Gait Pattern Research Articles

Gait pattern modification based on ground contact adaptation using the robot-assisted training platform (RATP).

Robot-assisted rehabilitation and training systems are utilized to improve the functional recovery of individuals with mobility limitations. These systems offer structured rehabilitation through precise human-robot interaction, outperforming traditional physical therapy by delivering advantages such as targeted muscle recovery, optimization of walking patterns, and automated training routines tailored to the user's objectives and musculoskeletal attributes. In our research, we propose the development of a walking simulator that considers user-specific musculoskeletal information to replicate natural walking dynamics, accounting for factors like joint angles, muscular forces, internal user-specific constraints, and external environmental factors. The integration of these factors into robot-assisted training can provide a more realistic rehabilitation environment and serve as a foundation for achieving natural bipedal locomotion. Our research team has developed a robot-assisted training platform (RATP) that generates gait training sets based on user-specific internal and external constraints by incorporating a genetic algorithm (GA). We utilize the Lagrangian multipliers to accommodate requirements from the rehabilitation field to instantly reshape the gait patterns while maintaining their overall characteristics, without an additional gait pattern search process. Depending on the patient's rehabilitation progress, there are times when it is necessary to reorganize the training session by changing training conditions such as terrain conditions, walking speed, and joint range of motion. The proposed method allows gait rehabilitation to be performed while stably satisfying ground contact constraints, even after modifying the training parameters.

Motor interference on lateral pelvis shifting towards the paretic leg during walking and its cortical mechanisms in persons with stroke.

Motor interference, where new skill acquisition disrupts the performance of a previously learned skill, is a critical yet underexplored factor in gait rehabilitation post-stroke. This study investigates the interference effects of two different practice schedules, applying interleaved (ABA condition) and intermittent (A-A condition) pulling force to the pelvis during treadmill walking, on lateral pelvis shifting towards the paretic leg in individuals with stroke. Task A involved applying resistive pelvis force (pulling towards the non-paretic side), and Task B applied assistive force (pulling towards the paretic side) at the stance phase of the paretic leg during walking. Sixteen individuals with chronic stroke were tested for gait pattern changes, including lateral pelvis shifting and spatiotemporal gait parameters, and neurophysiological changes, including muscle activity in the paretic leg and beta band absolute power in the lesioned cortical areas. A-A condition demonstrated increased lateral pelvis shifting towards the paretic side, extended paretic stance time and longer non-paretic step length after force release while ABA condition did not show any changes. These changes in gait pattern after A-A condition were accompanied by increased muscle activities of the ankle plantarflexors, and hip adductors/abductors. A-A condition demonstrated greater changes in beta band power in the sensorimotor regions compared to ABA condition. These findings suggest that while walking practice with external force to the pelvis can improve lateral pelvis shifting towards the paretic leg post-stroke, practicing a new pelvis shifting task in close succession may hinder the performance of a previously obtained lateral pelvis shifting pattern during walking.

Theory of Biomechanical Evolution of the Rheumatoid Foot: A Narrative Review.

In patients with rheumatoid arthritis (RA), the pathological progression of lower limb biomechanics is established. Although specific aspects of RA gait patterns have been studied and described, we are aware of no studies of gait pattern compensations over the entire disease course. This study aimed to describe a model that could predict the evolution of lower limb pathomechanics in patients with RA. A literature review was conducted of electronic databases (MEDLINE, PEDro, Trip Database, DOAJ, BioMed Central, PLOS clinical trial, ScienceDirect, and CRD York University, AHRQ, NICE, Cochrane Library) to October 3, 2023. A theory was developed that all people with RA induce or augment gait evolution syndromes following the same biomechanical course. Specifically, we postulate the "rheumatoid equinus syndrome," the "rheumatoid abnormal pronation syndrome" and the "rheumatoid shuffle syndrome," which have never been described before. A new model of the evolution of gait compensation in RA is proposed. An important challenge of RA is that it increases the risk of ulcerative lesions, falls, pain, fractures, and healthcare costs. The proposed model can be used to reduce morbidity in this patient group by helping to explain and reduce the pain, deformity, and ankylosis of foot RA.

Automatic Detection of Fatigued Gait Patterns in Older Adults: An Intelligent Portable Device Integrating Force and Inertial Measurements with Machine Learning.

This study aimed to assess the feasibility of early detection of fatigued gait patterns for older adults through the development of a smart portable device. The smart device incorporated seven force sensors and a single inertial measurement unit (IMU) to measure regional plantar forces and foot kinematics. Data were collected from 18 older adults walking briskly on a treadmill for 60min. The optimal feature set for each recognition model was determined using forward sequential feature selection in a wrapper fashion through fivefold cross-validation. The recognition model was selected from four machine learning models through leave-one-subject-out cross-validation. Five selected characteristics that best represented the state of fatigue included impulse at the medial and lateral arches (increased, p = 0.002 and p < 0.001), contact angle and rotation range of angle in the sagittal plane (increased, p < 0.001), and the variability of the resultant swing angular acceleration (decreased, p < 0.001). The detection accuracy based on the dual signal source of IMU and plantar force was 99%, higher than the 95% accuracy based on the single source. The intelligent portable device demonstrated excellent generalization (ranging from 93 to 100%), real-time performance (2.79ms), and portability (32g). The proposed smart device can detect fatigue patterns with high precision and in real time. The application of this device possesses the potential to reduce the injury risk for older adults related to fatigue during gait.

