Walking Motion Research Articles

THE INFLUENCE OF ATHLETIC EXERCISES ON THE DEVELOPMENT OF PRESCHOOL CHILDREN MOTOR ABILITIES

The bases of athletic exercises are natural human movements. Their use in the educational process will contribute to the child’s health comprehensive physical development and strengthening. Athletic exercises improve the skills of walking, running, jumping and other movements that the child needs in his future daily life. The integrated use of athletic exercises teaches the child to feel and control the capabilities of his own body and to enjoy it. Athletic exercises stimulate the activity of each child, taking into consideration his individual characteristics. The aim of the research is the development of motor qualities in the educational process of preschool children using various athletic exercises. The objectives of the research are to increase the motor and functional abilities of children with the help of general developing and special athletic exercises, developing strength, speed, endurance, agility, etc., using various athletic exercises, and instilling love and interest in athletics. We used the method of study, analysis, comparison and synthesis of scientific and methodological literature, pedagogical experiment, methods of empirical analysis (observation, demonstration, etc.) The experiment carried out indicates that athletic exercises have the most effective impact on the process of development of preschool children motor abilities. This confirms the peculiarity and necessity of the quantitative and qualitative use of athletic exercises in improving the physical development of the younger generation.

Monitoring walking asymmetries and endpoint control in persons living with chronic stroke: Implications for remote diagnosis and telerehabilitation.

The objective of this study was to assess the feasibility of monitoring and diagnosing compromised walking motion in the frontal plane, particularly in persons living with the chronic effects of stroke (PwCS). The study aimed to determine whether active control of walking in the frontal plane could be monitored and provide diagnostic insights into compensations made by PwCS during community living. The study recruited PwCS with noticeable walking asymmetries and employed a monitoring method to assess frontal plane motion. Monitoring was conducted both within a single assessment and between assessments. The study aimed to uncover baseline data and diagnostic information about active control in chronic stroke survivors. Data were collected using sensors during 6 minutes of walking and compared between the paretic and non-paretic legs. The study demonstrated the feasibility of monitoring frontal plane motion and diagnosing disturbed endpoint control (p < 0.0125) in chronic stroke survivors when comparing the paretic leg to the non-paretic leg. A greater variability was observed in the paretic leg (p < 0.0125), and sensors were able to diagnose a stronger coupling of the body with its endpoint on the paretic side (p < 0.0125). Similar results were obtained when monitoring was conducted over a six-minute walking period, and no significant diagnostic differences were found between the two monitoring assessments. Monitoring did not reveal performance fatigue or debilitation over time. This study's findings indicate that monitoring frontal plane motion is a feasible approach for diagnosing compromised walking motion. The results suggest that individuals with walking asymmetries, exhibit differences in endpoint control and variability between their paretic and non-paretic legs. These insights could contribute to more effective rehabilitation strategies and highlight the potential for monitoring compensations during various activities of daily living.

FOREIGN EXCHANGE MARKET EFFICIENCY: CASE STUDY ON INDONESIA

This research examines the foreign exchange market efficiency in Indonesia using currency pairs chosen based on Indonesia’s largest international trading partner by volume, namely, China, United States and Japan. Consequently, the currency pairs used for this research are IDR/USD, IDR/JPY, IDR/CNY. The test utilizes the Augmented Dickey Fuller procedure in determining whether the datasets contain any unit roots in which case foreign exchange price movements follow a random walk movement (non-stationary), or not (stationary). The research findings show that historical data for all three currencies follow a random walk process or the market is weak-form efficient.

Neural correlates of motor imagery and execution in real-world dynamic behavior: evidence for similarities and differences.

