Gait Pattern Research Articles

Improving Task-Agnostic Energy Shaping Control of Powered Exoskeletons with Task/Gait Classification.

Emerging task-agnostic control methods offer a promising avenue for versatile assistance in powered exoskeletons without explicit task detection, but typically come with a performance trade-off for specific tasks and/or users. One such approach employs data-driven optimization of an energy shaping controller to provide naturalistic assistance across essential daily tasks with passivity/stability guarantees. This study introduces a novel control method that merges energy shaping with a machine learning-based classifier to deliver optimal support accommodating diverse individual tasks and users. The classifier detects transitions between multiple tasks and gait patterns in order to employ a more optimal, task-agnostic controller based on the weighted sum of multiple optimized energy-shaping controllers. To demonstrate the efficacy of this integrated control strategy, an in-silico assessment is conducted over a range of gait patterns and tasks, including incline walking, stairs ascent/descent, and stand-to-sit transitions. The proposed method surpasses benchmark approaches in 5-fold cross-validation ( ), yielding 93.17 ± 7.39% cosine similarity and 77.92 ± 19.76% variance-accounted-for across tasks and users. These findings highlight the control approach's adaptability in aligning with human joint moments across various tasks.

Serial kinematic analysis using inertial measurement units in growing dogs at risk of hip dysplasia

In this study, 54 dogs were examined at regular intervals from 12 weeks to 15 months of age using a gait analysis system based on inertial measurement sensors. At the end of the study, the dogs were examined for hip dysplasia (HD) and elbow dysplasia (ED) under sedation and officially classified at the Dysplasia Commission in Zurich. Gait parameters which are characteristic for the gait pattern of dogs, were calculated according to recent publications. These parameters were analysed for variance throughout the entire study period and assigned to healthy dogs and those suffering from HD. The findings of the study show that dogs suffering from HD exhibit a more unsteady gait pattern, e.g. higher variance, as they grow.

A study on machine learning-based prediction of cerebellar ataxia using gait analysis and accelerometer data

In the neurological field, predicting Cerebellar Ataxia (CA) is based on analyzing gait values of human actions. Analyzing Gait (AoG) can potentially guide effective treatment strategies. This study aimed to create a machine-learning model for predicting AoG using gait patterns indicative of pre-AoG conditions. During the execution of designed walking tasks to provokeAoG, accelerometers were attached to the lower back of 21 subjects as they performed 12 different walking positions to collect acceleration impulses. The participants engaged in walking exercises for one minute at 12 different walking speeds on a split-belt treadmill, ranging from 0.6 to 1.7 m/s in 0.1 m/s increments. The speed sequence was randomized and concealed from the subjects to minimize fatigue effects. Prior research studies have surveyed machine-learning algorithms such as support vector machine (SVM) and k-nearest neighbors (KNN). These algorithms demonstrate strong performance, especially when the dataset is trivial, and the classification is binary. SVM, KNN, decision trees, and XGBoost algorithms were utilized in the proposed study on the CA dataset. Our findings revealed that the AdaBoost algorithm, with its high precision, offers a more precise categorisation of the severity of CA disease, instilling confidence in the study's findings.

Safety Footwear Impact on Workers' Gait and Foot Problems: A Comparative Study.

In this study, we hypothesized that safety footwear (SF) impacts gait patterns, potentially contributing to the podiatric symptoms reported by workers. The purpose of this work was to compare the gait analyses of workers wearing SF and sneakers using inertial sensors while also examining the occurrence of foot problems. A consecutive cohort of workers from different occupational sectors who wore SF during their work shifts were prospectively assessed through a gait analysis. The gait analysis was conducted under two conditions: first, while wearing SF, and second, while wearing sneakers. In both conditions, inertial sensors were used (Wiva® MOB). Participants also underwent a podiatric physical examination to evaluate foot problems. This study shows that SF resulted in a worsening gait pattern compared to sneakers in both genders. The impact was particularly pronounced in female participants, resulting in a significant decline in walking speed and cadence. Discomfort was reported by 83.3% of participants, with a higher prevalence in females (46.6% vs. 36.6%). The SF group exhibited an elevated prevalence of foot problems, with no significant gender variations. It seems that foot problems are more likely to occur when a foot deformity, such as flat or cavus foot or hallux valgus, is present. This study suggests that SF may contribute to the reported podiatric symptoms among workers. Certain footwear characteristics, including weight, mis-fit, and inadequate design, may be factors associated with footwear discomfort and adverse gait patterns, potentially leading to increased foot problems among workers.

