘BACKGROUND Older adults with mild cognitive impairment(MCI)often show motor dysfunction,including slower gait and impaired handwriting.While gait and handwriting parameters are promising for MCI screening,their combined potential to distinguish MCI from cognitively normal adults is unclear.AIM To assess gait and handwriting differences and their potential for screening MCI in older adults.METHODS Ninety-five participants,including 34 with MCI and 61 cognitively normal controls,were assessed for gait using the GAITRite^(R)system and handwriting with a dot-matrix pen.Five machine learning models were developed to assess the discriminative power of gait and handwriting data for MCI screening.RESULTS Compared to the cognitively normal group,the MCI group had slower gait velocity(Z=-2.911,P=0.004),shorter stride and step lengths(t=-3.005,P=0.003;t=2.863,P=0.005),and longer cycle,standing,and double support times(t=-2.274,P=0.025;t=-2.376,P=0.018;t=-2.717,P=0.007).They also had reduced cadence(t=2.060,P=0.042)and increased double support time variability(Z=-2.614,P=0.009).In handwriting,the MCI group showed lower average pressure(all tasks:Z=-2.135,P=0.033)and decreased accuracy(graphic task:Z=-2.447,P=0.014;Chinese character task:Z=-3.078,P=0.002).In the graphic task,they demonstrated longer time in air(Z=-2.865,P=0.004),reduced X-axis maximum velocities(Z=-3.237,P=0.001),and lower accelerations(X-axis:Z=-2.880,P=0.004;Y-axis:Z=-1.987,P=0.047)and maximum accelerations(X-axis:Z=-3.998,P<0.001;Y-axis:Z=-2.050,P=0.040).The multimodal analysis achieved the highest accuracy(74.4%)with the Gradient Boosting Classifier.CONCLUSION Integrating gait and handwriting kinematics parameters provides a viable method for distinguishing MCI,potentially supporting large-scale screening,especially in resource-limited settings.
基金King Saud University,Grant/Award Number:RSP2024R157。
文摘Biometric characteristics are playing a vital role in security for the last few years.Human gait classification in video sequences is an important biometrics attribute and is used for security purposes.A new framework for human gait classification in video sequences using deep learning(DL)fusion assisted and posterior probability-based moth flames optimization(MFO)is proposed.In the first step,the video frames are resized and finetuned by two pre-trained lightweight DL models,EfficientNetB0 and MobileNetV2.Both models are selected based on the top-5 accuracy and less number of parameters.Later,both models are trained through deep transfer learning and extracted deep features fused using a voting scheme.In the last step,the authors develop a posterior probabilitybased MFO feature selection algorithm to select the best features.The selected features are classified using several supervised learning methods.The CASIA-B publicly available dataset has been employed for the experimental process.On this dataset,the authors selected six angles such as 0°,18°,90°,108°,162°,and 180°and obtained an average accuracy of 96.9%,95.7%,86.8%,90.0%,95.1%,and 99.7%.Results demonstrate comparable improvement in accuracy and significantly minimize the computational time with recent state-of-the-art techniques.
基金Project supported by the National Natural Science Foundation of China(Nos.12272092 and 12332004)。
文摘In quadrupeds,the cervical and lumbar circuits work together to achieve the speed-dependent gait expression.While most studies have focused on how local lumbar circuits regulate limb coordination and gaits,relatively few studies are known about cervical circuits and even less about locomotor gaits.We use the previously published models by Danner et al.(DANNER,S.M.,SHEVTSOVA,N.A.,FRIGON,A.,and RYBAK,I.A.Computational modeling of spinal circuits controlling limb coordination and gaits in quadrupeds.e Life,6,e31050(2017))as a basis,and modify it by proposing an asymmetric organization of cervical and lumbar circuits.First,the model reproduces the typical speed-dependent gait expression in mice and more biologically appropriate locomotor parameters,including the gallop gait,locomotor frequencies,and limb coordination of the forelimbs.Then,the model replicates the locomotor features regulated by the M-current.The walk frequency increases with the M-current without affecting the interlimb coordination or gaits.Furthermore,the model reveals the interaction mechanism between the brainstem drive and ionic currents in regulating quadrupedal locomotion.Finally,the model demonstrates the dynamical properties of locomotor gaits.Trot and bound are identified as attractor gaits,walk as a semi-attractor gait,and gallop as a transitional gait,with predictable transitions between these gaits.The model suggests that cervical-lumbar circuits are asymmetrically recruited during quadrupedal locomotion,thereby providing new insights into the neural control of speed-dependent gait expression.
基金the members of the Extending Station for Animal Husbandry and Veterinary Technology of Tianzhu Xizang Autonomous County for their help in sample collection and data acquisition.This research was supported by the Supercomputing Center of Lanzhou University and the Chakouyi horse conservation project from the Tianzhu Xizang Autonomous County Government([20]0097).