A large body of evidence shows that motor imagery and action execution behaviors result from overlapping neural substrates, even in the absence of overt movement during motor imagery. To date it is unclear how neural activations in motor imagery and execution compare for naturalistic whole-body movements, such as walking. Neuroimaging studies have not directly compared imagery and execution during dynamic walking movements. Here we recorded brain activation with mobile EEG during walking compared to during imagery of walking, with mental counting as a control condition. We asked 24 healthy participants to either walk six steps on a path, imagine taking six steps, or mentally count from one to six. We found beta and alpha power modulation during motor imagery resembling action execution patterns; a correspondence not found performing the control task of mental counting. Neural overlap occurred early in the execution and imagery walking actions, suggesting activation of shared action representations. Remarkably, a distinctive walking-related beta rebound occurred both during action execution and imagery at the end of the action suggesting that, like actual walking, motor imagery involves resetting or inhibition of motor processes. However, we also found that motor imagery elicits a distinct pattern of more distributed beta activity, especially at the beginning of the task. These results indicate that motor imagery and execution of naturalistic walking involve shared motor-cognitive activations, but that motor imagery requires additional cortical resources.

Walking position commanded NAO robot using nonlinear disturbance observer-based fixed-time terminal sliding mode

The walking stability of a humanoid robot is a fundamental problem due to the complex nonlinear dynamic model of the robot’s legs. This work introduces the performance tracking control for the humanoid NAO robot by using a Nonlinear Disturbance Observer (NDO)-based Fixed-time Terminal Sliding Mode (FTSM). The influence of uncertain external disturbance is considered while implementing the control strategy to improve the walking motion of the NAO robot. An NDO is adapted to estimate the uncertainties and external disturbances. A novel FTSM surface is proposed to drive the tracking errors to zero in fixed-time. The designed NDO-based FTSM control law achieves robustness while reducing the chattering phenomenon. The Lyapunov’s stability theory is used to establish the fixed-time stability of the sliding surface and system states under the proposed control method. To validate the performance of the proposed NDO-based FTSM control, a real-time experiment was conducted on a humanoid NAO robot to demonstrate the improved tracking performance in the presence of the uncertain perturbation effect. The effectiveness of the proposed controller design is validated on a flat, upward inclined surface, and compared to another controller.

Depth-aware pose estimation using deep learning for exoskeleton gait analysis

In rehabilitation medicine, real-time analysis of the gait for human wearing lower-limb exoskeleton rehabilitation robot during walking can effectively prevent patients from experiencing excessive and asymmetric gait during rehabilitation training, thereby avoiding falls or even secondary injuries. To address the above situation, we propose a gait detection method based on computer vision for the real-time monitoring of gait during human–machine integrated walking. Specifically, we design a neural network model called GaitPoseNet, which is used for posture recognition in human–machine integrated walking. Using RGB images as input and depth features as output, regression of joint coordinates through depth estimation of implicit supervised networks. In addition, joint guidance strategy (JGS) is designed in the network framework. The degree of correlation between the various joints of the human body is used as a detection target to effectively overcome prediction difficulties due to partial joint occlusion during walking. Finally, a post processing algorithm is designed to describe patients’ walking motion by combining the pixel coordinates of each joint point and leg length. Our advantage is that we provide a non-contact measurement method with strong universality, and use depth estimation and JGS to improve measurement accuracy. Conducting experiments on the Walking Pose with Exoskeleton (WPE) Dataset shows that our method can reach 95.77% PCKs@0.1, 93.14% PCKs@0.08 and 3.55 ms runtime. Therefore our method achieves advanced performance considering both speed and accuracy.

A Semi-Empirical Model of Cathodic Arc Spot Motion under the Influence of External Magnetic Fields

Plasma generation by cathodic arc spots plays a crucial role for coating processes that make use of the Arc-PVD technology. Usually, the arc spot motion over the cathode is steered by a magnetic field of a particular distribution and magnitude to ensure a continuous plasma generation, the avoidance of liquid droplets, and a proper utilization of cathode material by homogeneous erosion. This study presents a semi-empirical model that allows for an examination and characterization of the arc spot motion with regard to direction and speed as a function of an imposed magnetic field. This model considers the different components of random walk, retrograde, and Robson drift motion. Introduced empirical coefficients were determined by corresponding experimental investigations. The calibrated model describes the arc spot motion in good agreement to the recorded spot tracks and can therefore be applied for an evaluation of different magnetic field configurations.

Illusory percepts of curvilinear self-motion when moving through crowds.

