Gait Modification Research Articles

Personalization improves the biomechanical efficacy of foot progression angle modifications in individuals with medial knee osteoarthritis

Modifying the foot progression angle during walking can reduce the knee adduction moment, a surrogate measure of medial knee loading. However, not all individuals reduce their knee adduction moment with the same modification. This study evaluates whether a personalized approach to prescribing foot progression angle modifications increases the proportion of individuals with medial knee osteoarthritis who reduce their knee adduction moment, compared to a non-personalized approach. Individuals with medial knee osteoarthritis (N=107) walked with biofeedback instructing them to toe-in and toe-out by 5° and 10° relative to their self-selected angle. We selected individuals’ personalized foot progression angle as the modification that maximally reduced their larger knee adduction moment peak. Additionally, we used lasso regression to identify which secondary kinematic changes made a 10° toe-in gait modification more effective at reducing the first knee adduction moment peak. Seventy percent of individuals reduced their larger knee adduction moment peak by at least 5% with a personalized foot progression angle modification, which was more than (p≤0.002) the 23–57% of individuals who reduced it with a uniformly assigned 5° or 10° toe-in or toe-out modification. When toeing-in, greater reductions in the first knee adduction moment peak were related to an increased frontal-plane tibia angle (knee more medial than ankle), a more valgus knee abduction angle, reduced contralateral pelvic drop, and a more medialized center of pressure in the foot reference frame. In summary, personalization increases the proportion of individuals with medial knee osteoarthritis who may benefit from a foot progression angle modification.

Gait Pattern Identification and Phase Estimation in Continuous Multilocomotion Mode Based on Inertial Measurement Units

In the field of lower limb exoskeletons, it is essential to accurately estimate the gait phase of humans. Many methods have been proposed to estimate the gait phase, but only a few studies have considered the multi-locomotion mode. This paper proposes a novel inertial measurement unit(IMU)-based method to estimate the gait phase of a pilot in continuous multi-locomotion mode. The method includes gait pattern recognition based on long short-term memory (LSTM), continuous phase estimation based on a dual adaptive frequency oscillator(DAFO), threshold-based toe-off event detection and a rule-based gait phase synchronization module. First, we used the LSTM-based network to identify four gait patterns including standing, level ground walking, upstairs and downstairs. Next, the DAFO was used to obtain the continuous gait phase of the pilot. Then, we detected the gait events in different gait modes. Finally, the continuous gait phase was synchronized according to the gait events. The experimental result shows that the gait pattern classification accuracy using 5 IMUs is 98.58% and the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {F}_{\!{1}}$ </tex-math></inline-formula> score reaches 0.9875. The proposed DAFO model can maintain good stability when multiple gait modes are frequently switched, significantly improving the problem of slow convergence and the poor robustness of single adaptive frequency oscillator(SAFO) models. Toe-off gait events of 492 steps are all detected and the average error at the detected gait events in different gait modes is 15.34± 40.58 ms.

Effect of toe - out gait modification on pain and quality of life in patients with knee osteoarthritis: a randomised controlled pilot trial

Effect of toe - out gait modification on pain and quality of life in patients with knee osteoarthritis: a randomised controlled pilot trial

Finding Emergent Gait Patterns May Reduce Progression of Knee Osteoarthritis in a Clinically Relevant Time Frame.

A high contact force between the medial femoral condyle and the tibial plateau is the primary cause of medial compartment knee osteoarthritis (OA). A high medial contact force (MCF) during gait has been shown to be correlated to both the knee adduction moment (KAM) and knee flexion/extension moment (KFM). In this study, we used OpenSim Moco to find gait kinematics that reduced the peaks of the KAM, without increasing the peaks of the KFM, which could potentially reduce the MCF and, hence, the progression of knee OA. We used gait data from four knee OA participants. Our simulations decreased both peaks of the KAM without increasing either peak of the KFM. We found that increasing the step width was the primary mechanism, followed by simulations of all participants to reduce the frontal plane lever arm of the ground reaction force vector about the knee, in turn reducing the KAM. Importantly, each participant simulation followed different patterns of kinematic changes to achieve this reduction, which highlighted the need for participant-specific gait modifications. Moreover, we were able to simulate emerging gait patterns within 15 min, enhancing the relevance and potential for the application of developed methods in clinical settings.

