Gait Motion Research Articles

Model predictive control of an active ankle-foot orthosis with non-linear actuation constraints

The goal of active ankle-foot orthoses (AAFO) is to assist the user in recreating a nominal gait motion. Previously proposed control systems for AAFOs have been reactive, with controllers based on the current tracking error. However, the optimal AAFO controller must ensure adequate trajectory tracking while minimizing the amount of assistance provided to the user. To this end, a model predictive controller (MPC) can be considered to determine the optimal control sequence for a given trajectory. The MPC minimizes the control effort, thereby determining the minimal assistance required for a user to regain a natural gait. This work outlines the formulation of a non-linear MPC for an AAFO and its combination with an extended state observer (ESO) for tracking of an AAFO.In this work, a MPC controller is proposed for optimal control of an AAFO, a linear plant subject to non-linear actuation constraints. A novel method is expressed to determine the initial control horizon selection in combination with variable step Newton-based optimization to enable real-time implementation. The MPC control effort is then combined with the estimated disturbance from an ESO to accurately track the desired gait.Simulations and experimental results prove the tracking capabilities of the proposed method. Compared to a tuned proportional–derivative controller, the simulated controller reduced the trajectory tracking error by up to 65.1% and 15.3% in an undisturbed and disturbed environment, respectively. The MPC and ESO combination allows the controller to identify the optimal control action for accurate trajectory tracking. Concurrently, the ESO rejects both external and internal disturbances, with superior tracking capabilities in both undisturbed and heavily disturbed systems, making it the optimal choice for AAFO control.

Neurotypicals with higher autistic traits have delayed visual processing of an approaching life-sized avatar's gait: an event-related potentials study.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder, which is reportedly related to difficulties in the visual processing of human motion, such as biological motion and gestures. Moreover, neurotypical (here, we mention it as individuals without a diagnosis) adults with autistic traits are clumsier than those without autistic traits when passing by others. It is still unclear whether the clumsiness derived from atypical visual processing of another's approaching gait motion. We aim to address this question by investigating the association between autistic traits in neurotypical adults and the visual processing of an approaching life-sized avatar's gait. We clarified a typical visual motion processing and autistic traits in daily life in 26 neurotypical adults by analyzing the subthreshold autism trait questionnaire (SATQ) score, a 24-item self-report scale of ASD, and event-related potentials (ERPs) in response to walking motion of a passing avatar. Videos of walking life-sized virtual avatars approaching and retreating were presented as visual stimuli. The association between the participants' SATQ scores and the latencies and amplitudes of the ERPs was examined. ERP components (N170 and P200) components were identified at T5 and T6 electrodes. Participants reporting higher SATQ scores had longer latencies of P200 at T6 and lower amplitudes of P200 at T5 and T6 electrodes for the approaching avatar than those reporting lower SATQ scores. These findings indicate that adults with autistic traits have delayed and less sensitive visual processing of the approaching avatar. It suggests that while passing another person, these individuals have atypical visual processing of their approach. This study may contribute to elucidating autistic traits from the perspective of visual processing in an environment simulating daily life.

A triboelectric gait sensor system for human activity recognition and user identification

Floor-based sensing systems to monitor human activities and identify users are essential for smart homes and intelligent buildings. A low-cost, easy-to-fabricate, and flexible gait sensor system based on triboelectric nanogenerator (TENG) is presented in this paper, which can transform gait movements, even in the low-frequency form, into electrical impulses without an external power source. To realize this, a TENG-based gait sensor unit with an optimized structure design is proposed to enhance the sensing sensitivity. A sensing insole module is formed by arranging the sensor units according to the foot pressure distribution. The sensor distribution is then explored and improved by comparative studies of gait recognition performance, which increases the recognition efficiency and the possible application in edge computing scenarios. Furthermore, a deep learning network is developed based on long short-term memory (LSTM) and residual units to extract deep features from multichannel time-series gait data to boost recognition performance. Experimental results demonstrate that the proposed gait sensor system can be utilized for human activity recognition and user identification with accuracies of 97.9 % and 99.4 %, respectively. Finally, a gait-sensing-based fitness exercise monitoring system is constructed that can estimate calorie expenditure and distinguish between standard and non-standard fitness activities with an accuracy of 97.2 %. This work can be extended to various application scenarios such as security surveillance, health monitoring, and intelligent control, which provides a new ubiquitous self-powered sensing solution for the Internet of Things (IoT).

