Gait Phase Research Articles

A two-dimensional video based quantification method and clinical application research of motion disorders

The increasing prevalence of the aging population, and inadequate and uneven distribution of medical resources, have led to a growing demand for telemedicine services. Gait disturbance is a primary symptom of neurological disorders such as Parkinson's disease (PD). This study proposed a novel approach for the quantitative assessment and analysis of gait disturbance from two-dimensional (2D) videos captured using smartphones. The approach used a convolutional pose machine to extract human body joints and a gait phase segmentation algorithm based on node motion characteristics to identify the gait phase. Moreover, it extracted features of the upper and lower limbs. A height ratio-based spatial feature extraction method was proposed that effectively captures spatial information. The proposed method underwent validation via error analysis, correction compensation, and accuracy verification using the motion capture system. Specifically, the proposed method achieved an extracted step length error of less than 3 cm. The proposed method underwent clinical validation, recruiting 64 patients with Parkinson's disease and 46 healthy controls of the same age group. Various gait indicators were statistically analyzed using three classic classification methods, with the random forest method achieving a classification accuracy of 91%. This method provides an objective, convenient, and intelligent solution for telemedicine focused on movement disorders in neurological diseases.

Effects of Rearfoot Eversion on Foot Plantar Pressure and Spatiotemporal Gait Parameters in Adolescent Athletes

Background: Foot malalignment can augment the risk of lower-extremity injuries and lead to musculoskeletal disorders. This study aimed to clarify the contribution of rearfoot alignment to plantar pressure distribution and spatiotemporal parameters during gait in healthy adolescent athletes. Methods: This retrospective study included 39 adolescent athletes who were divided into the rearfoot eversion and control groups according to a leg heel angle of 7°. A total of 78 legs were analyzed (45 and 33 legs in the rearfoot eversion [women, 53.3%] and control groups [women, 48.5%], respectively). Gait was assessed using an in-shoe plantar pressure measuring system and a wearable inertial sensor. Results: The foot plantar pressure distribution in the hallux was higher in the rearfoot eversion group than that in the control group (p = 0.034). Spatiotemporal parameters showed that the foot pitch angle at heel strike was significantly larger in the rearfoot eversion group than that in the control group (24.5° vs. 21.7°; p = 0.015). Total sagittal range of motion of the ankle during the stance phase of gait was significantly larger in the rearfoot eversion group than that in the control group (102.5 ± 7.1° vs. 95.6 ± 15.8°; p = 0.020). Logistic regression analysis revealed that plantar pressure at the hallux and medial heel and foot pitch angle at heel strike were significantly associated with rearfoot eversion. Conclusions: Our findings suggest that rearfoot eversion affects the gait patterns of adolescent athletes. Notably, leg heel angle assessment, which is a simple and quick procedure, should be considered as an alternative screening tool for estimating plantar pressure and spatiotemporal gait parameters to prevent sports-related and overuse injuries in adolescent athletes.

Variability of the lower limb symmetry index associated with the gait parameters in the overweight adult population with flatfoot: a case-control study.

Background: Adult acquired flatfoot is characterized by a medial arch collapse during monopodal support in the stance phase, developing eversion of the calcaneus and abduction of the forefoot linked to the hindfoot. The purpose of our research was to analyze the dynamic symmetry index in the lower limbs comparing patients with flatfoot and normal foot. Methods: A case-control study was carried out with a sample of 62 participants divided into two groups consisting of 31 participants were overweight with bilateral flatfoot and 31 participants with healthy feet. A portable plantar pressure platform with piezoresistive sensors was used to measure the load symmetry index in the lower limbs in the foot areas and gait phases. Results: Gait pattern analysis showed statistically significant differences in the symmetry index for lateral load (p = 0.004), the initial contact phase (p = 0.025) and the forefoot phase (p < 0.001). Conclusion: The adults were overweight with bilateral flatfoot evidenced alterations in the symmetry index in the lateral load and in the initial contact and flatfoot contact phases, showing greater instability in overweight adult flatfoot compared to the people with normal feet.