The Design and the Development of a Biped Robot Cooperation System

The aim of this paper is to design a fuzzy motion control algorithm for a developed monocular vision system based on a cooperative transportation system of two humanoid robots. The control strategies of the cooperation transportation system contain three stages, including object searching, walking toward the transported object, and cooperatively moving the transported object. To have different moving speeds, the gait step size was pre-planned as two different modes, i.e., one of the gaits is selected to let the HR have large variations of motion and another gait is to make the HR with small variations. The fuzzy motion control algorithm is utilized to select the appropriate mode of gait. Both humanoid robots can actively search and move to the front of the target object, then cooperatively lift the target and carry it to the platform. The task of synchronous movement is controlled with fuzzy techniques through the control terminal. From the experimental results, it can be seen that both robots can distinguish the orientation of the target, move to the appropriate position, and then successfully raise the target together.

Deep learning-based discriminant model for wearable sensing gait pattern

&lt;p&gt;In order to effectively improve the accuracy of identifying the gait pattern of wearable sensing data, this paper proposes a new model for deep learning gait mode discrimination that integrates convolutional neural network and long short-term memory neural network, which makes full use of the convolutional neural network to obtain the most local spatial characteristics of data and the long short-term memory neural network to obtain the inherent characteristics of the data, and effectively excavates the hidden high-dimensional, nonlinear, time-space gait characteristics of random wearable sensing timing gait data that are closely related to gait pattern changes, to improve the classification performance of gait mode. The effectiveness of the proposed model in this paper is evaluated using the HAR dataset from University of California UCI database. The experiment results showed that the proposed model in this paper can effectively obtain the time-space gait characteristics embedded in the wearable sensor gait data, and the classification accuracy can reach 91.45%, the precision rate 91.54%, and the recall rate 91.53%, and the classification performance is significantly better than that of the traditional machine learning model, which provides a new solution for accurately identifying the gait mode of wearable sensor data.&lt;/p&gt;

Trajectory tracking of multi-legged robot based on model predictive and sliding mode control

To address the problem of achieving accurate tracking control of multi-legged robots, which is difficult due to their strong coupling structure, this paper decomposes the trajectory tracking of the whole-body into body-level and limb-level sub-control systems. First, a tracking controller based on the model predictive control strategy is designed for the body-level subsystem, and the general projection neural network method is used to quickly solve for the optimal control signal that satisfies stride constraints. Then, a robust adaptive terminal sliding mode controller is designed for the limb-level subsystem to resist the influence of model uncertainties and external disturbances on tracking performance. The designed gait mode generator and stride solver modules serve as two bridges connecting the body- and limb-level subsystems, thus coalescing the entire control system into a closed loop, which greatly improves the tracking accuracy of the whole system. Finally, two cases, the directed curved-trajectory tracking with tripod gait and the undirected polyline-trajectory tracking with quadruped gait, are given to illustrate the omnidirectional mobility of the multi-legged robot, and to demonstrate the effectiveness and practicability of the proposed control strategy.

Relation between Step-To-Step Transition Strategies and Walking Pattern in Older Adults

In older adults, two different modes of step-to-step transition have been observed: an anticipated mode when the redirection of the centre of mass of the body (COM) begins before double stance and another when the transition begins during double stance. However, the impact of transition mode on gait kinetics and kinematics has not been investigated. Age and step-to-step-transition-related differences in intersegmental coordination and in the COM trajectory during walking were identified. Fifteen young (24.1 ± 0.7 y.o.) and thirty-six older adults (74.5 ± 5.0 y.o.) walked on a treadmill at 1.11 m s−1 and 1.67 m s−1. Lower-limb motion and ground reaction force were recorded. The COM dynamics were evaluated by measuring the pendulum-like exchange of the COM energies. While all young adults and 21 of the older adults used an anticipated transition, 15 older adults presented a non-anticipated transition. Previously documented changes of intersegmental coordination with age were accentuated in older adults with non-anticipated transition (p &lt; 0.001). Moreover, older adults with non-anticipated transition had a smaller pendulum-like energy exchange than older adults with anticipated transition (p = 0.03). The timing of COM redirection is linked to kinematic and mechanic modification of gait and could potentially be used as a quantitative assessment of age-related decline in gait.