文摘Chakouyi(CKY)horses from the Qinghai-Xizang Plateau are well known for their unique lateral gaits and high-altitude adaptation,but genetic mechanisms underlying these phenotypes remain unclear.This study presents a comparison of 60 newly resequenced genomes of gaited CKY horses with 139 public genomes from 19 horse breeds.Population structure analyses(admixture,PCA,and neighbor-joining tree)reveal a close genetic relationship between CKY and other highland breeds(Tibetan and Chaidamu horses).Compared with other Chinese breeds,CKY horses present reduced nucleotide diversity(θπ)and lower inbreeding(FROHcoefficient),suggesting possible selective pressures.A key region on chromosome 23(Chr23:22.3-22.6 Mb)is associated with the lateral gaits and harbors a highly prevalent nonsense mutation(Chr 23:22,391,254 C>A,Ser301STOP)in the DMRT3 gene,with an 88%homozygosity rate,which is strongly correlated with the distinctive gait of CKY horses.Furthermore,selection signals reveal that the EPAS1 gene is related to high-altitude adaptation,and the CAT gene contributes to altitude resilience in CKY horses.These findings suggest that preserving genetic diversity is essential for maintaining the unique gaits and high-altitude adaptations of CKY horses.
基金supported by the MSIT(Ministry of Science and ICT),Republic of Korea,under the Convergence Security Core Talent Training Business Support Program(IITP-2025-RS-2023-00266605)supervised by the IITP(Institute for Information&Communications Technology Planning&Evaluation).
文摘Surveillance systems can take various forms,but gait-based surveillance is emerging as a powerful approach due to its ability to identify individuals without requiring their cooperation.In the existing studies,several approaches have been suggested for gait recognition;nevertheless,the performance of existing systems is often degraded in real-world conditions due to covariate factors such as occlusions,clothing changes,walking speed,and varying camera viewpoints.Furthermore,most existing research focuses on single-person gait recognition;however,counting,tracking,detecting,and recognizing individuals in dual-subject settings with occlusions remains a challenging task.Therefore,this research proposed a variant of an automated gait model for occluded dual-subject walk scenarios.More precisely,in the proposed method,we have designed a deep learning(DL)-based dual-subject gait model(DSG)involving three modules.The first module handles silhouette segmentation,localization,and counting(SLC)using Mask-RCNN with MobileNetV2.The next stage uses a Convolutional block attention module(CBAM)-based Siamese network for frame-level tracking with a modified gallery setting.Following the last,gait recognition based on regionbased deep learning is proposed for dual-subject gait recognition.The proposed method,tested on Shri Mata Vaishno Devi University(SMVDU)-Multi-Gait and Single-Gait datasets,shows strong performance with 94.00%segmentation,58.36%tracking,and 63.04%gait recognition accuracy in dual-subject walk scenarios.
基金the National Traditional Chinese Medicine Inheritance and Innovation Team Project:Traditional Chinese Medicine Innovation Team for Prevention and Treatment of Bone and Joint Degenerative Diseases (No. ZYYCXTD-C-202003)National Natural Science Foundation of China:the study on the Quantification and Mechanism of the rotating-pulling-poking manipulation in the Treatment of Lateral Ankle Sprain (No. 81473694)China Academy of Chinese Medical Sciences Science and Technology Innovation Project:Establishment and Promotion of a Simulation Operation Evaluation System for Rotating-pulling-poking Manipulation (CI2021A02015)。
文摘OBJECTIVE:To compare the changes in gait parameters before and after the treatment of lateral ankle sprain using the rotating-pulling-poking manipulation, and explore the potential bio-mechanical mechanism of this manipulation. METHODS:Forty patients with lateral ankle sprains were randomly divided into two groups in a 1∶1 ratio using a random number table. The experimental group were treated by rotating-pulling-poking manipulation and elastic bandage external fixation, while the control group were treated by ice compress and elastic bandage external fixation. The treatment courses of the two groups were both 2 weeks. We used a three-dimensional motion capture system for kinematic measurements and a Bertec gait analysis force measurement system for mechanical measurements, and compared the changes in gait parameters between the two groups of patients before and after treatment. RESULTS:Intragroup comparison showed that the support time, swing time, peak of back extension, peak of plantar flexion, peak of toe pedal force, and peak of heel landing force of the affected feet in the experimental group were significantly improved compared to those before treatment(P < 0.05). The swing time of the affected feet in the control group was significantly improved compared to that before treatment(P < 0.05). The inter group comparison showed that the gait speed, stride length, peak of back extension, peak of plantar flexion, peak of toe pedal force, and peak of heel landing force of the affected feet in the experimental group were significantly better than those in the control group(P < 0.05). CONCLUSIONS:The rotating-pulling-poking manipulation can effectively improve the patient's gait and range of motion of the affected ankle joint, and enhance the negative gravity in the vertical direction of the affected foot, and the braking and driving forces in the front and back directions. This may be the potential biomechanical mechanism of the rotating-pulling-poking manipulation for treating lateral ankle sprain.