Concomitant Triceps Surae Lengthening in Total Ankle Arthroplasty Affects the Mechanical Work at the Ankle Joint.

Previous studies have examined the effect of concomitant triceps surae lengthening on ankle dorsiflexion motion at the time of total ankle arthroplasty (TAA). As plantarflexor muscle-tendon structures are important for producing positive ankle work during the propulsive phase of gait, caution should be exercised when lengthening triceps surae, as it may decrease plantarflexion strength. In order to develop an understanding of the work of the anatomical structures crossing the ankle during propulsion, joint work must be measured. The aim of this explorative study was to assess the effect of concomitant triceps surae lengthening with TAA on the resultant ankle joint work. Thirty-three patients were recruited to the study and divided into 3 groups of 11. The first group underwent both triceps surae lengthening (Strayer and TendoAchilles) and TAA (Achilles group), the second group underwent only TAA (Non-Achilles group), and the third group underwent only TAA, but had a greater radiographic prosthesis range of motion (Control group) compared to the first 2 groups. The 3 groups were matched in terms of demographic variables and walking speed. All patients underwent a 3D gait analysis 1 year after surgery to measure intersegmental joint work using a 4-segmented kinetic foot model. An analysis of variance (ANOVA) or Kruskal-Wallis test was used to compare the 3 groups. The ANOVA showed significant differences between the 3 groups. Post hoc analyses suggested that (1) the Achilles group had less positive work at the ankle joint than the Non-Achilles and Control groups; (2) the Achilles group produced less positive work performed by all foot and ankle joints than the Control group; and (3) the Achilles and Non-Achilles groups absorbed less energy across all foot and ankle joints during the stance phase than the Control group. Concomitant triceps surae lengthening in TAA may reduce the positive work at the ankle joint. Level III, retrospective comparative study.

Real-Time Gait Event Detection with Adaptive Frequency Oscillators From a Single Head-Mounted IMU

Accurate real-time gait event detection is the basis for the development of new gait rehabilitation techniques, especially when utilizing robotics or virtual reality (VR). The recent emergence of affordable wearable technologies, especially inertial measurement units (IMUs), has brought forth various new methods and algorithms for gait analysis. In this paper, we highlight some advantages of using adaptive frequency oscillators (AFOs) over traditional gait event detection algorithms, implemented a real-time AFO-based algorithm that estimates the gait phase from a single head-mounted IMU, and validated our method on a group of healthy subjects. Gait event detection was accurate at two different walking speeds. The method was reliable for symmetric, but not asymmetric gait patterns. Our method could prove especially useful in VR applications since a head-mounted IMU is already an integral part of commercial VR products.

Continuous online prediction of lower limb joints angles based on sEMG signals by deep learning approach

Continuous online prediction of human joints angles is a key point to improve the performance of man-machine cooperative control. In this study, a framework of online prediction method of joints angles by long short-term memory (LSTM) neural network only based on surface electromyography (sEMG) signals was proposed. The sEMG signals from eight muscles of five subjects’ right leg and three joints angles and plantar pressure signals of subjects were collected simultaneously. Different inputs (only sEMG (unimodal), sEMG combined with plantar pressure (multimodal)) after online feature extraction and standardization were used for training the angle online prediction model by LSTM. The results indicate that there is no significant difference between the two kinds of inputs for LSTM model and the proposed method can make up for the shortage of using a single type of sensor. The range of mean values of root square mean error, mean absolute error and Pearson correlation coefficient of the three joints angles achieved by the proposed model only with the input of sEMG under four kinds of predicted time (50, 100, 150, and 200 ms) are [1.63°,3.20°],[1.27°, 2.36°] and [0.9747, 0.9935]. Three popular machine learning algorithms with different inputs were compared to the proposed model only based on sEMG. Experiment results demonstrate that the proposed method has the best prediction performance and there are highly significant differences between it and other methods. The difference of prediction results under different gait phases by the proposed method was also analyzed. The results indicate that the prediction effect of support phases is generally better than that of swing phases. Above experimental results show that the proposed method can realize accurate online joint angle prediction and has better performance to promote man-machine cooperation.