Gait modification with subject-specific foot progression angle in people with moderate knee osteoarthritis: Investigation of knee adduction moment and muscle activity

BackgroundSubject-specific foot progression angle (SSFPA) as a personalized gait modification is a novel approach to specifically reducing knee adduction. ObjectiveThis study aimed to investigate the effect of gait modification with SSFPA on the knee adduction moment and muscle activity in people with moderate knee osteoarthritis (KOA). MethodsIn this clinical trial, nineteen volunteers with moderate KOA were instructed to walk in four different foot progression angle conditions (5° toe-out, 10° toe-out, 5° toe-in, and 10° toe-in) to determine SSFPA that caused the greatest reduction in the greater peak of the knee adduction moment (PKAM). Immediately and after 30 minutes of gait modification with SSFPA, peak root means square (PRMS) and medial and lateral co-contraction index (CCI) were evaluated in the knee muscles. ResultWalking with 10° toe-in showed the most reduction in the greater PKAM (17.52 ± 15.39%) compared to 5° toe-in (7.1 ± 19.14%), 10° toe-out (1.26 ± 23.13%), and 5° toe-out (7.64 ± 16.71%). As the immediate effect, walking with SSFPA caused a 20.71 ± 12.07% reduction in the greater PKAM than the basic FPA (p < 0.001). After 30 minutes of gait retraining, the greater PKAM decreased by 10.36 ± 26.24%, but this reduction was not significant (p = 0.17). In addition, PRMS of lateral gastrocnemius increased (p = 0.04), and lateral CCI increased 10.72% during late stance (p = 0.04). ConclusionOur findings suggest the immediate effect of gait modification with SSFPA on decreasing the knee adduction moment. After gait retraining with SSFPA, the increase of lateral muscle co-contraction may enhance lateral knee muscle co-activity to unload the medial knee compartment.Clinical Trial Register Number: IRCT20101017004952N8.

Application of control strategies and machine learning techniques in prosthetic knee: a systematic review

This systematic review focuses on control strategies and machine learning techniques used in prosthetic knees for restoring mobility of individuals with trans-femoral amputations. Review and classification of control strategies that determine how these prosthetic knees interact with the user and gait strategy inspired algorithms for phase identification, locomotion mode, and motion intention recognition were studied. Relevant studies were identified using electronic databases such as PubMed, EMBASE, SCOPUS, and the Cochrane Controlled Trials Register (Rehabilitation and Related Therapies) up to April 2021. Abstracts were screened and inclusion and exclusion criteria were applied. Out of 278 potentially relevant studies, 65 articles were included. The specific variables on control approach, control modes, gait control, hardware level, machine learning algorithm, and measured signals mechanism were extracted and added to summary table. The results indicate that advanced methods for adapting position or torque depiction and automatic detection of terrains or gait modes are more commonly utilized, but they are largely limited to laboratory environments. It is concluded that a correct combination of control strategies and machine learning techniques will enable the improvement of prosthetic performance and enhance the standard of amputee’s lives.

Characterization and Categorization of Various Human Lower Limb Movements Based on Kinematic Synergies.

A proper movement categorization reduces the complexity of understanding or reproducing human movements in fields such as physiology, rehabilitation, and robotics, through partitioning a wide variety of human movements into representative sub-motion groups. However, how to establish a categorization (especially a quantitative categorization) for various human lower limb movements is rarely investigated in literature and remains challenging due to the diversity and complexity of the lower limb movements (diverse gait modes and interaction styles with the environment). Here we present a quantitative categorization for the various lower limb movements. To this end, a similarity measure between movements was first built based on limb kinematic synergies that provide a unified and physiologically meaningful framework for evaluating the similarities among different types of movements. Then, a categorization was established via hierarchical cluster analysis for thirty-four lower limb movements, including walking, running, hopping, sitting-down-standing-up, and turning in different environmental conditions. According to the movement similarities, the various movements could be divided into three distinct clusters (cluster 1: walking, running, and sitting-down-standing-up; cluster 2: hopping; cluster 3: turning). In each cluster, cluster-specific movement synergies were required. Besides the uniqueness of each cluster, similarities were also found among part of the synergies employed by these different clusters, perhaps related to common behavioral goals in these clusters. The mix of synergies shared across the clusters and synergies for specific clusters thus suggests the coexistence of the conservation and augmentation of the kinematic synergies underlying the construction of the diverse and complex motor behaviors. Overall, the categorization presented here yields a quantitative and hierarchical representation of the various lower limb movements, which can serve as a basis for the understanding of the formation mechanisms of human locomotion and motor function assessment and reproduction in related fields.

