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
[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
Gait coordination in lower limbs plays a critical role in maintaining stability of the human body during walking.For transfemoral amputees,the absence of limbs disrupts this coordination,reducing prosthesis control ac...Gait coordination in lower limbs plays a critical role in maintaining stability of the human body during walking.For transfemoral amputees,the absence of limbs disrupts this coordination,reducing prosthesis control accuracy.Hip-knee coordination mapping offers a feasible solution for lower-limb prosthesis control,involving the generation of a reference trajectory for the knee joint by leveraging information from the hip.However,current reference trajectories are usually derived from static models,which cannot generate reference trajectories robustly when dealing with perturbations.Therefore,this paper introduces a time-dependent model based on the Delayed Feedback Reservoir(DFR)for hip-knee coordination in lower-limb prosthetic control.Experimental results show that DFR outperforms classical gait planning approaches when facing perturbations,achieving a 20%lower Root Mean Square Error(RMSE)and reducing residuals by up to 18.14 degrees.This research contributes to understanding gait mapping approaches and emphasizes the potential of time-dependent models for robust and strong lower-limb prosthetic control.The discovery provides a novel way to enhance the perturbation adaptability of prosthetic control.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
The integration of human-robot collaboration(HRC)in manufacturing,particularly within the framework of Human-Cyber-Physical Systems(HCPS)and the emerging paradigm of Industry 5.0,has the potential to significantly enh...The integration of human-robot collaboration(HRC)in manufacturing,particularly within the framework of Human-Cyber-Physical Systems(HCPS)and the emerging paradigm of Industry 5.0,has the potential to significantly enhance productivity,safety,and ergonomics.However,achieving seamless collaboration requires robots to recognize the identity of individual human workers and perform appropriate collaborative operations.This paper presents a novel gait identity recognition method using Inertial Measurement Unit(IMU)data to enable personalized HRC in manufacturing settings,contributing to the human-centric vision of Industry 5.0.The hardware of the entire system consists of the IMU wearable device as the data source and a collaborative robot as the actuator,reflecting the interconnected nature of HCPS.The proposed method leverages wearable IMU sensors to capture motion data,including 3-axis acceleration,3-axis angular velocity.The two-tower Transformer architecture is employed to extract and analyze gait features.It consists of Temporal and Channel Modules,multi-head Auto-Correlation mechanism,and multi-scale convolutional neural network(CNN)layers.A series of optimization experiments were conducted to improve the performance of the model.The proposed model is compared with other state-of-the-art studies on two public datasets as well as one self-collected dataset.The experimental results demonstrate the better performance of our method in gait identity recognition.It is experimentally verified in the manufacturing environment involving four workers and one collaborative robot in an HRC assembly task,showcasing the practical applicability of this human-centric approach in the context of Industry 5.0.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
Gait,the unique pattern of how a person walks,has emerged as one of the most promising biometric features in modern intelligent sensing.Unlike fingerprints or facial characteristics,gait can be captured unobtrusively ...Gait,the unique pattern of how a person walks,has emerged as one of the most promising biometric features in modern intelligent sensing.Unlike fingerprints or facial characteristics,gait can be captured unobtrusively and at a distance,without requiring the subject’s awareness or cooperation.This makes it highly suitable for long-range surveillance,forensic investigation,and smart environments where contactless recognition is crucial.Traditional gait-recognition systems rely either on silhouettes,which capture the outer appearance of a person,or on skeletons,which describe the internal structure of human motion.Each modality provides only a partial understanding of gait.Silhouettes emphasize shape and contour but are easily distorted by clothing or carried objects;skeletons describe motion dynamics and limb coordination but lose discriminative details about body shape.This article presents the concept of Complementary Semantic Embedding(CSE),a unified framework that merges silhouette and skeleton information into a comprehensive semantic representation of human walking.By modeling the complementary nature of appearance and structure,the approach achieves more robust and accurate gait recognition even under challenging conditions.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
Bionic gait learning of quadruped robots based on reinforcement learning has become a hot research topic.The proximal policy optimization(PPO)algorithm has a low probability of learning a successful gait from scratch ...Bionic gait learning of quadruped robots based on reinforcement learning has become a hot research topic.The proximal policy optimization(PPO)algorithm has a low probability of learning a successful gait from scratch due to problems such as reward sparsity.To solve the problem,we propose a experience evolution proximal policy optimization(EEPPO)algorithm which integrates PPO with priori knowledge highlighting by evolutionary strategy.We use the successful trained samples as priori knowledge to guide the learning direction in order to increase the success probability of the learning algorithm.To verify the effectiveness of the proposed EEPPO algorithm,we have conducted simulation experiments of the quadruped robot gait learning task on Pybullet.Experimental results show that the central pattern generator based radial basis function(CPG-RBF)network and the policy network are simultaneously updated to achieve the quadruped robot’s bionic diagonal trot gait learning task using key information such as the robot’s speed,posture and joints information.Experimental comparison results with the traditional soft actor-critic(SAC)algorithm validate the superiority of the proposed EEPPO algorithm,which can learn a more stable diagonal trot gait in flat terrain.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
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.
基金supported by the National Natural Science Foundation of China(Grant Nos.12372065,12372022,and 11932015)Shanghai Pilot Program for Basic Research—Fudan University(Grant No.21TQ1400100-22TQ009).