Arm Swing Movements during Walking as an Early Predictor of Multiple Sclerosis Progression

Persons with Multiple Sclerosis (pwMS) are at a high risk of falling, with abnormal gait pattern. The upper limbs play an important role in postural control and gait stability. The presence of arm swing changes during walking in pwMS, especially in the early period, may be an indicator of balance problems. The current study aimed to assess arm swing during walking in early MS. A total of 18 pwMS were evaluated in two time points. The first time was after their first (stable) diagnosis (pre-evaluation) and the second time was 3 months after the pre-evaluation. In addition, 10 healthy controls were evaluated once. Arm swing analysis during walking, using video recording, was applied to both groups. Additionally, the MS group performed the Two-Minute Walk Test, Timed Up and Go, and Timed 25-Foot Walk Test. The pwMS showed similar joint angles at both the first and second evaluations. Only the elbow ROM value on the least affected side was lower in pwMS than healthy controls at the second evaluation (p = 0.027). The early MS patients showed altered arm swing pattern. As walking speed and mobility scores did not change over time, the decrease in elbow amplitude over a 3-month period indicates that the arm swing may present a pattern resulting from MS-specific disorders rather than being a compensatory mechanism in walking. From the earliest stages of the disease, variations in arm swing movements during walking may be considered as a disease progression-predictor for MS.

Enhancing gait recognition by multimodal fusion of mobilenetv1 and xception features via PCA for OaA-SVM classification

Gait recognition has become an increasingly promising area of research in the search for noninvasive and effective methods of person identification. Its potential applications in security systems and medical diagnosis make it an exciting field with wide-ranging implications. However, precisely recognizing and assessing gait patterns is difficult, particularly in changing situations or from multiple perspectives. In this study, we utilized the widely used CASIA-B dataset to observe the performance of our proposed gait recognition model, with the aim of addressing some of the existing limitations in this field. Fifty individuals are randomly selected from the dataset, and the resulting data are split evenly for training and testing purposes. We begin by excerpting features from gait photos using two well-known deep learning networks, MobileNetV1 and Xception. We then combined these features and reduced their dimensionality via principal component analysis (PCA) to improve the model's performance. We subsequently assessed the model using two distinct classifiers: a random forest and a one against all support vector machine (OaA-SVM). The findings indicate that the OaA-SVM classifier manifests superior performance compared to the others, with a mean accuracy of 98.77% over eleven different viewing angles. This study is conducive to the development of effective gait recognition algorithms that can be applied to heighten people’s security and promote their well-being.

Microchannel pressure sensor for continuous and real-time wearable gait monitoring

A highly sensitive and multi-functional pressure sensor capable of continuous pressure readings is greatly needed, particularly for precise gait pattern analysis. Here, we fabricate a sensitive and reliable pressure sensor by employing eutectic gallium indium (EGaIn) liquid metal as the sensing material and EcoFlex 00-30 silicone as the substrate, via a low-cost process. The device architecture features a microchannel, creating two independent sensing devices, and the mechanical properties of the substrate and sensing material contribute to high stretchability and flexibility, resulting in a sensitivity of 66.07 MPa−1 and a low measurement resolution of 0.056 kPa. The sensor detects applied pressure accurately and can distinguish pressure distribution across a wide area. We demonstrate high efficiency for monitoring human walking gait at various speeds when a single sensor is attached to the foot, and can differentiate between walking postures. This device has strong potential for clinical and rehabilitation applications in gait analysis.

