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

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

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

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

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

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

Smart Gait:A Gait Optimization Framework for Hexapod Robots

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

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

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

Research on human gait sensing based on triboelectric nanogenerator

To address the problem of frequent battery replacement for wearable sensors applied to fall detection among the elderly,a portable and lowcost triboelectric nanogenerator(TENG)-based self-powered sensor for human gait monitoring is proposed.The main fabrication materials of the TENG are polytetrafluoroethylene(PTFE)film,aluminum(Al)foil,and polyimide(PI)film,where PTFE and Al are the friction layer materials and the PI film is used to improve the output performance.Exploiting the ability of TENGs to monitor changes in environmental conditions,a self-powered sensor based on the TENG is placed in an insole to collect gait information.Since a TENG does not require a power source to convert physical and mechanical signals into electrical signals,the electrical signals can be used as sensing signals to be analyzed by a computer to recognize daily human activities and fall status.Experimental results show that the accuracy of the TENG-based sensor for recognizing human gait is 97.2%,demonstrating superior sensing performance and providing valuable insights for future monitoring of fall events in the elderly population.

Personalized Lower Limb Gait Reconstruction Modeling Based on RFA-ProMP

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

Gait Kinematic Analysis Facilitates Rapid Early Recovery Following Total Knee Arthroplasty

Objective: To explore gait kinematics analysis and evaluate the surgical efficacy of total knee arthroplasty (TKA), as well as its guiding significance for postoperative rehabilitation. Method: Fifty patients admitted to TKA treatment for knee osteoarthritis from December 2022 to July 2023 were included, which were divided into an intervention group (gait kinematics analysis group, n = 25) and a control group (conventional rehabilitation program group, n = 25). All patients underwent HSS score and KSS score before surgery (T0), 1 month after surgery (T1), 3 months after surgery (T2), and 6 months after surgery (T3). The intervention group underwent gait kinematics analysis at 1 month after surgery (T1) and 3 months after surgery (T2). Two groups measured the hip knee ankle angle (HKA), distal femoral lateral angle (LDFA), and proximal tibial medial angle (MPTA) on knee joint radiographs before and after surgery. Results: There was no significant difference in general information, preoperative imaging parameters, and functional scores between the two groups of patients. There was no significant difference in functional scores and postoperative prosthesis alignment between the two groups of patients in the first month after surgery. The intervention group showed a significant decrease in gait kinematic scores in the first month, with hip joint scores being particularly prominent (P 0.05). Conclusion: Gait kinematic analysis is helpful in evaluating the postoperative efficacy of TKA and can guide early and rapid recovery after TKA.

Using Linear and Non-Linear Techniques to Characterize Gait Coordination Patterns of Two Individuals with NGLY1 Deficiency

Individuals with NGLY1 Deficiency, an inherited autosomal recessive disorder, exhibit hyperkinetic movements including athetoid, myoclonic, dysmetric, and dystonic movements impacting both upper and lower limb motion. This report provides the first set of laboratory-based measures characterizing the gait patterns of two individuals with NGLY1 Deficiency, using both linear and non-linear measures, during treadmill walking, and compares them to neurotypical controls. Lower limb kinematics were obtained with a camera-based motion analysis system and bilateral time normalized lower limb joint time series waveforms were developed. Linear measures of joint range of motion, stride times and peak angular velocity were obtained, and confidence intervals were used to determine if there were differences between the patients and control. Correlations between participant and control mean joint waveforms were calculated and used to evaluate the similarities between patients and controls. Non-linear measures included: joint angle-angle diagrams, phase-portrait areas, and continuous relative phase (CRP) measures. These measures were used to assess joint coordination and control features of the lower limb motion. Participants displayed high correlations with their control counterparts for the hip and knee joint waveforms, but joint motion was restricted. Peak angular velocities were also significantly less than those of the controls. Both angle-angle and phase-portrait areas were less than the controls although the general shapes of those diagrams were similar to those of the controls. The NGLY1 Deficient participants’ CRP measures displayed disrupted coordination patterns with the knee-ankle patterns displaying more disruption than the hip-knee measures. Overall, the participants displayed a functional walking pattern that differed in many quantitative ways from those of the neurotypical controls. Using both linear and non-linear measures to characterize gait provides a more comprehensive and nuanced characterization of NGLY1 gait and can be used to develop interventions targeted toward specific aspects of disordered gait.