Residual gait deviations in children treated by medial open reduction for developmental dysplasia of the hip at long-term follow-up: a comparison with healthy controls.

This study aimed to analyze and compare gait patterns and deviations at long-term follow-up in children who received medial open reduction (MOR) before 18months for unilateral or bilateral hip developmental dysplasia (DDH). A retrospective chart review was conducted on children who underwent MOR. The study population was divided into two groups: the unilateral group, including unilateral (five children with unilateral) and bilateral (five children with bilateral DDH). Ten healthy children were recruited for the control group. Spatiotemporal, kinematic, stiff-knee gait (SKG), and kinetic gait characteristics were analyzed. Stance time was significantly shorter in both the unilateral (median [IQR]; 590ms, [560.0-612.5] and bilateral (575ms, [550-637.5]) groups than in the control group (650, [602.5-677.5]) (p < 0.001), whereas swing time did not differ substantially (p = 0.065) There was no considerable difference in the mean knee flexion at swing between the unilateral (31.6°, [30-36]) and control (30.11°, [27.8-33.6] groups (p > 0.05), but the bilateral group (28.5°, [24.9-32.1]) showed the lower values than the other groups (p < 0.001 for bilateral vs unilateral group; p = 0.008 bilateral vs unilateral group). All the SKG parameters significantly differed among the groups in multi-group comparisons (p < 0.001 for each parameter). Three children had borderline SKG, and two had not-stiff limbs in the unilateral group. In the bilateral group, four children had stiff limbs, and one had borderline SKG. Most kinetic gait parameters were not statistically different between groups (p > 0.05). This study has revealed notable deviations in gait patterns of children with DDH treated by MOR at long-term follow-up compared to healthy children's gait. MOR could negatively affect pelvic motion during gait due to impaired functions of the iliopsoas and adductor muscles, and SKG can be encountered secondary to iliopsoas weakness.

Design and multiobjective optimization of a two‐point contact ladder‐climbing robot using a genetic algorithm

AbstractThis paper presents the design and optimization of a climbing robot. The design of a ladder‐climbing robot is done with fundamental mathematical considerations. The designed robot is robust enough to manage all environmental calamities, and at the same time, it is optimized for lightweight to reduce the actuator's cost and ease of transportation. An analytical evaluation is carried out for both static and dynamic conditions to determine strength and motion characteristics. The multiobjective optimization of the design parameters of a ladder‐climbing robot is done to obtain optimized values of design parameters. The formulation of an optimization problem that considers the minimization of weight and natural frequency is performed. Using an evolutionary genetic algorithm (GA) for the multicriteria optimization problem is solved, and a Pareto front solution is obtained. The optimal values of the parameters are decided based on the knee selection technique. As both objective functions are contradictory, the optimum results significantly improve the robot's performance. Controlling the proportional–integral–derivative (PID) parameters is crucial as the robot climbs with a two‐point contact gait pattern. The controlling parameters impart stability to the robot. PID parameters like proportional, integral and derivative gain are tunned using the GA. Finally, the developed prototype is tested on the ladders of the tower, and satisfactory climbing motion is achieved.

Design, motions, capabilities, and applications of quadruped robots: a comprehensive review

Robots are becoming integral to society and industries due to their enormous advantages. Among the various categories of mobile robots, including wheeled robot, tracked robot, and legged robots, the latter stands out as a better choice for most field applications due to their adaptability across various terrains. The purpose of this review is to study the locomotion capabilities of quadruped robots and judge their suitability for climbing applications as most unexplored applications of automation and robotics are required to climb. This review explores the locomotion capabilities of quadruped robots. It covers different aspects of quadruped robots like types of legs, leg design, gait patterns, and their mathematical formulations, and types of motions like omnidirectional motion and body sway motion. It also emphasizes its fault-tolerant gait, adaptability, and reliability. The paper also focuses on slope and stair climbing, outlining design requirements and applications. The study includes an examination of the applicability of various gaits under different conditions and the methods for increasing stability without compromising speed. Overall, the review serves as a valuable resource for future research in this field.

Approach to gait disorders and orthotic management in adult onset neuromuscular diseases.