Symmetry-Driven hyper feature GCN for skeleton-based gait recognition

Gait recognition, as an attractive task in biometrics, remains challenging due to significant intra-class changes of clothing and pose variations across different cameras. Recent approaches mainly focus on silhouette-based gait mode, which is easy to model in Convolutional Neural Networks (CNNs). Compared with silhouettes, the dynamics of skeletons essentially convey more robust information, which is invariant to view and clothing changes. Conventional approaches for modeling skeletons usually rely on hand-crafted features or traversal rules, thus resulting in limited expressive power and difficulties of generalization. In this work, we address the skeleton-based gait recognition task with a novel Symmetry-Driven Hyper Feature Graph Convolutional Network (SDHF-GCN), which goes beyond the limitations of previous approaches by automatically learning multiple dynamic patterns and hierarchical semantic features in a unified Graph Convolutional Network (GCN). This model involves three dynamic patterns: natural connection, temporal correlation and symmetric interaction, which enriches the description of dynamic patterns by exploiting symmetry perceptual principles. Furthermore, a hyper feature network is proposed to aggregate the hierarchical semantic features, including dynamic features at the high level, structured features at the intermediate level, and static features at the low level, which complement each other to enhance the discriminative ability. By integrating different patterns in the hierarchical structure, the model is able to generate versatile and discriminative representations, thus improving the recognition rate. On the CASIA-B and OUMVLP-Pose datasets, the proposed SDHF-GCN renders substantial improvements over mainstream methods, especially in the coat-wearing scenario, with superior robustness to covariate factors.

Remotely Delivered, Individualized, and Self-Directed Gait Modification for Knee Osteoarthritis: A Pilot Trial

Remotely Delivered, Individualized, and Self-Directed Gait Modification for Knee Osteoarthritis: A Pilot Trial

Development and Evaluation of a Portable Functional Electrical Stimulator with Built-in Feedback Mechanism for Gait Modification of Osteoarthritis of the Knee

Development and Evaluation of a Portable Functional Electrical Stimulator with Built-in Feedback Mechanism for Gait Modification of Osteoarthritis of the Knee

The Effect of Gait Modification on the Response of Medial Meniscus Extrusion During Gait in Patients with Knee Osteoarthritis

The Effect of Gait Modification on the Response of Medial Meniscus Extrusion During Gait in Patients with Knee Osteoarthritis

Measurement of Force Myography in Level Gait and Stair Gait

By measuring the activity of the leg muscles during walking, clinical information such as gait abnormality, muscle spasticity, and muscle weakness can be obtained. The surface electromyography (sEMG) method is widely used to measure changes in muscle activity. However, sEMG sensors must be attached to specific muscles and complex signal processing is required. In this paper, a band-type device using eight pneumatic-based sensors was fabricated. Eight sensors were equally spaced on an elastic fabric band. Two straps were made to measure changes in the muscles around the thigh and calf of one leg during walking. An insole-type pressure sensor system was used to accurately distinguish swing phase and stance phase. Measurements of muscle changes were tested when walking on a treadmill, climbing an eight-story stairway, and descending the stairs. The measured muscle change results can be used to classify gait modes and gait patterns, or predict gait intentions.

