Measurement of Force Parametersfor the Human Gait Study

Adaptive neural fuzzy reasoning method for recognizing human movement gait phase

Background Gait analysis systems serve as important tools for assessing disturbed gait patterns. Amongst other factors, functional limitations of the shoulder joint may relate to such disturbances. Patient-reported outcome measures, assessment of pain, and active range of motion are commonly used to describe shoulder impairment. Purpose The aim of this cohort study was to evaluate the impact of unilateral limitations of shoulder mobility and pain on gait patterns and to detect correlations between pain, shoulder mobility, and particular phases of human gait using a Zebris gait analysis system. Methods 20 subjects with unilaterally restricted mobility and pain of the affected shoulder and a control group of 10 healthy subjects underwent a gait analysis. Various gait parameters, the DASH score, pain at rest and movement of the affected shoulder, and the active range of motion (aROM) for shoulder flexion and abduction were recorded. Results We determined significant differences of the duration of the loading response (p = 0.021), midstance (p = 0.033), and the terminal stance phase (p = 0.019) between the shoulder group and the control group, with a shorter loading response phase and a longer terminal stance phase of the affected side in the shoulder group. In the shoulder group, we found significant correlations between the DASH and the duration of the midstance phase (p = 0.023) and the terminal stance phase (p = 0.038). In addition, there was a significant correlation between shoulder flexion and the duration of the midstance phase (p = 0.047).

The human gait contains the information of lower limb movement posture. Gait recognition and analysis can provide control information for lower limb rehabilitation robots. In different gait phases, the position of the foot touching the ground is different. Therefore, gait classification can be performed according to plantar pressure. This study investigated the changes in plantar pressure information during human lower limb walking. Firstly, the STM32 single-chip microcomputer is used to collect the plantar pressure information detected by the high dynamic force sensing resistor(HD-FSR); secondly, the collected information is transmitted to the upper computer through Bluetooth; finally, all kinds of gait phases pressure features are analyzed and classified. Since the two constant parameters in the Support Vector Machine(SVM) classification algorithm have a great influence on the classification effect, a SVM classification algorithm based on the Particle Swarm Optimization(PSO) is proposed to realize the recognition of different phases.

Protecting COVID-19 Vaccine Transportation and Storage from Analog Cybersecurity Threats

An exact classification of different gait phases is essential to enable the control of exoskeleton robots and detect the intentions of users. We propose a gait phase classification method based on neural networks using sensor signals from lower limb exoskeleton robots. In such robots, foot sensors with force sensing registers are commonly used to classify gait phases. We describe classifiers that use the orientation of each lower limb segment and the angular velocities of the joints to output the current gait phase. Experiments to obtain the input signals and desired outputs for the learning and validation process are conducted, and two neural network methods (a multilayer perceptron and nonlinear autoregressive with external inputs (NARX)) are used to develop an optimal classifier. Offline and online evaluations using four criteria are used to compare the performance of the classifiers. The proposed NARX-based method exhibits sufficiently good performance to replace foot sensors as a means of classifying gait phases.

Comparison of different kinematic and loading conditions on muscle activation in clinically complete spinal cord-injured subjects stepping unilaterally with manual assistance. To determine if rhythmic lower limb loading or movement could produce rhythmic muscle activation in the nonstepping limb of subjects with clinically complete spinal cord injury (SCI). Human Locomotion Research Center, Department of Neurology, University of California, Los Angeles, USA. We recorded electromyography, joint kinematics, and vertical ground reaction forces as four subjects with clinically complete SCI stepped with manual assistance and partial bodyweight support. For all trials, one limb continuously stepped while the other limb underwent different conditions, including rhythmic lower limb loading in an extended position without limb movement, rhythmic lower limb movement similar to stepping without limb loading, and no lower limb loading or movement with the leg in an extended or flexed position. Three subjects displayed rhythmic muscle activity in the nonstepping limb for trials with rhythmic limb loading, but no limb movement. One subject displayed rhythmic muscle activity in the nonstepping limb for trials without ipsilateral limb loading or movement. The rhythmic muscle activity in the nonstepping limb was similar to the rhythmic muscle activity during bilateral stepping. The human spinal cord can use sensory information about ipsilateral limb loading to increase muscle activation even when there is no limb movement. The results also indicate that movement and loading in one limb can produce rhythmic muscle activity in the other limb even when it is stationary and unloaded. These findings emphasize the importance of optimizing load-related and contralateral sensory input during gait rehabilitation after SCI.