文摘Gait recognition,a promising biometric technology,relies on analyzing individuals' walking patterns and offers a non-intrusive and convenient approach to identity verification.However,gait recognition accuracy is often compromised by external factors such as changes in viewpoint and attire,which present substantial challenges in practical applications.To enhance gait recognition performance under diverse viewpoints and complex conditions,a global-local part-shift network is proposed in this paper.This framework integrates two novel modules:the part-shift feature extractor and the dynamic feature aggregator.The part-shift feature extractor strategically shifts body parts to capture the intrinsic relationships between non-adjacent regions,enriching the recognition process with both global and local spatial features.The dynamic feature aggregator addresses long-range dependency issues by incorporating multi-range temporal modeling,effectively aggregating information across parts and time steps to achieve a more robust recognition outcome.Comprehensive experiments on the CASIA-B dataset demonstrate that the proposed global-local part-shift network delivers superior performance compared with state-of-the-art methods,highlighting its potential for practical deployment.
基金supported by the National Natural Science Foundation of China,No.82071909(to GF)the Natural Science Foundation of Liaoning Province,No.2023-MS-07(to HL)。
文摘Freezing of gait is a significant and debilitating motor symptom often observed in individuals with Parkinson's disease.Resting-state functional magnetic resonance imaging,along with its multi-level feature indices,has provided a fresh perspective and valuable insight into the study of freezing of gait in Parkinson's disease.It has been revealed that Parkinson's disease is accompanied by widespread irregularities in inherent brain network activity.However,the effective integration of the multi-level indices of resting-state functional magnetic resonance imaging into clinical settings for the diagnosis of freezing of gait in Parkinson's disease remains a challenge.Although previous studies have demonstrated that radiomics can extract optimal features as biomarkers to identify or predict diseases,a knowledge gap still exists in the field of freezing of gait in Parkinson's disease.This cross-sectional study aimed to evaluate the ability of radiomics features based on multi-level indices of resting-state functional magnetic resonance imaging,along with clinical features,to distinguish between Parkinson's disease patients with and without freezing of gait.We recruited 28 patients with Parkinson's disease who had freezing of gait(15 men and 13 women,average age 63 years)and 30 patients with Parkinson's disease who had no freezing of gait(16 men and 14 women,average age 64 years).Magnetic resonance imaging scans were obtained using a 3.0T scanner to extract the mean amplitude of low-frequency fluctuations,mean regional homogeneity,and degree centrality.Neurological and clinical characteristics were also evaluated.We used the least absolute shrinkage and selection operator algorithm to extract features and established feedforward neural network models based solely on resting-state functional magnetic resonance imaging indicators.We then performed predictive analysis of three distinct groups based on resting-state functional magnetic resonance imaging indicators indicators combined with clinical features.Subsequently,we conducted 100 additional five-fold cross-validations to determine the most effective model for each classification task and evaluated the performance of the model using the area under the receiver operating characteristic curve.The results showed that when differentiating patients with Parkinson's disease who had freezing of gait from those who did not have freezing of gait,or from healthy controls,the models using only the mean regional homogeneity values achieved the highest area under the receiver operating characteristic curve values of 0.750(with an accuracy of 70.9%)and 0.759(with an accuracy of 65.3%),respectively.When classifying patients with Parkinson's disease who had freezing of gait from those who had no freezing of gait,the model using the mean amplitude of low-frequency fluctuation values combined with two clinical features achieved the highest area under the receiver operating characteristic curve of 0.847(with an accuracy of 74.3%).The most significant features for patients with Parkinson's disease who had freezing of gait were amplitude of low-frequency fluctuation alterations in the left parahippocampal gyrus and two clinical characteristics:Montreal Cognitive Assessment and Hamilton Depression Scale scores.Our findings suggest that radiomics features derived from resting-state functional magnetic resonance imaging indices and clinical information can serve as valuable indices for the identification of freezing of gait in Parkinson's disease.
基金funded by the Research Foundation of Education Bureau of Hunan Province,China,under Grant Number 21B0060the National Natural Science Foundation of China,under Grant Number 61701179.
文摘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.
基金supported in part by the National Key Research and Development Program of China[Grant No.2020AAA0108900]the National Natural Science Foundation of China[No.91948201,62003190,62203268,61973185]+1 种基金the Open Research Projects of Zhejiang Lab(No.2022NB0AB06)the National Natural Science Foundation of Shandong Province of China[No.ZR2022QF027].
文摘Quadruped animals in the nature realize high energy efficiency locomotion by automatically changing their gait at different speeds.Inspired by this character,an efficient adaptive diagonal gait locomotion controller is designed for quadruped robot.A unique gait planning method is proposed in this paper.As the speed of robot varies,the gait cycle time and the proportion of stance and swing phase of each leg are adjusted to form a variety of gaits.The optimal joint torque is calculated by the controller combined with Virtual Model Control(VMC)and Whole-Body Control(WBC)to realize the desired motion.The gait and step frequency of the robot can automatically adapt to the change of speed.Several experiments are done with a quadruped robot made by our laboratory to verify that the gait can change automatically from slow-trotting to flying-trot during the period when speed is from 0 to 4 m/s.The ratio of swing phase is from less than 0.5 to more than 0.5 to realize the running motion with four feet off the ground.Experiments have shown that the controller can indeed consume less energy when robot runs at a wide range of speeds comparing to the basic controller.