文摘Gait coordination in lower limbs plays a critical role in maintaining stability of the human body during walking.For transfemoral amputees,the absence of limbs disrupts this coordination,reducing prosthesis control accuracy.Hip-knee coordination mapping offers a feasible solution for lower-limb prosthesis control,involving the generation of a reference trajectory for the knee joint by leveraging information from the hip.However,current reference trajectories are usually derived from static models,which cannot generate reference trajectories robustly when dealing with perturbations.Therefore,this paper introduces a time-dependent model based on the Delayed Feedback Reservoir(DFR)for hip-knee coordination in lower-limb prosthetic control.Experimental results show that DFR outperforms classical gait planning approaches when facing perturbations,achieving a 20%lower Root Mean Square Error(RMSE)and reducing residuals by up to 18.14 degrees.This research contributes to understanding gait mapping approaches and emphasizes the potential of time-dependent models for robust and strong lower-limb prosthetic control.The discovery provides a novel way to enhance the perturbation adaptability of prosthetic control.
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 National Natural Science Foundation of China(Grant Nos.52375031,52405038)Zhejiang Provincial Natural Science Foundation(Grant No.LRG25E050001)+4 种基金China Postdoctoral Science Foundation(Grant Nos.GZB20240654,2024M762812,2025T180371)the Priority-Funded Postdoctoral Research Project of Zhejiang Province(Grant No.ZJ2024013)the Dongfang Electric Corporation-Zhejiang University Joint Innovation Research Institutethe Bellwethers+X Research and Development Plan of Zhejiang Province(Grant Nos.2024C04057(CSJ),2025C01012)the Joint Research Project of Sci-Tech Innovation Community in Yangtze River Delta(Grant No.2023CSJGG1400)。
文摘The integration of human-robot collaboration(HRC)in manufacturing,particularly within the framework of Human-Cyber-Physical Systems(HCPS)and the emerging paradigm of Industry 5.0,has the potential to significantly enhance productivity,safety,and ergonomics.However,achieving seamless collaboration requires robots to recognize the identity of individual human workers and perform appropriate collaborative operations.This paper presents a novel gait identity recognition method using Inertial Measurement Unit(IMU)data to enable personalized HRC in manufacturing settings,contributing to the human-centric vision of Industry 5.0.The hardware of the entire system consists of the IMU wearable device as the data source and a collaborative robot as the actuator,reflecting the interconnected nature of HCPS.The proposed method leverages wearable IMU sensors to capture motion data,including 3-axis acceleration,3-axis angular velocity.The two-tower Transformer architecture is employed to extract and analyze gait features.It consists of Temporal and Channel Modules,multi-head Auto-Correlation mechanism,and multi-scale convolutional neural network(CNN)layers.A series of optimization experiments were conducted to improve the performance of the model.The proposed model is compared with other state-of-the-art studies on two public datasets as well as one self-collected dataset.The experimental results demonstrate the better performance of our method in gait identity recognition.It is experimentally verified in the manufacturing environment involving four workers and one collaborative robot in an HRC assembly task,showcasing the practical applicability of this human-centric approach in the context of Industry 5.0.
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.
文摘Gait,the unique pattern of how a person walks,has emerged as one of the most promising biometric features in modern intelligent sensing.Unlike fingerprints or facial characteristics,gait can be captured unobtrusively and at a distance,without requiring the subject’s awareness or cooperation.This makes it highly suitable for long-range surveillance,forensic investigation,and smart environments where contactless recognition is crucial.Traditional gait-recognition systems rely either on silhouettes,which capture the outer appearance of a person,or on skeletons,which describe the internal structure of human motion.Each modality provides only a partial understanding of gait.Silhouettes emphasize shape and contour but are easily distorted by clothing or carried objects;skeletons describe motion dynamics and limb coordination but lose discriminative details about body shape.This article presents the concept of Complementary Semantic Embedding(CSE),a unified framework that merges silhouette and skeleton information into a comprehensive semantic representation of human walking.By modeling the complementary nature of appearance and structure,the approach achieves more robust and accurate gait recognition even under challenging conditions.
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.
基金the National Natural Science Foundation of China(No.62103009)。
文摘Bionic gait learning of quadruped robots based on reinforcement learning has become a hot research topic.The proximal policy optimization(PPO)algorithm has a low probability of learning a successful gait from scratch due to problems such as reward sparsity.To solve the problem,we propose a experience evolution proximal policy optimization(EEPPO)algorithm which integrates PPO with priori knowledge highlighting by evolutionary strategy.We use the successful trained samples as priori knowledge to guide the learning direction in order to increase the success probability of the learning algorithm.To verify the effectiveness of the proposed EEPPO algorithm,we have conducted simulation experiments of the quadruped robot gait learning task on Pybullet.Experimental results show that the central pattern generator based radial basis function(CPG-RBF)network and the policy network are simultaneously updated to achieve the quadruped robot’s bionic diagonal trot gait learning task using key information such as the robot’s speed,posture and joints information.Experimental comparison results with the traditional soft actor-critic(SAC)algorithm validate the superiority of the proposed EEPPO algorithm,which can learn a more stable diagonal trot gait in flat terrain.
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.