A novel Dynamic Body Weight Support overground co-walker enabling variable unloading ratio and Motion Tracking.

Dynamic Body Weight Support (BWS) systems have gained attention in recent years for their potential in gait training. However, maintaining a natural gait and vertical unloading have been less explored. In our previous work, we developed a body Motion Tracking (MT) walker that can move with patients. In this study, we introduce a novel Motion Tracking Variable Body Weight Support (MTVBWS) system for overground walkers. This system utilizes Center of Mass (COM) tracking and gait phase detection to not only dynamically support the user's body weight in the vertical direction but also to facilitate movement in all directions. The system achieves this horizontal omnidirectional movement by employing active Mecanum wheels, guided by COM recognition. The validation experiments were implemented with the MT mode, passive mode, and BWS mode in "static," "fixed unloading ratio (FUR)," and "variable unloading ratio (VUR)" settings with unloading force of 20 and 30%. The result shows that, compared to other modes, the proposed system in the MTVBWS mode can reduce the dragging effect in the horizontal plane caused by the walker. Moreover, the unloading force can be adjusted automatically to minimize the fluctuations in the force experienced by each lower limb during the rehabilitation walking training process. In comparison to natural walk, this mode presents smaller force fluctuations for each lower limb.

Abnormal gait partitioning and real-time recognition of gait phases in children with cerebral palsy

ObjectiveSince the gait of children with cerebral palsy (CP) lacks distinct foot event characteristics, it’s difficult to accurately distinguish the key gait events, especially in the stance phase. To solve the problem of gait phase detection for children with CP, a unique gait classification system is designed to detect the walking maneuvers during a gait cycle. This system can detect all gait phases of CP patients, and send the kinematic information to a rule-based algorithm through a series of sensors installed in the legs to identify the salient features of the signal. MethodsIn the light of the quasi-stiffness gait phase transformation as the classification guide, two sets of motion signal feature sets are extracted from the leg feature signals of 10 CP children, and two gait stage recognition algorithms are proposed to divide the six gait sub-phases. ResultsThe proposed algorithms of peak-zero-based angular velocity and thresholding-based angular realize the effective recognition of gait phases, especially for the stance phase. ConclusionThe proposed methods have the potential to obtain correct decoding of motion information and recognize gait phases. Significance: This study provides a new idea to solve the problem of difficult identification of gait phase for children with CP, which can be applied to a behavior-driven lower limb exoskeleton robot system for assisting walking and gait correction.

The Evidence for Pain in Hip Osteoarthritis and Symptom Production during the Stance Phase of Gait

The Evidence for Pain in Hip Osteoarthritis and Symptom Production during the Stance Phase of Gait

Surface electromyogram, kinematic, and kinetic dataset of lower limb walking for movement intent recognition

Surface electromyogram (sEMG) offers a rich set of motor information for decoding limb motion intention that serves as a control input to Intelligent human-machine synergy systems (IHMSS). Despite growing interest in IHMSS, the current publicly available datasets are limited and can hardly meet the growing demands of researchers. This study presents a novel lower limb motion dataset (designated as SIAT-LLMD), comprising sEMG, kinematic, and kinetic data with corresponding labels acquired from 40 healthy humans during 16 movements. The kinematic and kinetic data were collected using a motion capture system and six-dimensional force platforms and processed using OpenSim software. The sEMG data were recorded using nine wireless sensors placed on the subjects’ thigh and calf muscles on the left limb. Besides, SIAT-LLMD provides labels to classify the different movements and different gait phases. Analysis of the dataset verified the synchronization and reproducibility, and codes for effective data processing are provided. The proposed dataset can serve as a new resource for exploring novel algorithms and models for characterizing lower limb movements.