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

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

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

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

Gait Definition and Successive Gait-transition Method Based on Energy Consumption for a Quadruped

In nature, to realize the smooth motion for different speeds, the continuous gait transition is usually required for the quadrupeds. Thus, the gait simulation of quadrupeds is a requisite step to obtain the stable and energy-efficient gait for the walking machines. In this paper, the definitions of the two gait parameters, phasic difference and duty factor are presented, which can determine the gait of the quadrupeds. Then, several typical gaits of the quadrupeds are analyzed such that the seven standard gaits and corresponding parameters are summarized. Additionally, the variance law of the two parameters, which determine the relationship of gait transition, is analyzed. Furthermore, the quadruped gait derivative spectrum (QGDS) is proposed and the gait definition of the quadrupeds is presented. To minimize the power consumption, the choice criterion of gait, the optimal gait in terms of the motion speed, duty factory, and power consumption for the walking machines, is developed. Last, the continuous variance of the gait is implemented by the simulation of the gait transition from walk to trot, which evaluate the choice criterion and transition of gait.

融合轮廓增强和注意力机制的改进GaitSet步态识别方法

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

Inchworm locomotion gait for snakelike robot

To establish a universal and easily controlled gait for practical use of snakelike robot movement, an inchworm locomotion gait model based on a serpenoid curve is presented. By analyzing the relations of two adjacent waves in the process of locomotion and doing an approximation of the serpenoid curve, the motion function of relative angles between two adjacent links and the absolute angles between each link and the baseline on the traveling curve are built. Two efficiency criterions of the gait are given as the energy loss function f and the unit displacement in one cycle dunit.Three parameters of the criterions affecting the efficiency of the gait （ the number of links that form the traveling wave n, the included angle between two adjacent links α, and the phase difference of adjacent included angles β） are discussed by simulations and experiments. The results show that f is insensitive to n; raising n increases dunit significantly; the maximum wave amplitude of α is a decreasing function of n; and increasing α reduces the displacement influence off when n is determined. The gait model is suitable for different inchworm locomotions of a snakelike robot whose traveling waves are formed by different numbers of identical links. A wave formed by more links or a greater relative angle between two adjacent links both lead to greater velocity of the movement.

Motion mechanism and gait planning of a wheeled micro robot

Based on a novel shape memory alloy （SMA） actuator, a micro worming robot is presented. The robot adopts a wheeled moving mechanism. The principle of the robot＇s enlarged pace is introduced, and the structure and motion mechanism of the SMA actuator and the wheeled moving mechanism are discussed. The gait about the robot＇s rectilinear movement and turning movement is also planned. Under the effect of the eccentric wheel self-locking mechanisms and changing-direction mechanisms, the robot can move forward and backward, and turn actively, which overcomes the disadvantages of the traditional SMA micro robots to a certain extent. Furthermore, some experiments on the heating current of the SMA actuator and the robot＇s motion capability are carded out.

