Walking Motion Research Articles

Controller design and experimental validation of walking for a musculoskeletal bipedal lower limb robot based on the spring-loaded inverted pendulum model

In the study of PAM (McKibben-type pneumatic artificial muscle)-driven bipedal robots, it is essential to investigate whether the intrinsic properties of the PAM contribute to achieving stable robot motion. Furthermore, it is crucial to determine if this contribution can be achieved through the interaction between the robot’s mechanical structure and the PAM. In previous research, a PAM-driven bipedal musculoskeletal robot was designed based on the principles of the spring-loaded inverted pendulum (SLIP) model. The robot features low leg inertia and concentrated mass near the hip joint. However, it is important to note that for this robot, only the design principles were based on the SLIP model, and no specialized controller was specifically designed based on the model. To address this issue, based on the characteristics of the developed robot, a PAM controller designed also based on the SLIP model is developed in this study. This model-based controller regulates ankle flexion PAM to adjust the direction of the ground reaction force during robot walking motion. The results indicate that the proposed controller effectively directs the leg ground reaction force towards the center of mass during walking.

Walking and talking for well‐being: Exploring the effectiveness of walk and talk therapy

AbstractDespite the rising trend of integrating nature and movement into talking therapy, the extent of empirical support remains limited. This study explored the effects of a walk and talk intervention on adult mood and well‐being using a multiple baseline design with pre, post and follow‐up assessments. Five adults (3 males and 2 females) from a technology company took part in a walk and talk intervention lasting approximately 6–8 sessions. They completed self‐report measures at baseline, post‐intervention and follow‐up, with semi‐structured interviews conducted at follow‐up. Results showed a reduction in psychological distress and an increase in well‐being for all participants from baseline to follow‐up. Qualitative data from a thematic analysis supported these findings: participants found the natural, calming effect of the outdoors, combined with the movement of walking, facilitated a sense of freedom to express themselves and, in doing so, maximise the benefits of the therapeutic space. These included gaining a broader perspective, heightened self‐awareness and greater acceptance of oneself, others and life's challenges. This study offers real‐world evidence supporting walk and talk therapy as an effective intervention for adult well‐being and mood.

Piezoelectric energy harvesting from walking motion of a passive biped robot model with flexible legs

Passive dynamic walking biped robots utilize the inherent dynamics of their structure to achieve walking motion without continuous actuation, enhancing energy efficiency and stability. Replacing conventional rigid legs with flexible elastic beams in the simplest passive walker will be associated with undesirable vibrations. Despite the positive effect of the damper in reducing vibrations, the current research has used the piezoelectric energy harvesting with the aim of reducing the wasted energy of the gait cycle and improving its stability range. For this purpose, the electromechanical Lagrange equations are determined by applying Hamilton's principle and the assumed mode method and then, the new definition of the step function is obtained through numerical methods, solving a boundary value problem to establish proper initial conditions for stable walking. Numerical simulations indicate that the piezoelectric energy harvester can create a stable period-one gait cycle by optimizing the length of the piezo layers attached to the undamped elastic legs despite the presence of small vibrations. Additional results are presented in bifurcation diagrams, investigating the effect of important physical and structural parameters such as slope angle, length and thickness of the piezo layer.

Effectiveness of Proprioceptive Neuromuscular Facilitation on Walking Speed, Discomfort, and Knee Extension in an Acute Stroke Patient: A Case Study

This study investigates the impact of proprioceptive neuromuscular facilitation (PNF) on improving functional mobility in a stroke patient, with a focus on walking speed, discomfort levels, and knee extension range of motion (ROM). PNF techniques were applied over four weeks to assess their effectiveness. The patient's walking performance was evaluated using the 6-minute walk test (6MWT), and discomfort was measured using the visual analogue scale (VAS) during walking and knee extension movements in both seated and standing positions. The results revealed a significant reduction in 6MWT time from 16.47 seconds to 14.2 seconds post-intervention. VAS scores showed a marked decrease in discomfort during the 6MWT from 8 to 1 and during knee extension from 7 to 4 in both positions. Additionally, the knee extension ROM increased significantly, with the most pronounced improvement observed in the seated position (from 141.3° to 158.5°). These findings suggest that PNF is an effective therapeutic approach for enhancing walking performance and reducing discomfort related to hamstring stiffness in acute stroke patients. The study's limitations include its single-case design, limiting the generalizability of the results. Future research with a larger sample size and more objective assessment tools is recommended to validate these findings.