Smartphone IMU Sensors for Human Identification through Hip Joint Angle Analysis.

Gait monitoring using hip joint angles offers a promising approach for person identification, leveraging the capabilities of smartphone inertial measurement units (IMUs). This study investigates the use of smartphone IMUs to extract hip joint angles for distinguishing individuals based on their gait patterns. The data were collected from 10 healthy subjects (8 males, 2 females) walking on a treadmill at 4 km/h for 10 min. A sensor fusion technique that combined accelerometer, gyroscope, and magnetometer data was used to derive meaningful hip joint angles. We employed various machine learning algorithms within the WEKA environment to classify subjects based on their hip joint pattern and achieved a classification accuracy of 88.9%. Our findings demonstrate the feasibility of using hip joint angles for person identification, providing a baseline for future research in gait analysis for biometric applications. This work underscores the potential of smartphone-based gait analysis in personal identification systems.

Understanding mechanisms of generalization following locomotor adaptation

Our nervous system has the remarkable ability to adapt our gait to accommodate changes in our body or surroundings. However, our adapted walking patterns often generalize only partially (or not at all) between different contexts. Here, we sought to understand how the nervous system generalizes adapted gait patterns from one context to another. Through a series of split-belt treadmill walking experiments, we evaluated different mechanistic hypotheses to explain the partial generalization of adapted gait patterns from split-belt treadmill to overground walking. In support of the credit assignment hypothesis, our experiments revealed the central finding that adaptation involves recalibration of two distinct forward models. Recalibration of the first model generalizes to overground walking, suggesting that the model represents the general movement dynamics of our body. On the other hand, recalibration of the second model does not generalize to overground walking, suggesting the model represents dynamics specific to treadmill walking. These findings reveal that there is a predefined portion of forward model recalibration that generalizes across context, leading to overall partial generalization of walking adaptation.

Overground Gait Training With a Wearable Robot in Children With Cerebral Palsy: A Randomized Clinical Trial.

Cerebral palsy (CP) is the most common developmental motor disorder in children. Robot-assisted gait training (RAGT) using a wearable robot can provide intensive overground walking experience. To investigate the effectiveness of overground RAGT in children with CP using an untethered, torque-assisted, wearable exoskeletal robot. This multicenter, single-blind randomized clinical trial was conducted from September 1, 2021, to March 31, 2023, at 5 rehabilitation institutions in Korea. Ninety children with CP in Gross Motor Function Classification System levels II to IV were randomized. The RAGT group underwent 18 sessions of RAGT during 6 weeks, whereas the control group received standard physical therapy for the same number of sessions during the same period. The primary outcome measure was the Gross Motor Function Measure 88 (GMFM-88) score. Secondary outcome measures were the GMFM-66, Pediatric Balance Scale, selective control assessment of the lower extremity, Pediatric Evaluation of Disability Inventory-Computer Adaptive Test (PEDI-CAT), 6-minute walking test scores (distance and oxygen consumption), muscle and fat mass via bioelectrical impedance analysis, and gait parameters measured via 3-dimensional analysis. All assessments were performed for all patients at baseline, at the end of the 6-week intervention, and after the 4-week follow-up. Of the 90 children (mean [SD] age, 9.51 [2.48] years; 49 [54.4%] male and 41 [45.6%] female) in the study, 78 (86.7%) completed the intervention, with 37 participants (mean [SD] age, 9.57 [2.38] years; 19 [51.4%] male) and 41 participants (mean [SD] age, 9.32 [2.37] years; 26 [63.4%] male) randomly assigned to the RAGT and control groups, respectively. Changes in the RAGT group significantly exceeded changes in the control group in GMFM-88 total (mean difference, 2.64; 95% CI, 0.50-4.78), GMFM-E (mean difference, 2.70; 95% CI, 0.08-5.33), GMFM-66 (mean difference, 1.31; 95% CI, 0.01-2.60), and PEDI-CAT responsibility domain scores (mean difference, 2.52; 95% CI, 0.42-4.63), indicating independence in daily living at postintervention assessment. At the 4-week follow-up, the RAGT group showed significantly greater improvements in balance control (mean difference, 1.48; 95% CI, 0.03-2.94) and Gait Deviation Index (mean difference, 6.48; 95% CI, 2.77-10.19) compared with the control group. In this randomized clinical trial, overground RAGT using a wearable robot significantly improved gross motor function and gait pattern. This new torque-assisted wearable exoskeletal robot, based on assist-as-needed control, may complement standard rehabilitation by providing adequate assistance and therapeutic support to children with CP. CRIS Identifier: KCT0006273.