Biomechanical considerations for an easily-restricted robot-assisted kinematic alignment: a surgical technique note

BackgroundIn total knee arthroplasty, the normal kinematics of the knee may not be restored solely based on preoperative gait, fluoroscopic-based, and dynamic radiostereometric analyses.Surgical technique case presentationThis note introduced a 69-year-old male patient who sustained post-traumatic osteoarthritis of his right knee. He underwent robot-assisted total knee arthroplasty based on anatomical reproduction of knee stability during the swing phase of gait. The kinematic alignment was simply achieved within an easy-to-identified range after preoperative radiographic assessment, intraoperative landmarking and pre-validated osteotomy, and intraoperative range of motion testing.ConclusionsThis novel technique allows personalized and imageless total knee arthroplasty. It provides a preliminary path in reproducing the anatomy alignment, natural collateral ligament laxity, and accurate component placement within safe-to-identified alignment boundaries.

Gait phase recognition of multi-mode locomotion based on multi-layer perceptron for the plantar pressure measurement system

Gait phase recognition of multi-mode locomotion based on multi-layer perceptron for the plantar pressure measurement system

Adaptive Symmetry Reference Trajectory Generation in Shared Autonomy for Active Knee Orthosis

Gait symmetry training plays an essential role in the rehabilitation of hemiplegic patients. Robotics-based gait training has been widely accepted by patients and clinicians. Reference trajectory generation for the affected side using the motion data of the unaffected side is an important way to achieve this. However, online generation of gait reference trajectory requires the algorithm to provide correct gait phase delay and could reduce the impact of measurement noise from sensors and input uncertainty from users. Based on an active knee orthosis (AKO) prototype, this work presents an adaptive symmetric gait trajectory generation framework for the gait rehabilitation of hemiplegic patients. Using the adaptive nonlinear frequency oscillators (ANFO) and movement primitives, we implement online gait pattern encoding and adaptive phase delay according to the real-time user input. A shared autonomy (SA) module with online input validation and arbitration has been designed to prevent undesired movements from being transmitted to the actuator on the affected side. The experimental results demonstrate the feasibility of the framework. Overall, this work suggests that the proposed method has the potential to perform gait symmetry rehabilitation in an unstructured environment and provide a kinematic reference for torque-assistance AKO.

Design Methodology and Experimental Study of a Lower Extremity Soft Exosuit

Flexibility and light weight have become the development trends in the field of exoskeleton research. With high movement flexibility, low movable inertia and excellent wearable comfort, such a type of system is gradually becoming an exclusive candidate for applications such as military defense, rehabilitation training and industrial production. In this paper, aiming at assisting the walking of human lower limbs, a soft exosuit is investigated and developed based on the considerations of fabric structure, sensing system, cable-driven module, and control strategy, etc. Evaluation experiments are also conducted to verify its effectiveness. A fabric optimization of the flexible suit is performed to realize the tight bond between human and machine. Through the configuration of sensor nodes, the motion intention perception system is constructed for the lower limb exosuit. A flexible actuation unit with a Bowden cable is designed to improve the efficiency of force transmission. In addition, a position control strategy based on division of the gait phase is applied to achieve active assistance during plantar flexion of the ankle joint. Finally, to verify the assistive effectiveness of the proposed lower extremity exosuit, experiments including a physiological metabolic test and a muscle activation test are conducted. The experiment results show that the exosuit proposed in this paper can effectively reduce the metabolic consumption and muscle output of the human body. The design and methodology proposed in this paper can be extended to similar application scenarios.

Simulation of Lower Limb Muscle Moment and Total Muscle Fiber Force Using OpenSim for Knee Osteoarthritis

Computational modelling is a tool that provide accurate readings and simulation by using a combination of trusted and tested mathematical models of materials and system dynamics. Existing study in gait analysis mostly focus on extrinsic parameter such as kinetics and kinematics measures. The analysis of gait especially for disease such as knee osteoarthritis (OA) can provides insight into muscle properties, activation pattern and potential impact on the knee stability and function. This study aims to simulate and compare muscle activation properties in normal individual and individuals with knee osteoarthritis (OA) during gait phases. This study used musculoskeletal modeling software, OpenSim, to analyze the total fiber force and moment of several key muscles (such as medial gastrocnemius, vastus lateralis, and rectus femoris) relative to knee flexion moments. The motion analysis data of three subjects (one normal subject and two knee OA patients) were used in this study. The results shows that total muscle fiber force and muscle moment for medial gastrocnemius and vastus lateralis muscle were higher among individual with knee osteoarthritis. Meanwhile, rectus femoris muscle behaved differently where both parameters were smaller among knee OA patient. These simulated findings suggest that muscle mechanism changed due to knee osteoarthritis disease. The muscles force and moment were observed in this study gave some insight on the devastating effect of the disease on the lower limb joint muscle. Thus, further study is still warranted to investigate the risk of muscle injury and alteration of gait on individual with knee osteoarthritis. It is highly recommended to repeat this study with larger number of subjects for a better data interpretation.