Self-motion generates optic flow, a pattern of expanding visual motion. Heading estimation from optic flow analysis is accurate in rigid environments, but it becomes challenging when other human walkers introduce independent motion to the scene. Previous studies showed that heading perception is surprisingly accurate when moving through a crowd of walkers but revealed strong heading biases when either articulation or translation of biological motion were presented in isolation. We hypothesized that these biases resulted from misperceiving the self-motion as curvilinear. Such errors might manifest as opposite biases depending on whether the observer perceived the crowd motion as indication of his/her self-translation or self-rotation. Our study investigated the link between heading biases and illusory path perception. Participants assessed heading and path perception while observing optic flow stimuli with varying walker movements. Self-motion perception was accurate during natural locomotion (articulation and translation), but significant heading biases occurred when walkers only articulated or translated. In this case, participants often reported a curved path of travel. Heading error and curvature pointed in opposite directions. On average, participants perceived the walker motion as evidence for viewpoint rotation leading to curvilinear path percepts.

AudioGuard: Omnidirectional Indoor Intrusion Detection Using Audio Device

Indoor intrusion detection is a critical task for home security. Previous works in intrusion detection suffer from the problems such as blind spots in non-line-of-sight (NLOS) areas, restricted device locations, massive offline training required, and privacy concern. In this article, we design and implement an omnidirectional indoor intrusion detection system, named AudioGuard , using only a pair of speaker and microphone. AudioGuard is able to detect both line-of-sight (LOS) and NLOS intrusions. Our observation of acoustic signal propagation in an indoor environment shows that there exist abundant multipath reflections and human movement introduces Doppler shift in echo signals. We hence capture periodical Doppler shift caused by intruder's walking motion to detect intrusion. Specifically, we first extract the Doppler shift embedded in echo signals, and we then propose a periodicity polarization method to cancel out the impact of the change of radial angle and the distance on periodicity of Doppler shift. Finally, we detect intrusion by measuring periodicity of Doppler shift over time. Extensive experiments show that AudioGuard achieves a miss report rate of 0% and 1.75% for LOS and NLOS intrusion, respectively, and a false alarm rate of 4.17%.

Simplified Physical Model‐based Balance‐preserving Motion Re‐targeting for Physical Simulation

AbstractIn this study, we propose a novel motion re‐targeting framework that provides natural motions of target robot character models similar to the given source motions of a different skeletal structure. The natural target motion requires satisfying kinematic constraints to show a similar motion to the source motion although the kinematical structure between the source and the target character models differ from each other. Simultaneously, the target motion should maintain physically plausible features such as keeping the balance of the target character model. To handle the issue, we utilize a simple physics model (an inverted‐pendulum‐on‐a‐cart model) during the motion re‐targeting process. By interpreting the source motion's balancing property via the pendulum model, the target motion inherits the balancing property of the source motion. The inheritance is derived by performing the motion analysis to extract the necessary parameters for re‐targeting the pendulum model's motion pattern and parameter learning to estimate the suitable parameters for the target character model. Based on the simple physics inheritance, the proposed framework provides balance‐preserving target motions, even applicable to the full‐body physics simulation or a real robot control. We validate the framework by experimenting with motion re‐targeting from animal character and human character source models to the quadruped‐ and humanoid‐type target models with Muaythai punching, kicking and walking motions. We also implement comparisons with the existing methods to clarify the enhancement.

Characterization Technique for a Doppler Radar Occupancy Sensor

Occupancy sensors are electronic devices used to detect the presence of people in monitored areas, and the output of these sensors can be used to optimize lighting control, heating and ventilation control, and real-estate utilization. Testing methods already exist for certain types of occupancy sensors (e.g., passive infrared) to evaluate their relative performance, allowing manufacturers to report coverage patterns for different types of motion. However, the existing published techniques are mostly tailored for passive-infrared sensors and therefore limited to evaluation of large motions, such as walking and hand movement. Here we define a characterization technique for a Doppler radar occupancy sensor based on detecting a small motion representing human breathing, using a well-defined readily reproducible target. The presented technique specifically provides a robust testing method for a single-channel continuous wave Doppler-radar based occupancy sensor, which has variation in sensitivity within each wavelength of range. By comparison with test data taken from a human subject, we demonstrate that the mobile target provides a reproducible alternative for a human target that better accounts for the impact of sensor placement. This characterization technique enables generation of coverage patterns for breathing motion for single-channel continuous wave Doppler radar-based occupancy sensors.