In order to understand abnormal gait, this article will first review normal gait, discuss how neuromuscular diseases disturb gait patterns and review orthotic interventions. In normal gait, concentric contractions accelerate and eccentric contractions decelerate the limb. Neuromuscular gait disorders can be grouped into (1) proximal weakness, (2) distal weakness, (3) nonlength-dependent or generalized weakness, (4) asymmetric weakness, and (5) sensory disorders. Identification of gait disturbance type in neuromuscular diseases leads to the appropriate orthotic prescription since orthotic strategies are grouped into (1) proximal weakness, (2) distal weakness, and (3) sensory disturbances. Orthotics is not indicated in all types of gait disturbance. Weakness in proximal hip musculature can be managed with gait aids such as walkers. In contrast, distal muscle weakness can be managed with orthotics. Preservation of gait assists in maintenance of daily function and integration in society.

A porous elastomer with a cavity array for three-dimensional plantar force sensing

Human gait is an important indicator for diagnosis of various movement-related disorders. Three-dimensional (3D) plantar force monitoring enables real-time recording of contact forces in both the normal and tangential directions in each step, providing useful clues for identification of irregular gait patterns or gait-related health issues. Most flexible sensors for gait monitoring focus on contact forces in the normal direction, being limited to detect forces in the tangential direction. Herein we have developed a porous elastomer with a cavity array (PECA) to measure 3D contact forces for gait analysis. The sensor consists of two fabric electrodes and a dielectric layer of porous Ecoflex elastomer containing a 5 × 5 cavity array, forming four parallel-plate capacitor units in a soft construction. Therefore, a 3D force in any arbitrary direction generates four unique capacitive electrical signals. Importantly, the PECA includes a well-designed combination of micrometer-scale pores and millimeter-sized cavities, dramatically improving the detection sensitivity and linear elasticity range. We have demonstrated high sensitivities of 0.41 N−1 and 0.23 N−1 in the normal and tangential directions, respectively. The flexible PECA-based sensors have been tested to sensitively distinguish various standing postures and different stride lengths for gait analysis, promising reliable translation into areas such as healthcare monitoring, sports training, and rehabilitation engineering.

A usability study on the inGAIT-VSO: effects of a variable-stiffness ankle-foot orthosis on the walking performance of children with cerebral palsy

BackgroundAnkle-foot orthoses (AFOs) are commonly used by children with cerebral palsy (CP), but traditional solutions are unable to address the heterogeneity and evolving needs amongst children with CP. One key limitation lies in the inability of current passive devices to customize the torque–angle relationship, which is essential to adapt the support to the specific individual needs. Powered alternatives can provide customized behavior, but often face challenges with reliability, weight, and cost. Overall, clinicians find certain barriers that hinder their prescription. In recent work, the Variable Stiffness Orthosis (VSO) was developed, enabling stiffness customization without the need for motors or sophisticated control.MethodsThis work evaluates a pediatric version of the VSO (inGAIT-VSO) by investigating its impact on the walking performance of children with CP and its potential to be used as a tool for assessing the effect of variable stiffness on pathological gait. Data was collected for three typical developing (TD) children and six pediatric participants with CP over two sessions involving walking/balance tasks and questionnaires.ResultsThe sensors of the inGAIT-VSO provided useful information to assess the impact of the device. Increasing the stiffness of the inGAIT-VSO significantly reduced participants’ dorsiflexion and plantarflexion. Despite reduced range of motion, the peak restoring torque increased with stiffness. Overall the participants’ gait pattern was altered by reducing crouch gait, preventing drop-foot and supporting body weight. Participants with CP exhibited significantly lower (p < 0.05) physiological cost when walking with the inGAIT-VSO compared to normal condition (own AFO or shoes only). Generally, the device did not impair walking and balance of the participants compared to normal conditions. According to the questionnaire results, the inGAIT-VSO was easy to use and participants reported positive experiences.ConclusionThe inGAIT-VSO stiffnesses significantly affected participants’ plantarflexion and dorsiflexion and yielded objective data regarding walking performance in pathological gait (e.g. ankle angle, exerted torque and restored assistive energy). These effects were captured by the sensors integrated in the device without using external equipment. The inGAIT-VSO shows promise for customizing AFO stiffness and aiding clinicians in selecting a personalized stiffness based on objective metrics.

Wearable gait recognition system based on time domain features of Bluetooth inertial sensor network

Abstract As an important part of medical monitoring, gait recognition has been used in lower limb rehabilitation diagnosis and gait assessment. To meet practical requirements, we designed a wearable gait recognition system based on time domain features of Bluetooth inertial sensor network, which can be used for real-time acquisition and evaluation of gait signals. In this research, we used 6 sensor units to integrate 10 kinds of gait information, and designed a gait monitoring system covering the main joints of lower limbs based on Bluetooth network. The gait monitoring system can collect the inertial sensing data of lower limbs at a maximum rate of 90 Hz through the 6 sensor units, and has good electrical performance. In daily use, each unit can work for more than 3 hours and maintain a received signal strength indicator (RSSI) of more than -90 dBm at a spatial distance of 10 meters. On the gait dataset collected in the experiment, we used sliding window to segment the time domain signals and extract 10 kinds of time domain features for classification algorithm. We achieved 97.1% average identification accuracy of 7 different gait patterns by support vector machine (SVM).

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.