TeCVP: A Time-Efficient Control Method for a Hexapod Wheel-Legged Robot Based on Velocity Planning.

Addressing the problem that control methods of wheel-legged robots for future Mars exploration missions are too complex, a time-efficient control method based on velocity planning for a hexapod wheel-legged robot is proposed in this paper, which is named time-efficient control based on velocity planning (TeCVP). When the foot end or wheel at knee comes into contact with the ground, the desired velocity of the foot end or knee is transformed according to the velocity transformation of the rigid body from the desired velocity of the torso which is obtained by the deviation of torso position and posture. Furthermore, the torques of joints can be obtained by impedance control. When suspended, the leg is regarded as a system consisting of a virtual spring and a virtual damper to realize control of legs in the swing phase. In addition, leg sequences of switching motion between wheeled configuration and legged configuration are planned. According to a complexity analysis, velocity planning control has lower time complexity and less times of multiplication and addition compared with virtual model control. In addition, simulations show that velocity planning control can realize stable periodic gait motion, wheel-leg switching motion and wheeled motion and the operation time of velocity planning control is about 33.89% less than that of virtual model control, which promises a great prospect for velocity planning control in future planetary exploration missions.

Dynamic stability during level walking and obstacle crossing in children aged 2-5 years estimated by marker-less motion capture.

In the present study, dynamic stability during level walking and obstacle crossing in typically developing children aged 2-5 years (n = 13) and healthy young adults (n = 19) was investigated. The participants were asked to walk along unobstructed and obstructed walkways. The height of the obstacle was set at 10% of the leg length. Gait motion was captured by three RGB cameras. 2D body landmarks were estimated using OpenPose, a marker-less motion capture algorithm, and converted to 3D using direct linear transformation (DLT). Dynamic stability was evaluated using the margin of stability (MoS) in the forward and lateral directions. All the participants successfully crossed the obstacles. Younger children crossed the obstacle more carefully to avoid falls, as evidenced by obviously decreased gait speed just before the obstacle in 2-year-olds and the increased in maximum toe height with younger age. There was no significant difference in the MoS at the instant of heel contact between children and adults during level walking and obstacle crossing in the forward direction, although children increased the step length of the lead leg to a greater extent than the adults to ensure base of support (BoS)-center of mass (CoM) distance. In the lateral direction, children exhibited a greater MoS than adults during level walking [children: 9.5%, adults: 6.5%, median, W = 39.000, p < .001, rank-biserial correlation = -0.684]; however, some children exhibited a smaller MoS during obstacle crossing [lead leg: -5.9% to 3.6% (min-max) for 4 children, 4.7%-6.4% [95% confidence interval (CI)] for adults, p < 0.05; trail leg: 0.1%-4.4% (min-max) for 4 children, 4.7%-6.4% (95% CI) for adults, p < 0.05]]. These results indicate that in early childhood, locomotor adjustment needed to avoid contact with obstacles can be observed, whereas lateral dynamic stability is frangible.

Convolutional neural network for the detection of Parkinson disease based on hand-draw spiral images

Parkinson’s disease (PD) is a chronic and increasing sickness that hits hundreds-thousands of people globally. Patients who are infected by PD have been proven to show some common symptoms such as slowness of movement, tremors, and freezing of gait. One of the most popular exams to detect the PD is to use the handwritten assessment tool, where the individuals are asked to draw spirals on a template paper. Therefore, this study proposes a convolutional neural network algorithm for detecting the PD by utilizing the hand-draw spiral images. In the present study, balanced spiral images dataset has been utilized for both categories (i.e., Parkinson and healthy). The dataset contains 102 samples as a total number of spiral images (i.e., 51 Parkinson and 51 healthy). Moreover, numerous evaluation measurements were utilized in order to assess the proposed approach such as recall, precision, accuracy, F-measure, specificity, Matthew's correlation coefficient (MCC), and G-mean. Based on the outcomes of the experiments, the proposed approach achieves 93.33% accuracy, 86.67% specificity, 88.24% precision, 100.00% recall, 93.75% F-measure, 93.93% G-mean, and 87.45% MCC. The proposed approach demonstrates promising outcomes in the detection of PD. As well as the proposed convolutional neural network (CNN) approach was outperformed all its comparatives regarding the classification accuracy rate.