Background: We aimed to study the contribution of upper limb movements to propulsion during walking in typically developing (TD) children (n = 5) and children with hemiplegic and diplegic cerebral palsy (CP; n = 5 and n = 4, respectively).Methods: Using integrated three-dimensional motion capture data and a scaled generic musculoskeletal model that included upper limbs, we generated torque driven simulations of gait in OpenSim. Induced acceleration analyses were then used to determine the contributions of the individual actuators located at the relevant degrees of freedoms of the upper and lower limb joints to the forward acceleration of the COM at each time point of the gait simulation. The mean values of the contribution of the actuators of upper limbs, lower limbs, and gravity in different phases of the gait cycle were compared between the three groups.Findings: The results indicated a limited contribution of the upper limb actuators to COM forward acceleration compared to the contribution of lower limbs and gravity, in the three groups. In diplegic CP, the contribution of the upper limbs seemed larger compared to TD during the preswing and swing phases of gait. In hemiplegic CP, the unaffected arm seemed to contribute more to COM deceleration during (pre)swing, while the affected side contributed to COM acceleration.Interpretation: These findings suggest that in the presence of lower limb dysfunction, the contribution of the upper limbs to forward propulsion is altered, although they remain negligible compared to the lower limbs and gravity.

Most existing lower limb orthosis use actuators and active controller to guide the motion of human lower limbs. Actuators with relatively large power are usually required to compensate the gravity effect of the human lower limbs, even for a normal walking. Hence, design of an orthosis for the weight balance of human lower limbs is desired. For the motion compatibility, the human hip joint is treated as a planar pair and the knee joint as a revolute pair. As a consequence, while the lower limb is in motion, the exact positions of the mass centers of the human lower limbs cannot be obtained. Hence, in this work, topological synthesis of the orthosis mechanisms, which can trace the mass centers of the human thigh and shank, respectively, is implemented. The weight balance of the human lower limbs is achieved by fitting a minimum number of zero-free-length springs. Based on the anthropometric parameters, dimensions of the lower limb orthosis is determined and the proposed design is justified by the simulation executed by the software of ProEngineer. Finally, a first generation prototype is built.

The estimation of human gait phase has recently been used to parameterize continuous lower limb exoskeleton controllers. By deriving the gait phase estimation directly from the user's biological signals (i.e., joint/segment angles), the user can be granted an avenue to control their progression through the gait cycle. The inherent challenge for this strategy is the high accuracy required, as the estimated gait phase determines the timing of the device's assistive and resistive torques. Poor device timing can result in desynchronization of the device with the user. To address this challenge, this paper presents a novel method for using the information from the contralateral leg to improve the estimation accuracy. Simulation results showed a 75.2% reduction in estimation error relative to the state of the art method for able-bodied individuals. Additionally, an average of 47.6% reduction in estimation error was observed among data from three hemiparetic stroke patients. Across both able bodied and hemiparetic subjects, an estimation RMSE of 2.28% was shown. The evaluation of this technique displays its promise for the control of rehabilitative robotics.Clinical Relevance-This improvement to lower limb exoskeleton control aims to encourage greater user participation in robot assisted gait training for stroke rehabilitation.

This paper presents a study on quantitative dynamics analysis of human lower limb using developed wearable sensor systems that can measure reaction force and detect the following gait phases: initial contact, loading response, mid stance, terminal stance, pre-swing, initial swing, mid swing and terminal swing. Since conventional camera-based motion analysis system and reaction force plate system require costly devices, vast space as well as time-consuming calibration experiments, the wearable sensor-based system is much cheaper. Gyroscopes and two-axis accelerometers are incorporated in this wearable sensor system. The former are attached on the surface of the foot, shank and thigh to measure the angular velocity of each segment, and the latter are used to measure inclination of the attached leg segment (shank) in every single human motion cycle for recalibration. Ground reaction forces during human walking are synchronously measured using a wearable force sensor integrated in a shoes mechanism. Finally, experiment has been performed to compare the measurement results from the wearable sensor system with the data obtained from an optical motion analysis system and a force plate. The results showed that the measurement of human lower limb orientations and reaction forces for human dynamics analysis could be reliably implemented using the wearable sensor systems.

Purpose: This paper presents the development of a knowledge based system for the detection of gait phases based on EMGs from muscles of the lower limb. Methods: An empirical analysis of the EMG characteristics for the most representative muscle of every muscle group concerning their suitability for the gait phase detection is presented. The same approach is applied to every lower limb muscle where an EMG could be received. The entities and the decision-making mechanism of the knowledge based system is presented in a formal way. Results: A knowledge based system is built upon the knowledge acquired from this analysis. Finally, an example is presented where the developed knowledge based system is used to support the conceptual design of a drop foot correction system. Conclusions: The knowledge based system can be used in the conceptual design of any rehabilitation system for lower limb disabilities using EMG signals from the lower limbs.