Observations and Considerations for Implementing Vibration Signals as an Input Technique for Mobile Devices

This work examines swipe-based interactions on smart devices, like smartphones and smartwatches, that detect vibration signals through defined swipe surfaces. We investigate how these devices, held in users’ hands or worn on their wrists, process vibration signals from swipe interactions and ambient noise using a support vector machine (SVM). The work details the signal processing workflow involving filters, sliding windows, feature vectors, SVM kernels, and ambient noise management. It includes how we separate the vibration signal from a potential swipe surface and ambient noise. We explore both software and human factors influencing the signals: the former includes the computational techniques mentioned, while the latter encompasses swipe orientation, contact, and movement. Our findings show that the SVM classifies swipe surface signals with an accuracy of 69.61% when both devices are used, 97.59% with only the smartphone, and 99.79% with only the smartwatch. However, the classification accuracy drops to about 50% in field user studies simulating real-world conditions such as phone calls, typing, walking, and other undirected movements throughout the day. The decline in performance under these conditions suggests challenges in ambient noise discrimination, which this work discusses, along with potential strategies for improvement in future research.

Computational biomechanics for a standing human body: Modal analysis and simulation.

We develop computational mechanical modeling and methods for the analysis and simulation of the motions of a human body. This type of work is crucial in many aspects of human life, ranging from comfort in riding, the motion of aged persons, sports performance and injuries, and many ergonomic issues. A prevailing approach for human motion studies is through lumped parameter models containing discrete masses for the parts of the human body with empirically determined spring, mass, damping coefficients. Such models have been effective to some extent; however, a much more faithful modeling method is to model the human body as it is, namely, as a continuum. We present this approach, and for comparison, we choose two digital CAD models of mannequins for a standing human body, one from the versatile software package LS-DYNA and another from open resources with some of our own adaptations. Our basic view in this paper is to regard human motion as a perturbation and vibration from an equilibrium position which is upright standing. A linear elastodynamic model is chosen for modal analysis, but a full nonlinear viscoelastoplastic extension is possible for full-body simulation. The motion and vibration of these two mannequin models is analyzed by modal analysis, where the normal vibration modes are determined. LS-DYNA is used as the supercomputing and simulation platform. Four sets of low-frequency modes are tabulated, discussed, visualized, and compared. Higher frequency modes are also selectively displayed. We have found that these modes of motion and vibration form intrinsic basic modes of biomechanical motion of the human body. This view is supported by our finding of the upright walking motion as a low-frequency mode in modal analysis. Such a "walking mode" shows the in-phase and out-of-phase movements between the legs and arms on the left and right sides of a human body, implying that this walking motion is spontaneous, likely not requiring any directives from the brain. Dynamic motions of CAD mannequins are also simulated by drop tests for comparisons and the validity of the models is discussed through Fourier frequency analysis. All computed modes of motion are collected in several sets of video animations for ease of visualization. Samples of LS-DYNA computer codes are also included for possible use by other researchers.

Muscle Property Optimization Based on Motion Intentionality for Motor Skill Augmentation

Understanding motion intentionality is important to augment motor skills. This can be explained by the anatomical structure and innate immune mechanisms of humans. This study aimed to analyze human motion using musculoskeletal information and estimate motion intentionality, motion direction, and muscle properties. The calculation method for the easy-to-move musculoskeletal direction in the task space was reversed to calculate the optimal muscle properties and matching rate for the motion direction. The analysis results of each walking and feint motion showed that the muscles were trained toward reduced muscular effort and improved skills for each motion. In addition, the results of the optimization of the matching rate and muscle properties indicate that the optimized muscle properties can express the motion better than the reference values, and the average matching rate increases by 2.41×10-1, particularly for a feint motion. Therefore, skill acquisition and augmentation were achieved.

Walking representation and simulation based on multi-source image fusion and multi-agent reinforcement learning for gait rehabilitation

In the formulation of strategies for walking rehabilitation, achieving precise identification of the current state and making rational predictions about the future state are crucial but often unrealized. To tackle this challenge, our study introduces a unified framework that integrates a novel 3D walking motion capture method using multi-source image fusion and a walking rehabilitation simulation approach based on multi-agent reinforcement learning. We found that, (i) the proposal achieved an accurate 3D walking motion capture and outperforms other advanced methods. Experimental evidence indicates that, compared to similar visual skeleton tracking methods, the proposed approach yields results with higher Pearson correlation (r=0.93), intra-class correlation coefficient (ICC(2,1)=0.91), and narrower confidence intervals ([0.90,0.95] for r, [0.88,0.94] for ICC(2,1)) when compared to standard results. The outcomes of the proposed approach also exhibit commendable correlation and concurrence with those obtained through the IMU-based skeleton tracking method in the assessment of gait parameters ([0.85,0.89] for r, [0.75,0.81] for ICC(2,1)); (ii) multi-agent reinforcement learning has the potential to be used to solve the simulation task of gait rehabilitation. In mimicry experiment, our proposed simulation method for gait rehabilitation not only enables the intelligent agent to converge from the initial state to the target state, but also observes evolutionary patterns similar to those observed in clinical practice through motor state resolution. This study offers valuable contributions to walking rehabilitation, enabling precise assessment and simulation-based interventions, with potential implications for clinical practice and patient outcomes.