Surrogate multivariate Hurst exponent analysis of gait dynamics

The analysis of complex patterns in human gait dynamics typically relies on measuring stride intervals. Several studies have revealed that the dynamics of stride intervals exhibit fractal characteristics that depend on health, age, and task conditions. However, with current measurement devices, other gait parameters such as the swing and stance intervals can also be measured. This prompts the question of whether multivariate analysis provides a more detailed view of gait dynamics. This work aims to use multivariate rescaled range (MR/S) analysis to characterize the fractality of the combined behavior of the stride, stance, and swing intervals in terms of the Hurst exponent, which is an index of the fractality of a time series. The MR/S method was equipped with a surrogate data analysis, to refer to a corrected Hurst exponent, which is defined as a distance to randomness. Datasets of neurodegenerative conditions (amyotrophic lateral sclerosis, Huntington's disease and Parkinson's disease) from Physionet were used to evaluate the ability of MR/S analysis to characterize different phases of gait dynamics. An index of distance to randomness relative to a 95 % confidence interval obtained from surrogate data was introduced to alleviate biased estimation of the Hurst exponent for relatively small samples. The results showed that the distance to randomness of stance and stride intervals was higher (p < 0.05) than that of swing intervals. On the other hand, the stride interval discriminated (p < 0.05) the control conditions from the neurodegenerative conditions. In contrast, the swing interval discriminated (p < 0.05) the Parkinson's disease from ALS and Huntington's disease. Overall, the results indicate that multivariate analysis is a suitable approach for a detailed characterization of the impact of different gait phases on gait dynamics.

There are unique kinematics during locomotor transitions between level ground and stair ambulation that persist with increasing stair grade

Human ambulation is typically characterized during steady-state isolated tasks (e.g., walking, running, stair ambulation). However, general human locomotion comprises continuous adaptation to the varied terrains encountered during activities of daily life. To fill an important gap in knowledge that may lead to improved therapeutic and device interventions for mobility-impaired individuals, it is vital to identify how the mechanics of individuals change as they transition between different ambulatory tasks, and as they encounter terrains of differing severity. In this work, we study lower-limb joint kinematics during the transitions between level walking and stair ascent and descent over a range of stair inclination angles. Using statistical parametric mapping, we identify where and when the kinematics of transitions are unique from the adjacent steady-state tasks. Results show unique transition kinematics primarily in the swing phase, which are sensitive to stair inclination. We also train Gaussian process regression models for each joint to predict joint angles given the gait phase, stair inclination, and ambulation context (transition type, ascent/descent), demonstrating a mathematical modeling approach that successfully incorporates terrain transitions and severity. The results of this work further our understanding of transitory human biomechanics and motivate the incorporation of transition-specific control models into mobility-assistive technology.

Transferable multi-modal fusion in knee angles and gait phases for their continuous prediction