Unraveling stroke gait deviations with movement analytics, more than meets the eye: a case control study.

This study aimed to identify and quantify the kinematic and kinetic gait deviations in post-stroke hemiplegic patients with matched healthy controls using Statistical Parametric Mapping (SPM). Fifteen chronic stroke patients [4 females, 11 males; age 53.7 (standard deviation 12.2) years; body mass 65.4 (10.4) kg; standing height 168.5 (9.6) cm] and 15 matched healthy controls [4 females, 11 males; age 52.9 (11.7) years; body weight 66.5 (10.7) years; standing height 168.3 (8.8) cm] were recruited. In a 10-m walking task, joint angles, ground reaction forces (GRF), and joint moments were collected, analyzed, and compared using SPM for an entire gait cycle. Generally, when comparing the stroke patients' affected (hemiplegic) and less-affected (contralateral) limbs with the control group, SPM identified significant differences in the late stance phase and early swing phase in the joint angles and moments in bilateral limbs (all p < 0.005). In addition, the vertical and anteroposterior components of GRF were significantly different in various periods of the stance phase (all p < 0.005), while the mediolateral component showed no differences between the two groups. SPM was able to detect abnormal gait patterns in both the affected and less-affected limbs of stroke patients with significant differences when compared with matched controls. The findings draw attention to significant quantifiable gait deviations in the less-affected post-stroke limb with the potential impact to inform gait retraining strategies for clinicians and physiotherapists.

Kinematic characteristics of daily gait patterns perceived as beautiful by third parties

Research questionThis study was designed for kinematic clarification of those characteristics of gait in daily gait behavior which are recognized as beautiful. MethodsThe gait motions of 50 healthy adults across a flat floor were measured using a markerless motion capture system. After skeletal modeled gait animations were created, the gait aesthetics of each were rated using a questionnaire survey. Animations were created from two perspectives: sagittal and frontal planes. We asked a third party to observe these animations and to rate the gait beauty. Raters had received no special gait training. The study examined 166 participants in the sagittal plane survey and 175 participants in the frontal plane survey. From the questionnaire survey results, we produced four gait groups and extracted the kinematic characteristics of the gait groups which were recognized as beautiful. The spatiotemporal parameters including arm swing, joint angles of the lower limbs, and trunk tilt angle were calculated and compared statistically among groups. ResultsThe following features were extracted as beautiful gait characteristics: (1) long gait cycle; (2) long double limb support time; (3) narrow step width; (4) large arm swing length; (5) hands and feet located close to the central axis of the body; (6) flexion of the knee joints of both legs suppressed during the double limb support phase, with knees kept in extension; and (7) trunk kept in the median or posterior tilt.

Gait Reconstruction Strategy Using Botulinum Toxin Therapy Combined with Rehabilitation.