Effects of backward-directed resistance on propulsive force generation during split-belt treadmill walking in non-impaired individuals.

Backward-directed resistance is the resistance applied in the opposite direction of the individual's walking motion. Progressive application of backward-directed resistance during walking at a target speed engages adaptive motor control to maintain that speed. During split-belt walking, a motor control strategy must be applied that allows the person to keep up with the two belts to maintain their position on the treadmill. This situation becomes more challenging when progressive resistance is applied since each limb needs to adapt to the greater resistance to maintain the position. We propose that strategies aimed at changing relative propulsion forces with each limb may explain the motor control strategy used. This study aimed to identify the changes in propulsive force dynamics that allow individuals to maintain their position while walking on an instrumented split-belt treadmill with progressively increasing backward-directed resistance. We utilized an instrumented split-belt treadmill while users had to overcome a set of increasing backward-directed resistance through the center of mass. Eighteen non-impaired participants (mean age = 25.2 ± 2.51) walked against five levels of backward resistance (0, 5, 10, 15, and 20% of participant's body weight) in two different modalities: single-belt vs. split-belt treadmill. On the single-belt mode, the treadmill's pace was the participant's comfortable walking speed (CWS). In split-belt mode, the dominant limb's belt pace was half of the CWS, and the non-dominant limb's belt speed was at the CWS. We assessed differences between single-belt vs. split-belt conditions in the slope of the linear relationship between change in propulsive impulse relative to change of backward resistance amount. In split-belt conditions, the slower limb showed a significantly steeper increase in propulsion generation compared to the fast limb across resistance levels. As a possible explanation, the slow limb also exhibited a significantly increased slope of the change in trailing limb angle (TLA), which was strongly correlated to the propulsive impulse slope values. We conclude that the motor control strategy used to maintain position on a split-belt treadmill when challenged with backward-directed resistance is to increase the propulsive forces of the slow limb relative to the fast limb by progressively increasing the TLA. ClinicalTrials.gov, identifier NCT04877249.

A new method for finding the proper initial conditions in passive locomotion of bipedal robotic systems

In this paper, the walking motion of a biped robot consisting of upper and lower body members (arms, legs, upper trunk and neck) on an inclined surface is analyzed kinematically and dynamically. Some of the challenges faced by this research include the dual-phase dynamics that govern the periodic gait of this robotic system and the consideration of all the body parts that are involved in the walking motion; which make it extremely difficult to derive the kinetic equations of such robotic systems. To deal with these challenges, the Gibbs-Appell formulation and the Newton's impact laws are employed to derive the most general form of the system's dynamic equations in the swing and transient phases of motion. Subsequently, by using the obtained motion equations and implementing a systematic procedure, we achieve an eigenvalue problem. By solving this problem, the suitable initial conditions that are necessary for the passive gait of this biped robot on a sloping surface are determined. By considering the obtained initial conditions as well as the dynamic response of the system to the Earth's gravity, we describe a general method for designing a biped robot's walking trajectory on an inclined surface. The most important feature of the designed trajectory is its close agreement with the trajectory followed by a biped robot walking passively down a sloping ramp. This close match between the said trajectories enables us to guide the robot in tracking a desired path by applying very small control torques to the robot joints at the start of each step. This claim is corroborated by the simulations carried out for a biped walking robot composed of 6 rigid links, in which the control torques that are applied to the actuated robot joints are obtained by employing a feedback linearization method and via a designed LQR controller.

A steerable robot walker driven by two actuators

Abstract This article describes a robot walker based on a new single degree-of-freedom six-bar leg mechanism that provides rectilinear, non-rotating, movement of the foot. The walker is statically stable and requires only two actuators, one for each side, to provide effective walking movement on a flat surface. We use Curvature Theory to design a four-bar linkage with a flat-sided coupler curve and then adds a translating link so that walker foot follows this coupler curve in rectilinear movement. A prototype walker was constructed that weighs 1.6 kg, is 180 mm tall, and travels at 162 mm/s. This is an innovative legged robot that has a simple reliable design.