Development of quadruped robot for inspection of underground pipelines in nuclear power plants

Structural integrity of seawater pipelines in nuclear power plants is a very important issue. In accordance with the operating technical guidelines, the human operators directly enter the pipe and inspect it at every maintenance test. However, in this regard, safety issues such as narrow space and harmful gas are emerging every year. In response to these needs, a quadruped robot that can inspect underground pipes and assist workers has been developed. The robot has an articulated robotic arm that can receive an impact sound of hammering a pipe wall to test pipe integrity. The state of the pipe was examined using a Convolutional Neural Network algorithm. On the other hand, moving in a plumbing environment requires stable walking ability. To determine the gait sequence, a hierarchical gait controller is proposed. The hybrid controller, which consists of joint impedance and torque control, calculated from Model Predictive Control, can switch the gait modes comparing the reference and the current foot contact condition at each control cycle.

The effect of two retraining programs, barefoot running vs increasing cadence, on kinematic parameters: A randomized controlled trial.

The aim of this study was to compare the effects of two 10-week non-laboratory-based running retraining programs on foot kinematics and spatiotemporal parameters in recreational runners. One hundred and three recreational runners (30±7.2years old, 39% females) were randomly assigned to either: a barefoot retraining group (BAR) with 3sessions/week over 10weeks, a cadence retraining group (CAD) who increased cadence by 10% again with 3sessions/week over 10weeks and a control group (CON) who did not perform any retraining. The footstrike pattern, footstrike angle (FSA), and spatial-temporal variables at comfortable and high speeds were measured using 2D/3D photogrammetry and a floor-based photocell system. A 3×2 ANOVA was used to compare between the groups and 2 time points. The FSA significantly reduced at the comfortable speed by 5.81° for BAR (p<0.001; Cohen's d=0.749) and 4.81° for CAD (p=0.002; Cohen's d=0.638), and at high speed by 6.54° for BAR (p<0.001; Cohen's d=0.753) and by 4.71° for CAD (p=0.001; Cohen's d=0.623). The cadence significantly increased by 2% in the CAD group (p= 0.015; Cohen's d=0.344) at comfortable speed and the BAR group showed a 1.7% increase at high speed. BAR and CAD retraining programs showed a moderate effect for reducing FSA and rearfoot prevalence, and a small effect for increasing cadence. Both offer low-cost and feasible tools for gait modification within recreational runners in clinical scenarios.

Deep Reinforcement Learning with Gait Mode Specification for Quadrupedal Trot-Gallop Energetic Analysis.

Quadruped system is an animal-like model which has long been analyzed in terms of energy efficiency during its various gait locomotion. The generation of certain gait modes on these systems has been achieved by classical controllers which demand highly specific domain-knowledge and empirical parameter tuning. In this paper, we propose to use deep reinforcement learning (DRL) as an alternative approach to generate certain gait modes on quadrupeds, allowing potentially the same energetic analysis without the difficulty of designing an ad hoc controller. We show that by specifying a gait mode in the process of learning, it allows faster convergence of the learning process while at the same time imposing a certain gait type on the systems as opposed to the case without any gait specification. We demonstrate the advantages of using DRL as it can exploit automatically the physical condition of the robots such as the passive spring effect between the joints during the learning process, similar to the adaptation skills of an animal. The proposed system would provide a framework for quadrupedal trot-gallop energetic analysis for different body structures, body mass distributions and joint characteristics using DRL.

Walking Pole Gait to Reduce Joint Loading post Total Knee Athroplasty: Musculoskeletal modeling Approach.

Excessive knee contact loading is precursor to osteoarthritis and related knee ailment leading to knee athroplasty. Reducing contact loading through gait modifications using assisted pole walking offers noninvasive process of medial load offloading at knee joint. In this paper, we evaluate the efficacy of different configuration of pole walking for reducing contact force at the knee joint through musculoskeletal (MSK) modeling. We have developed a musculoskeletal model for a subject with knee athroplasty utilizing in-vivo implant data and computed tibio-femoral contact force for different pole walking conditions to evaluate the best possible configuration for guiding rehabilitation, correlated with different gait phases. Effect of gait speed variation on knee contact force, hip joint dynamics and muscle forces are simulated using the developed MSK model. Results indicate some interesting trend of load reduction, dependent on loading phases pertaining to different pole configuration. Insights gained from the simulation can aid in designing personalized rehabilitation therapy for subjects suffering from Osteoarthritis.