A low-cost framework for the recognition of human motion gait phases and patterns based on multi-source perception fusion

A low-cost framework for the recognition of human motion gait phases and patterns based on multi-source perception fusion

Design of topology optimized compliant legs for bio-inspired quadruped robots

Robotic legs are an important component of the quadruped robot for achieving different motion gaits. Although the conventional rigid-link-based legs can generally perform robust motions, they still have the issues with poor sealing when operating in complex and liquid terrains. To cope with this problem, fully compliant legs with monolithic structure have been introduced in recent years to improve the system compactness and structural compliance of quadruped robots. In this article, we present a topology-optimization-based method to achieve efficient design of compliant robotic legs. In order to balance the structural stiffness and bending flexibility of the realized leg, a multi-objective optimization algorithm is utilized. A series of design cases are presented to illustrate the design principle and analytical procedure of the proposed method. In addition, experimental evaluation is also performed, and the results have demonstrated that, a quadruped robot with the optimized legs can successfully achieve stable and continuous straight-line walking motions.

End-to-End Deep Learning Method for Detection of Invasive Parkinson’s Disease

Parkinson's disease directly affects the nervous system are causes a change in voice, lower efficiency in daily routine tasks, failure of organs, and death. As an estimate, nearly ten million people are suffering from Parkinson's disease worldwide, and this number is increasing day by day. The main cause of an increase in Parkinson's disease patients is the unavailability of reliable procedures for diagnosing Parkinson's disease. In the literature, we observed different methods for diagnosing Parkinson's disease such as gait movement, voice signals, and handwriting tests. The detection of Parkinson's disease is a difficult task because the important features that can help in detecting Parkinson's disease are unknown. Our aim in this study is to extract those essential voice features which play a vital role in detecting Parkinson's disease and develop a reliable model which can diagnose Parkinson's disease at its early stages. Early diagnostic systems for the detection of Parkinson's disease are needed to diagnose Parkinson's disease early so that it can be controlled at the initial stages, but existing models have limitations that can lead to the misdiagnosing of the disease. Our proposed model can assist practitioners in continuously monitoring the Parkinson's disease rating scale, known as the Total Unified Parkinson's Disease Scale, which can help practitioners in treating their patients. The proposed model can detect Parkinson's disease with an error of 0.10 RMSE, which is lower than that of existing models. The proposed model has the capability to extract vital voice features which can help detect Parkinson's disease in its early stages.

Biomimetic, Programmable, and Part‐by‐Part Maneuverable Single‐Body Shape‐Morphing Film

Bio-Inspired Shape Morphing Film In article number 2200293, Yongrok Jeong, Jun-Ho Jeong, Inkyu Park, and co-workers propose the fabrication method of the biomimetic, programmable, and part-by-part maneuverable single-body shape morphing film (SMF) inspired by nature. The proposed SMF can realize various movements from nature such as the gripping motion of Drosera capensis and gait motion of the inchworm.

Numerical Analysis of a TCR Adaptive to Different Tree Diameters for Unstructured Forest Environment: A TCR of Variable Diameter with Different Tree Diameters

This paper proposes the optimal design and simulation analysis of a robot for tree-climbing, which has the ability to climb up the tree trunk to adapt to different tree diameter changes. In an unstructured forest environment, considering that the Tree-Climbing Robot (TCR) may collide with branches, reducing the success rate of tree climbing, the mechanical structure and movement strategy to avoid branches are predesigned. The presented TCR mimics several design principles that have been adapted to arboreal animals, including claw-hand grasping and climbing gait movements, and is optimally designed to achieve high manoeuvrability on irregularly shaped trees. Based on the D-H parameterization method, the kinematics model of the TCR is developed and its kinematic equations are solved. The accuracy of the kinematic equations is verified by MATLAB, and the reachable area of the working space of the manipulator of the TCR is determined. Using ADAMS to analyse the kinematics and dynamics of the climbing process of the TCR, the rationality of the gait planning of the robot is verified, and the time-varying characteristic curves of each physical quantity of each structural joint are derived and further simulated through numerical analysis, the results of which will profoundly validate the technical feasibility and design innovation of this presented TCR.