Objective. The gait phase and joint angle are two essential and complementary components of kinematics during normal walking, whose accurate prediction is critical for lower-limb rehabilitation, such as controlling the exoskeleton robots. Multi-modal signals have been used to promote the prediction performance of the gait phase or joint angle separately, but it is still few reports to examine how these signals can be used to predict both simultaneously. Approach. To address this problem, we propose a new method named transferable multi-modal fusion (TMMF) to perform a continuous prediction of knee angles and corresponding gait phases by fusing multi-modal signals. Specifically, TMMF consists of a multi-modal signal fusion block, a time series feature extractor, a regressor, and a classifier. The multi-modal signal fusion block leverages the maximum mean discrepancy to reduce the distribution discrepancy across different modals in the latent space, achieving the goal of transferable multi-modal fusion. Subsequently, by using the long short-term memory-based network, we obtain the feature representation from time series data to predict the knee angles and gait phases simultaneously. To validate our proposal, we design an experimental paradigm with random walking and resting to collect data containing multi-modal biomedical signals from electromyography, gyroscopes, and virtual reality. Main results. Comprehensive experiments on our constructed dataset demonstrate the effectiveness of the proposed method. TMMF achieves a root mean square error of s in knee angle prediction and a precision of % in gait phase prediction. Significance. We demonstrate the feasibility and validity of using TMMF to predict lower-limb kinematics continuously from multi-modal biomedical signals. This proposed method represents application potential in predicting the motor intent of patients with different pathologies.

Classification of gait phases based on a machine learning approach using muscle synergy.

The accurate detection of the gait phase is crucial for monitoring and diagnosing neurological and musculoskeletal disorders and for the precise control of lower limb assistive devices. In studying locomotion mode identification and rehabilitation of neurological disorders, the concept of modular organization, which involves the co-activation of muscle groups to generate various motor behaviors, has proven to be useful. This study aimed to investigate whether muscle synergy features could provide a more accurate and robust classification of gait events compared to traditional features such as time-domain and wavelet features. For this purpose, eight healthy individuals participated in this study, and wireless electromyography sensors were attached to four muscles in each lower extremity to measure electromyography (EMG) signals during walking. EMG signals were segmented and labeled as 2-class (stance and swing) and 3-class (weight acceptance, single limb support, and limb advancement) gait phases. Non-negative matrix factorization (NNMF) was used to identify specific muscle groups that contribute to gait and to provide an analysis of the functional organization of the movement system. Gait phases were classified using four different machine learning algorithms: decision tree (DT), k-nearest neighbors (KNN), support vector machine (SVM), and neural network (NN). The results showed that the muscle synergy features had a better classification accuracy than the other EMG features. This finding supported the hypothesis that muscle synergy enables accurate gait phase classification. Overall, the study presents a novel approach to gait analysis and highlights the potential of muscle synergy as a tool for gait phase detection.

Overground walking with a constraint force on the nonparetic leg during swing improves weight shift toward the paretic side in people after stroke.

Targeting enhancing the use of the paretic leg during locomotor practice might improve motor function of the paretic leg. The purpose of this study was to determine whether application of constraint force to the nonparetic leg in the posterior direction during overground walking would enhance the use of the paretic leg in people with chronic stroke. Fifteen individuals after stroke participated in two experimental conditions, i.e., overground walking with a constraint force applied to the nonparetic leg and overground walking only. Each participant was tested in the following procedures that consisted of overground walking with either constraint force or no constraint force, instrumented split-belt treadmill walking, and pressure-sensitive gait mat walking before and after the overground walking. Overground walking practice with constraint force resulted in greater enhancement in lateral weight shift toward the paretic side (P < 0.01), muscle activity of the paretic hip abductors (P = 0.04), and propulsion force of the paretic leg (P = 0.05) compared with the results of the no-constraint condition. Overground walking practice with constraint force tended to induce greater increase in self-selected overground walking speed (P = 0.06) compared with the effect of the no-constraint condition. The increase in propulsion force from the paretic leg was positively correlated with the increase in self-selected walking speed (r = 0.6, P = 0.03). Overground walking with constraint force applied to the nonparetic leg during swing phase of gait may enhance use of the paretic leg, improve weight shifting toward the paretic side and propulsion of the paretic leg, and consequently increase walking speed.NEW & NOTEWORTHY Application of constraint force to the nonparetic leg during overground walking induced improved lateral weight shifts toward the paretic leg and enhanced muscle activity of the paretic leg during walking. In addition, one session of overground walking with constraint force might induce an increase in propulsive force of the paretic leg and an increase in self-selected overground walking speed, which might be partially due to the improvement in motor control of the paretic leg.