Neuroplasticity in post-stroke gait recovery and noninvasive brain stimulation

Gait disorders drastically affect the quality of life of stroke survivors,making post-stroke rehabilitation an important research focus.Noninvasive brain stimulation has potential in facilitating neuroplasticity and improving post-stroke gait impairment.However,a large inter-individual variability in the response to noninvasive brain stimulation interventions has been increasingly recognized.We first review the neurophysiology of human gait and post-stroke neuroplasticity for gait recovery,and then discuss how noninvasive brain stimulation techniques could be utilized to enhance gait recovery.While post-stroke neuroplasticity for gait recovery is characterized by use-dependent plasticity,it evolves over time,is idiosyncratic,and may develop maladaptive elements.Furthermore,noninvasive brain stimulation has limited reach capability and is facilitative-only in nature.Therefore,we recommend that noninvasive brain stimulation be used adjunctively with rehabilitation training and other concurrent neuroplasticity facilitation techniques.Additionally,when noninvasive brain stimulation is applied for the rehabilitation of gait impairment in stroke survivors,stimulation montages should be customized according to the specific types of neuroplasticity found in each individual.This could be done using multiple mapping techniques.

Coordinated control strategy for robotic-assisted gait training with partial body weight support

Walking is the most basic and essential part of the activities of daily living. To enable the elderly and non-ambulatory gait-impaired patients, the repetitive practice of this task, a novel gait training robot(GTR) was designed followed the end-effector principle, and an active partial body weight support(PBWS) system was introduced to facilitate successful gait training. For successful establishment of a walking gait on the GTR with PBWS, the motion laws of the GTR were planned to enable the phase distribution relationships of the cycle step, and the center of gravity(COG) trajectory of the human body during gait training on the GTR was measured. A coordinated control strategy was proposed based on the impedance control principle. A robotic prototype was developed as a platform for evaluating the design concepts and control strategies. Preliminary gait training with a healthy subject was implemented by the robotic-assisted gait training system and the experimental results are encouraging.

A Multi-View Gait Recognition Method Using Deep Convolutional Neural Network and Channel Attention Mechanism

In many existing multi-view gait recognition methods based on images or video sequences,gait sequences are usually used to superimpose and synthesize images and construct energy-like template.However,information may be lost during the process of compositing image and capture EMG signals.Errors and the recognition accuracy may be introduced and affected respectively by some factors such as period detection.To better solve the problems,a multi-view gait recognition method using deep convolutional neural network and channel attention mechanism is proposed.Firstly,the sliding time window method is used to capture EMG signals.Then,the back-propagation learning algorithm is used to train each layer of convolution,which improves the learning ability of the convolutional neural network.Finally,the channel attention mechanism is integrated into the neural network,which will improve the ability of expressing gait features.And a classifier is used to classify gait.As can be shown from experimental results on two public datasets,OULP and CASIA-B,the recognition rate of the proposed method can be achieved at 88.44%and 97.25%respectively.As can be shown from the comparative experimental results,the proposed method has better recognition effect than several other newer convolutional neural network methods.Therefore,the combination of convolutional neural network and channel attention mechanism is of great value for gait recognition.

OPTIMIZATION OF THE BIPED ROBOT GAIT USING GENETIC ALGORITHM

Based on the 7-link dynamic model in the sagittal plane and the 5-link dynamic model in the lateral plane, the parametric gait of the biped robot is designed using walking velocity, step length and height of the hip. According to the condition of the stability, body swings forward and backward to dynamically balance in sagittal plane and the whole biped swings left and right to dynamically balance in lateral plane. And the genetic algorithm is applied to obtain the optimal parameters on condition of keeping dynamic stability and the minimizing of the value of the dynamic balance.

Continuous and smooth gait transition in a quadruped robot based on CPG

To improve the smoothness of motion control in a quadruped robot, a continuous and smooth gait transition method based on central pattern generator （CPG） was presented to solve the unsmooth or failed problem which may result in phase-locked or sharp point with direct replacement of the gait matrix. Through improving conventional weight matrix, a CPG network and a MATLAB/ Simulink model were constructed based on the Hopf oscillator for gait generation and transition in the quadruped robot. A co-simulation was performed using ADAMS/MATLAB for the gait transition between walk and trot to verify the correctness and effectiveness of the proposed CPG gait generation and transition algorithms. Related methods and conclusions can technically support the motion control technology of the quadruped robot.