Numerous studies have established a robust body of evidence for botulinum toxin A (BoNT-A) therapy as a treatment for upper motor neuron syndrome. These studies demonstrated improvements in spasticity, range of joint motion, and pain reduction. However, there are few studies that have focused on improvement of paralysis or functional enhancement as the primary outcome. This paper discusses the multifaceted aspects of spasticity assessment, administration, and rehabilitation with the goal of optimising the effects of BoNT-A on lower-limb spasticity and achieving functional improvement and gait reconstruction. This paper extracts studies on BoNT-A and rehabilitation for the lower limbs and provides new knowledge obtained from them. From these discussion,, key points in a walking reconstruction strategy through the combined use of BoNT-A and rehabilitation include: (1) injection techniques based on the identification of appropriate muscles through proper evaluation; (2) combined with rehabilitation; (3) effective spasticity control; (4) improvement in ankle joint range of motion; (5) promotion of a forward gait pattern; (6) adjustment of orthotics; and (7) maintenance of the effects through frequent BoNT-A administration. Based on these key points, the degree of muscle fibrosis and preintervention walking speed may serve as indicators for treatment strategies. With the accumulation of recent studies, a study focusing on walking functions is needed. As a result, it is suggested that BoNT-A treatment for lower limb spasticity should be established not just as a treatment for spasticity but also as a therapeutic strategy in the field of neurorehabilitation aimed at improving walking function.

Can International Classification of Functioning, Disability and Health (ICF) Be Used for Prediction of Work Capacity and Employment Status in Multiple Sclerosis?

Background: Multiple sclerosis (MS) affects many body functions and activities, including work capacity and ability to work. An evaluation of work-related parameters is important to understand the barriers to maintaining the job. The aim of this study was to evaluate if a Comprehensive International Classification of Functioning, Disability and Health (ICF) core set for MS can be used to predict work capacity and employment status. Methods: The cohort included 151 participants with MS (99 female/52 male, mean age 49 years) referred for a work capacity evaluation. Results: 71 (47.0%) were employed and a major part (131, 86.7%) had a work capacity between 20 and 40% with no difference between those who were employed and those who were unemployed. The analysis revealed that age and the following categories explained 68.8% of the work capacity: b770 Gait pattern functions; b730 Muscle power functions; b134 Sleep functions; d845 Acquiring, keeping and terminating a job; and b620 Urination functions. The following categories in 79.5% predicted ability to work: b164 Higher-level cognitive functions; d510 Washing oneself; d630; Preparing meals; and d870 Economic self-sufficiency. Conclusions: Here, we show that different functions/activities predicted work capacity in comparison with employment status in MS. Therefore, ICF should be implemented when assessing work ability.

Smartphone usage during walking decreases the positive persistency in gait cycle variability

Gait cycle variability during steady walking, described by the stride interval time series, has been used as a gait-stability-related measure. In particular, the positive persistency in the stride intervals with 1/f-like fluctuation and reduction of the persistency are the well-documented metrics that can characterize gait patterns of healthy young adults and elderly including patients with neurological diseases, respectively. Here, we examined effects of a dual task on gait cycle variability in healthy young adults, based on the mean and standard deviation statistics as well as the positive persistency of the stride intervals during steady walking on a treadmill. Specifically, three gait conditions were examined: control condition, non-cognitive task with holding a smartphone in front of the chest using their dominant hand and looking fixedly at a blank screen of the smartphone, and cognitive motor task with holding a smartphone as in the non-cognitive task and playing a puzzle game displayed on the smartphone by one-thumb operation. We showed that only the positive persistency, not the mean and standard deviation statistics, was affected by the cognitive and motor load of smartphone usage in the cognitive condition. More specifically, the positive persistency exhibited in the control and the non-cognitive conditions was significantly reduced in the cognitive condition. Our results suggest that the decrease in the positive persistency during the cognitive task, which might represent the deterioration of healthy gait pattern, is caused endogenously by the cognitive and motor load, not necessarily by the reduction of visual field as often hypothesized.