Walking is a fundamental human activity, and analyzing its complexities is essential for understanding gait abnormalities and musculoskeletal disorders. This article delves into the classification of gait phases using advanced machine learning techniques, specifically focusing on dividing these phases into five distinct subphases. The study utilizes data from 100 individuals obtained from an open-access platform and employs two distinct training methodologies. The first approach adopts stratified random sampling, where 80% of the data from each subphase are allocated for training and 20% for testing. The second approach involves participant-based splitting, training on data from 80% of the individuals and testing on the remaining 20%. Preprocessing methods such as Min–Max Scaling (MMS), Standard Scaling (SS), and Principal Component Analysis (PCA) were applied to the dataset to ensure optimal performance of the machine learning models. Several algorithms were implemented, including k-Nearest Neighbors (k-NNs), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), Naive Bayes (Gaussian, Bernoulli, and Multinomial) (NB), Linear Discriminant Analysis (LDA), and Quadratic Discriminant Analysis (QDA). The models were rigorously evaluated using performance metrics like cross-validation score, Mean Squared Error (MSE), Root Mean Squared Error (RMSE), accuracy, and R2 score, offering a comprehensive assessment of their effectiveness in classifying gait phases. In the five subphases analysis, RF again performed strongly with a 94.95% accuracy, an RMSE of 0.4461, and an R2 score of 90.09%, demonstrating robust performance across all scaling methods.

Human gait analysis is a common but also challenging method to diagnose and characterize gait disorders. In recent years, wearable technologies have been investigated for gait analysis including gait phase detection with promising results. In this paper, we investigated the feasibility of a textile-based strain sensor integrated into an ankle brace to detect gait phases. This threadlike resistive sensor detects elongations and is, flexible and easy to integrate into textiles. In a laboratory study, five healthy subjects wearing the ankle brace were asked to walk at various low speeds while the sensor data were recorded. Three commonly used machine learning algorithms (random forest, support vector machines, and neural network) were investigated to differentiate between four phases (initial contact, mid-stance, pre-swing, swing) of the walking data. The random forest classifier performed best on our data set followed by neural network and support vector machines. Using to-fold cross-validation, an average accuracy of 95.49% ± 2.32% was achieved. This result compares well to other methods such as IMU-based technologies and demonstrates the feasibility of our approach of using textile-based strain sensors to detect gait phases in human walking.

Functional electrical stimulation (FES) training of paralyzed muscles is effective for incomplete spinal cord injured patients in the early period of the rehabilitation process. Information of lower limb joint angles and gait phases are very important to assist walking and to restore motor function with FES. It is considered that a small and inexpensive gyroscope is useful to construct a practical sensor system in clinical. In this paper, we examined the simultaneous measurement method of the lower limb joint angles and the gait phases by using the gyroscopes. From the result of the walking analysis with normal subjects, it was indicated that the sensor system could measure the joint angles with sufficient accuracy and could detect practicably the swing phase and the stance phase without mistake. Therefore, the sensing system of lower limb condition with appropriate accuracy that could be used in clinical would be constructed compactly at inexpensive by using the gyroscopes.

The Bayesian optimal experimental design (OED) problem seeks to identify data, sensor configurations, or experiments which can optimally reduce uncertainty. The goal of OED is to find an experiment that maximizes the expected information gain (EIG) about quantities of interest given prior knowledge about expected data. Therefore, within the context of seismic monitoring, we can use Bayesian OED to configure sensor networks by choosing sensor locations, types, and fidelity in order to improve our ability to identify and locate seismic sources. In this work, we develop the framework necessary to use Bayesian OED to optimize the ability to locate seismic events from arrival time data of detected seismic phases. In order to do utilize Bayesian OED we must develop four elements:1. A likelihood function that describes the uncertainty of detection and travel times; 2. A Bayesian solver that takes a prior and likelihood to identify the posterior; 3. An algorithm to compute EIG; and, 4. An optimizer that finds a sensor network which maximizes EIG. Once we have developed this framework, we can explore many relevant questions to monitoring such as: how and what multiphenomenology data can be used to optimally reduce uncertainty, how to trade off sensor fidelity and earth model uncertainty, and how sensor types, number, and locations influence uncertainty