A Novel Spatial–Temporal Network for Gait Recognition Using Millimeter-Wave Radar Point Cloud Videos

Gait recognition is a behavioral biometric technology that aims to identify individuals through their manner of walking. Compared with vision and wearable solutions, millimeter-wave (mmWave)-radar-based gait recognition has drawn attention because radar sensing is privacy-preserving and non-contact. However, it is challenging to capture the motion dynamics of walking people from mmWave radar signals, which is crucial for robust gait recognition. In this study, a novel spatial–temporal gait recognition network based on mmWave radar is proposed to address this problem. First, a four-dimensional (4D) radar point cloud video (RPCV) was introduced to characterize human walking patterns. Then, a PointNet block was utilized to extract spatial features from the radar point clouds in each frame. Finally, a Transformer layer was applied for the spatial–temporal modeling of the 4D RPCVs, capturing walking motion information, followed by fully connected layers to output the identification results. The experimental results demonstrated the superiority of the proposed network over mainstream networks, which achieved the best human identification performance on a dataset of 15 volunteers.

A comparison of two methods for moving through a virtual environment: walking in place and interactive redirected walking

Moving through a virtual environment that is larger than the physical space in which the participant operates has been a challenge since the early days of virtual reality. Many different methods have been proposed, such as joystick-based navigation, walking in place where the participant makes walking movements but is stationary in the physical space, and redirected walking where the environment is surreptitiously changed giving the illusion of walking in a long straight line in the virtual space but maybe a circle in the physical space. Each type of method has its limitations, ranging from simulator sickness to still requiring more physical space than is available. Stimulated by the COVID-19 lockdown, we developed a new method of locomotion which we refer to as interactive redirected walking. Here, the participant really walks but, when reaching a boundary, rotates the virtual world so that continuation of walking is always within the physical boundary. We carried out an exploratory study to compare this method with walking in place with respect to presence using questionnaires as well as qualitative responses based on comments written by the participants that were subjected to sentiment analysis. Surprisingly, we found that smaller physical boundaries favor interactive redirected walking, but for boundary lengths more than approximately 7 adult paces, the walking-in-place method is preferable.

Development of a regenerable dual-trigger tripedal DNA walker electrochemical biosensor for sensitive detection of microRNA-155

Since microRNAs (miRNAs) are valuable biomarkers for disease diagnosis and prognosis, the pursuit of enhanced detection sensitivity through signal amplification strategies has emerged as a prominent focus in low-abundance miRNA detection research. DNA walkers, as dynamic DNA nanodevice, have gained significant attention for their applications as signal amplification strategies. To overcome the limitations of unipedal DNA walkers with a restricted signal amplification efficiency, there is a great need for multi-pedal DNA walkers that offer improved walking and signal amplification capabilities. Here, we employed a combination of catalytic hairpin assembly (CHA) and APE1 enzymatic cleavage reactions to construct a tripedal DNA walker, driving its movement to establish a cascade signal amplification system for the electrochemical detection of miRNA-155. The biosensor utilizes tumor cell-endogenous microRNA-155 and APE1 as dual-trigger for DNA walker formation and walking movement, leading to highly efficient and controllable signal amplification. The biosensor exhibited high sensitivity, with a low detection limit of 10 pM for microRNA-155, and successfully differentiated and selectively detected microRNA-155 from other interfering RNAs. Successful detection in 20 % serum samples indicates its potential clinical application. In addition, we harnessed strand displacement reactions to create a gentle yet efficient electrode regeneration strategy, to addresses the time-consuming challenges during electrode modification processes. We have successfully demonstrated the stability of current signals even after multiple cycles of electrode regeneration. This study showcased the high-efficiency amplification potential of multi-pedal DNA walkers and the effectiveness and versatility of strand displacement in biosensing applications. It opens a promising path for developing regenerable electrochemical biosensors. This regenerable strategy for electrochemical biosensors is both label-free and cost-effective, and holds promise for detecting various disease-related RNA targets beyond its current application.

Comparative Analysis of Audio Processing Techniques on Doppler Radar Signature of Human Walking Motion Using CNN Models.