스포츠 리듬운동을 이용한 기능성 인지보행훈련이 발달 장애 아동의 보행패턴과 인지능력에 미치는 영향

Objectives The purpose of this study was to help neurological disorders seen of the trends of children with developmental disabilities and improve their quality of life by investigating the functional cognitive gait in using the sports rhythm training on gait pattern and cognitive function in trends of children with developmental disabilities. Methods In order to achieve the purpose of this study, 39 trends of children with developmental disabilities at N located in K city, were selected at random and allocated 13 trends of children with developmental disabilities each for the Non Exercise group (NEG), Deficient Exercise group(DEG), Over Exercise group (OEG) and measured kinematic gait pattern and cognitive function before and after the experiment. The data obtained from this study were analyzed using the SPSS 26.0 statistical program the two-way repeated measures ANOVA and Bonferroni post-hoc test was performed. Results The conclusions obtained through this study were as follows. First, as a result of analyzing the change of gait pattern to functional cognitive gait in using the sports rhythm training program, there were significantly improved in velocity, cadence, step length, stride length in OEG compared with NEG and DEG after 24 weeks. Second, as a result of analyzing the change of cognitive function according to functional cognitive gait in using the sports rhythm training, there were significantly improved in body part, number display, movement, operation in OEG compared with NEG and DEG after 24 weeks. Conclusions As conclusions, this study confirmed that the functional cognitive gait in using the sports rhythm training program could improve the gait pattern and cognitive function in the trends of children with developmental disabilities.

Evaluating CNN Models for Gait Recognition: A Study on the CASIA-B Dataset

Gait recognition, a form of biometric identification, has garnered considerable interest for its ability to identify individuals from a distance. Yet, its effectiveness depends significantly on the precision of the underlying categorization models. This study thoroughly evaluates many Convolutional Neural Network (CNN) models to determine their effectiveness in properly identifying and categorizing gait patterns. We train and test several CNN architectures using advanced deep learning techniques on the widely known CASIA-B dataset, which is a benchmark dataset for gait recognition research. The analyses involve models like as VGG16, VGG19, NASNetLarge, NASNetMobile, EfficientNetB0, and Xception, each well-known for their effectiveness in picture categorization tasks. We evaluate the performance of these models by conducting thorough experiments and detailed analysis, focusing on accuracy, validation loss, and other pertinent metrics. The Xception model demonstrated the highest accuracy of 97.17% of the models assessed. This model frequently surpasses similar models, demonstrating its strength and effectiveness in precisely recognizing gait patterns. On the other hand, the accuracy of other models varied from 9.78% to 93.85%, demonstrating the variable levels of efficacy among various designs. The findings have significant implications for the creation and implementation of gait recognition systems. The high precision demonstrated by the Xception model highlights the promise of CNN-based methods in progressing gait identification technology. Our work highlights the significance of choosing suitable model architectures for achieving the best performance in gait detection tasks. In the future, research may focus on using larger datasets and exploring other CNN architectures to improve the accuracy and reliability of gait detection systems. Our goal is to enhance gait recognition technology to improve biometric identification for security, surveillance, and healthcare applications. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 10(1), 2023 P 17-26

Impacts of asymmetric hip rotation angle on gait biomechanics in patients with knee osteoarthritis