Synthesizing 3D Gait Data with Personalized Walking Style and Appearance

Extracting gait biometrics from videos has been receiving rocketing attention given its applications, such as person re-identification. Although deep learning arises as a promising solution to improve the accuracy of most gait recognition algorithms, the lack of enough training data becomes a bottleneck. One of the solutions to address data deficiency is to generate synthetic data. However, gait data synthesis is particularly challenging as the inter-subject and intra-subject variations of walking style need to be carefully balanced. In this paper, we propose a complete 3D framework to synthesize unlimited, realistic, and diverse motion data. In addition to walking speed and lighting conditions, we emphasize two key factors: 3D gait motion style and character appearance. Benefiting from its 3D nature, our system can provide various gait-related data, such as accelerometer data and depth map, not limited to silhouettes. We conducted various experiments using the off-the-shelf gait recognition algorithm and draw the following conclusions: (1) the real-to-virtual gap can be closed when adding a small portion of real-world data to a synthetically trained recognizer; (2) the amount of real training data needed to train competitive gait recognition systems can be reduced significantly; (3) the rich variations in gait data are helpful for investigating algorithm performance under different conditions. The synthetic data generator, as well as all experiments, will be made publicly available.

Locomotion through soft materials of paired solid spheres as a miniature robot

Locomotion of a small device through soft media such as living tissue remotely actuated by magnetic field is analyzed. The device consists of two differently sized magnetized or magnetizable spheres of millimeter or sub-millimeter diameter interacting with each other magnetically, but otherwise disconnected from each other. The magnetic field transfers energy to the pair by changing direction and magnitude, while the pair executes a reversible motion gait by increasing and decreasing the distance between the sphere centers. The locomotion model is based on previously developed model of solid sphere motion through soft medium.

Event-Triggered Sliding Mode Impulsive Control for Lower Limb Rehabilitation Exoskeleton Robot Gait Tracking

Lower limb rehabilitation exoskeleton robots (LLRERs) play an important role in lower limb rehabilitation training and assistance walking for patients with lower limb movement disorders. In order to reduce and eliminate adverse effects on the accuracy of human motion gait tracking during walking with an LLRER, which is caused by the gravity and friction, the periodic ground shock force, and the human–exoskeleton interaction force, this paper proposes a feedforward–feedback hybrid control strategy of sliding mode impulsive control with gravity and friction compensation, based on the event-triggered mechanism of Lyapunov function. Firstly, to realize high-precision gait tracking with bounded error, some constraints on controller parameters are deduced by analyzing the Lyapunov-based stability. Secondly, the Zeno behavior of impulsive event triggers is excluded by the analysis of three different cases of the triggering time sequence. Finally, the effectiveness of the proposed hybrid controller is verified by the numerical simulation of the LLRER human–exoskeleton integrated system based on a three-link simplified model. It shows that an event-triggered sliding mode impulsive control strategy with gravity and friction compensation can achieve complete gait tracking with bounded error and has excellent dynamic performance under the constraints.

The Validity of Motion Capture Analysis System against the Gold Standard Long-Standing Radiography in the Measurement of Lower Extremity Alignment.

Motion capture analysis (MCA) has the advantage of providing a static and dynamic leg axis analysis without radiation. Nevertheless, there is a lack of evidence regarding the accuracy of this technique. To test whether mechanical femorotibial axis angle (MAA) measurement recorded with a non-invasive MCA system is equal to the gold standard static long-standing full-leg radiographs (LSX) and if the degree of malalignment or other parameters (BMI, body mass, height, age) influence the accuracy, a total of 102 consecutive patients were examined using LSX and MCA. Static as well as all gait motion phases at 3 km/h were analyzed regarding the difference between the two angles. There was no statistical difference for MAA between LSX (MAArad) and MCA (MAAstat) (p = 0.091). There was a strong correlation (rs = 0.858, p < 0.001) between the two methods. The highest accuracy was detected for values of standing MCA. Also, the gait MCA values showed strong correlation with LSX but weaker correlation compared to standing MCA (initial swing rs = 0.549; terminal stance rs = 0.815; p < 0.001). BMI, body mass, and height did not influence the accuracy of MCA. MCA enables frontal alignment analysis with high accuracy and without the side effect of radiation.

Human Gait Activity Recognition Machine Learning Methods.