Artificial intelligence (AI) radar technology offers several advantages over other technologies, including low cost, privacy assurance, high accuracy, and environmental resilience. One challenge faced by AI radar technology is the high cost of equipment and the lack of radar datasets for deep-learning model training. Moreover, conventional radar signal processing methods have the obstacles of poor resolution or complex computation. Therefore, this paper discusses an innovative approach in the integration of radar technology and machine learning for effective surveillance systems that can surpass the aforementioned limitations. This approach is detailed into three steps: signal acquisition, signal processing, and feature-based classification. A hardware prototype of the signal acquisition circuitry was designed for a Continuous Wave (CW) K-24 GHz frequency band radar sensor. The collected radar motion data was categorized into non-human motion, human walking, and human walking without arm swing. Three signal processing techniques, namely short-time Fourier transform (STFT), mel spectrogram, and mel frequency cepstral coefficients (MFCCs), were employed. The latter two are typically used for audio processing, but in this study, they were proposed to obtain micro-Doppler spectrograms for all motion data. The obtained micro-Doppler spectrograms were then fed to a simplified 2D convolutional neural networks (CNNs) architecture for feature extraction and classification. Additionally, artificial neural networks (ANNs) and 1D CNN models were implemented for comparative analysis on various aspects. The experimental results demonstrated that the 2D CNN model trained on the MFCC feature outperformed the other two methods. The accuracy rate of the object classification models trained on micro-Doppler features was 97.93%, indicating the effectiveness of the proposed approach.

Interactive Locomotion Style Control for a Human Character based on Gait Cycle Features

AbstractThis article introduces a data‐driven locomotion style controller for full‐body human characters using gait cycle features. Based on gait analysis, we define a set of gait features that can represent various locomotion styles as spatio‐temporal patterns within a single gait cycle. We compute the gait features for every single gait cycle in motion capture data and use them to search for the desired motion. Our real‐time style controller provides users with visual feedback for the changing inputs, exploiting the Motion Matching algorithm. We also provide a graphical controller interface that visualizes our style representation to enable intuitive control for users. We show that the proposed method is capable of retrieving appropriate locomotions for various gait cycle features, from simple walking motions to single‐foot motions such as hopping and dragging. To validate the effectiveness of our method, we conducted a user study that compares the usability and performance of our system with those of an existing footstep animation tool. The results show that our method is preferred over the baseline method for intuitive control and fast visual feedback.

Environment‐dependent population dynamics emerging from dynamic energy budgets and individual‐scale movement behaviour

Individual biology influences environment‐dependent population dynamics through life history. Population models that consider individual physiology are therefore popular for modelling dynamics under various environments. In recent years, a quantitative framework integrating metabolic theory (dynamic energy budget theory) into individual‐based models (DEB‐IBMs) has emerged to investigate the link from individual physiology to demography. However, this link shows substantial variation, some of which may be explained by individual behaviours operating in local environments. Current DEB‐IBMs focusing on population modelling do not consider individual‐scale behaviours and instead resort to imposed population‐level relationships. We therefore propose to extend the DEB‐IBM approach to consider the role of individual‐scale behaviour by replacing the functional response – a population‐averaged phenomenological relationship – with individual‐scale foraging mechanisms in a spatially heterogeneous environment. Using this model, we simulate consumer dynamics in a consumer‐resource system for different individual behaviours across a range of temperature, resource carrying capacity and individual variability values. We further illustrate the model in a case study by comparing simulated population dynamics with both the classical DEB‐IBM and experimental data for a laboratory Daphnia magna population. Simulations reveal that temperature‐ and resource‐dependent consumer extinction probability patterns change with individual behaviour. Moreover, simulations agree with experimental data on D. magna populations: dynamics after the initial growth peak were better captured under random walk movement behaviour compared to the classical DEB‐IBM. Both the simulation and case study showed how fine‐scale behaviour mediates the metabolism's impact on population dynamics by allowing for the emergence of different functional responses. Our model thus provides a link between metabolism, life history and population dynamics by centring behavioural mechanisms and environmental heterogeneity at the individual scale. This expansion of the modelling toolbox for physiologically structured populations can boost theory development by bridging various fields in ecology, and contribute to our understanding of environment‐dependent ecological patterns.