BackgroundKnee Osteoarthritis (OA) is a highly prevalent age-related disease. The altered kinematic pattern of the knee joint as well as the adjacent joints affects to progression of knee OA. However, there is a lack of research on how asymmetry of the hip rotation angle affects the gait pattern in knee OA patients.Research questionWhat are the impacts of asymmetric hip rotation range on gait biomechanical characteristics and do the gait patterns differ between patients with knee OA and healthy elderly people?MethodsTwenty-nine female patients with knee OA and 15 healthy female elders as control group were enrolled in this study. The spatiotemporal parameters, kinematic and kinetic data during walking were measured using a three-dimensional motion capture system. The differences between knee OA and control group were analyzed using an independent t-test.ResultsThe knee OA group exhibited a significant reduction in hip internal rotation range and internal/external rotation ratio on more affected side (p < 0.05). Significant differences were found in spatiotemporal parameters except to the step width. Significant reductions were also found in kinematic parameters (pelvic lateral tilt range, sagittal angle ranges in hip, knee and ankle, knee adduction mean angle). There were also significant differences in vertical ground reaction force and knee adduction moment (p < 0.05).ConclusionsKnee OA patients have asymmetric hip rotation ranges. Especially limited hip internal rotation could lead to the reduction of pelvic lateral tilt, which may cause greater knee joint loading. Therefore, it is necessary to pay attention to recovery of hip rotation after knee surgery.

Valorization of Food Waste: Utilizing Natural Porous Materials Derived from Pomelo-Peel Biomass to Develop Triboelectric Nanogenerators for Energy Harvesting and Self-Powered Sensing.

Food waste is an enormous challenge, with implications for the environment, society, and economy. Every year around the world, 1.3 billion tons of food are wasted or lost, and food waste-associated costs are around $2.6 trillion. Waste upcycling has been shown to mitigate these negative impacts. This study's optimized pomelo-peel biomass-derived porous material-based triboelectric nanogenerator (PP-TENG) had an open circuit voltage of 58 V and a peak power density of 254.8 mW/m2. As porous structures enable such triboelectric devices to respond sensitively to external mechanical stimuli, we tested our optimized PP-TENG's ability to serve as a self-powered sensor of biomechanical motions. As well as successfully harvesting sufficient mechanical energy to power light-emitting diodes and portable electronics, our PP-TENGs successfully monitored joint motions, neck movements, and gait patterns, suggesting their strong potential for use in healthcare monitoring and physical rehabilitation, among other applications. As such, the present work opens up various new possibilities for transforming a prolific type of food waste into value-added products and thus could enhance long-term sustainability while reducing such waste.

Emerging trends in gait recognition based on deep learning: a survey.

Gait recognition, a biometric identification method, has garnered significant attention due to its unique attributes, including non-invasiveness, long-distance capture, and resistance to impersonation. Gait recognition has undergone a revolution driven by the remarkable capacity of deep learning to extract complicated features from data. An overview of the current developments in deep learning-based gait identification methods is provided in this work. We explore and analyze the development of gait recognition and highlight its uses in forensics, security, and criminal investigations. The article delves into the challenges associated with gait recognition, such as variations in walking conditions, viewing angles, and clothing as well. We discuss about the effectiveness of deep neural networks in addressing these challenges by providing a comprehensive analysis of state-of-the-art architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and attention mechanisms. Diverse neural network-based gait recognition models, such as Gate Controlled and Shared Attention ICDNet (GA-ICDNet), Multi-Scale Temporal Feature Extractor (MSTFE), GaitNet, and various CNN-based approaches, demonstrate impressive accuracy across different walking conditions, showcasing the effectiveness of these models in capturing unique gait patterns. GaitNet achieved an exceptional identification accuracy of 99.7%, whereas GA-ICDNet showed high precision with an equal error rate of 0.67% in verification tasks. GaitGraph (ResGCN+2D CNN) achieved rank-1 accuracies ranging from 66.3% to 87.7%, whereas a Fully Connected Network with Koopman Operator achieved an average rank-1 accuracy of 74.7% for OU-MVLP across various conditions. However, GCPFP (GCN with Graph Convolution-Based Part Feature Polling) utilizing graph convolutional network (GCN) and GaitSet achieves the lowest average rank-1 accuracy of 62.4% for CASIA-B, while MFINet (Multiple Factor Inference Network) exhibits the lowest accuracy range of 11.72% to 19.32% under clothing variation conditions on CASIA-B. In addition to an across-the-board analysis of recent breakthroughs in gait recognition, the scope for potential future research direction is also assessed.