Human gait activity recognition is an emerging field of motion analysis that can be applied in various application domains. One of the most attractive applications includes monitoring of gait disorder patients, tracking their disease progression and the modification/evaluation of drugs. This paper proposes a robust, wearable gait motion data acquisition system that allows either the classification of recorded gait data into desirable activities or the identification of common risk factors, thus enhancing the subject's quality of life. Gait motion information was acquired using accelerometers and gyroscopes mounted on the lower limbs, where the sensors were exposed to inertial forces during gait. Additionally, leg muscle activity was measured using strain gauge sensors. As a matter of fact, we wanted to identify different gait activities within each gait recording by utilizing Machine Learning algorithms. In line with this, various Machine Learning methods were tested and compared to establish the best-performing algorithm for the classification of the recorded gait information. The combination of attention-based convolutional and recurrent neural networks algorithms outperformed the other tested algorithms and was individually tested further on the datasets of five subjects and delivered the following averaged results of classification: 98.9% accuracy, 96.8% precision, 97.8% sensitivity, 99.1% specificity and 97.3% F1-score. Moreover, the algorithm's robustness was also verified with the successful detection of freezing gait episodes in a Parkinson's disease patient. The results of this study indicate a feasible gait event classification method capable of complete algorithm personalization.

Closing the Wearable Gap: Foot-ankle kinematic modeling via deep learning models based on a smart sock wearable.

The development of wearable technology, which enables motion tracking analysis for human movement outside the laboratory, can improve awareness of personal health and performance. This study used a wearable smart sock prototype to track foot-ankle kinematics during gait movement. Multivariable linear regression and two deep learning models, including long short-term memory (LSTM) and convolutional neural networks, were trained to estimate the joint angles in sagittal and frontal planes measured by an optical motion capture system. Participant-specific models were established for ten healthy subjects walking on a treadmill. The prototype was tested at various walking speeds to assess its ability to track movements for multiple speeds and generalize models for estimating joint angles in sagittal and frontal planes. LSTM outperformed other models with lower mean absolute error (MAE), lower root mean squared error, and higher R-squared values. The average MAE score was less than 1.138° and 0.939° in sagittal and frontal planes, respectively, when training models for each speed and 2.15° and 1.14° when trained and evaluated for all speeds. These results indicate wearable smart socks to generalize foot-ankle kinematics over various walking speeds with relatively low error and could consequently be used to measure gait parameters without the need for a lab-constricted motion capture system.

Surface electromyography-based analysis of the lower limb muscle network and muscle synergies at various gait speeds.

Gait movement is an important activity in daily human life. The coordination of gait movement is directly affected by the cooperation and functional connectivity between muscles. However, the mechanisms of muscle operation at different gait speeds remain unclear. Therefore, this study addressed the gait speed effect on the changes in cooperative modules and functional connectivity between muscles. To this end, surface electromyography (sEMG) signals were collected from eight key lower extremity muscles of twelve healthy subjects walking on a treadmill at high, middle, and low motion speeds. Nonnegative matrix factorization (NNMF) was applied to the sEMG envelope and intermuscular coherence matrix, yielding five muscle synergies. Muscle functional networks were constructed by decomposing the intermuscular coherence matrix, revealing different layers of functional muscle networks across frequencies. In addition, the coupling strength between cooperative muscles grew with gait speed. Different coordination patterns among muscles with changes in gait speed related to the neuromuscular system regulation were identified.

Bilateralna avaskularna nekroza glave butne kosti nakon infekcije COVID-19 - prikaz slučaja

Introduction: Despite having a substantial impact on survivors' mobility and leading to morbidity, musculoskeletal involvement was the post-COVID-19 infection sequelae area that received the least attention in the literature. Reviewing the COVID-19 histories of patients who visited a tertiary health center, the research is aimed at finding an association between COVID-19 infection and avascular femoral head necrosis, a condition brought on by insufficient blood flow. Avascular necrosis of the femoral head is a condition characterized by a restricted range of motion, pain, and disturbance of gait. It is brought on by insufficient blood flow. Case reports: We discussed several examples in which COVID-19 caused individuals to develop bilateral femoral head necrosis. When COVID-19 infection is the only factor present, and corticosteroids are used to treat it, avascular necrosis of the femoral head may become more prevalent. Detecting avascular necrosis of the femoral head in its early stages, hip MRI might reduce the patient's disability and need for continuous treatment. Conclusion: Early identification and treatment of AVN patients reduce the need for surgery and the chance of disabilities.

Human-powered and Wearable Mechanism to Assist Gait Motion by use of the Residual Function of Hemiplegic Patient

Human-powered and Wearable Mechanism to Assist Gait Motion by use of the Residual Function of Hemiplegic Patient