Effects of basic movements and gait in the case of homolateral imitative synkinesis

BackgroundHomolateral Imitative Synkinesis (HIS) is a rare form of associative movement between the ipsilateral upper and lower limbs. The incidence of HIS or its correlation with various movements remains uninvestigated. This study expounds on the characteristics of HIS, the frequency at which it occurs, and its relationship with movement, particularly walking. MethodsThis study included 1328 patients with acute stroke admitted to our healthcare facility between October 2019 and February 2022. We evaluated the severity of motor paralysis and sensory impairment in instances where HIS manifested, and assessed the relationship between HIS, basic activities, and gait. ResultsHIS was observed in 13/1328 patients. Motor paralysis was mild in all the cases. Each patient displayed a degree of sensory impairment, albeit of varying severity. HIS did not manifest during basic activities but was evident during walking movements in five instances. These patients displayed involuntary repetitive lifting of their upper limbs during the swing phase of their gait. Some individuals expressed discontent with involuntary upper-limb movements, citing them as contributors to a suboptimal gait. ConclusionsThis study identified HIS as a rare syndrome, manifesting at a rate of 0.9%. Focus was more common in patients with damage to the thalamus and parietal lobe. No manifestations of the HIS occurred during basic activities, suggesting a weak correlation between the HIS and such activities. Certain patients exhibit HIS during gait, report suboptimal gait, and have an increased risk of falls, potentially influencing their gait proficiency.

Penilaian Spatial dan Temporal Gaya Berjalan dalam Satah Hadapan menggunakan MediaPipe Pose

Gait analysis is a vital field in biomechanics, focusing on studying human walking patterns and movement. Traditional methods relied on visual observation, lacking accuracy and objectivity. Instrumented gait analysis improved measurements using specialized equipment like motion capture systems and wearable sensors, but had limitations due to cost, and markers or sensors interfering with natural movement. The field underwent a significant transformation with the advent of markerless pose estimation-based gait analysis, which harnessed computer vision methodologies to monitor human movement without the requirement for specialized equipment, delivering a cost-effective and easily accessible analysis in real-world environments. However, it still faces challenges, including limitations in the sagittal plane and reliance on computationally demanding models like OpenPose. This study introduced a novel frontal plane gait analysis approach using the lightweight MediaPipe Pose model and a single camera setup. The objective is to evaluate the feasibility, and accuracy of MediaPipe Pose by comparing it to the established 3D Vicon motion capture system. The proposed approach tracked body keypoints during gait, detected gait events based on ankle depth changes and vertical difference between left and right ankles, and calculated gait parameters. The findings demonstrated that MediaPipe Pose-based gait analysis showed promise for accurately analyzing gait parameters in the frontal plane with low mean absolute error (0.00–0.30). Combining this method with satellite technology enhances e-health by facilitating teleconsultations, remote diagnostics, and extending healthcare services to disaster-stricken or remote areas.

Relationship between walking movement and reproductive traits in the red flour beetle Tribolium castaneum

AbstractMany animal species have movement abilities. Behavior is important for evolutionary ecology because animal movement leads to dispersal, migration, search for food and mates, and escaping from enemies. However, individual differences in movement activity are found within a population. This phenomenon can be affected by various factors, one of which is suggesting that higher moving activity has fitness cost, whereas lower moving activity has benefits. Animal movement may also affect reproduction (e.g., resource allocation tradeoff between movement and reproduction as well as intra‐ and intersexual selection). Although many previous studies have investigated the relationship between movement and reproduction, less attention has been paid to walking movement. In this study, previous studies that investigated the relationship between movement and reproduction were reviewed using the flour beetle Tribolium castaneum, a model insect in behavioral ecology and genetics. Several previous studies suggest that beetle walking is strongly associated with male and female reproductive traits. In recent years, empirical studies on the correlation between walking and other traits have increased, particularly in T. castaneum. Although this species can fly and walk, the movement is often discussed without discriminating between flight and walk. Differences in modes of movement may affect the correlation between movement and other traits; thus, discussing each mode of movement separately is necessary.

Synergistic powering of DNA walker movement by endogenous dual enzymes for constructing dual-mode biosensors

To achieve highly sensitive and reliable detection of apurinic/apyrimidinic endonuclease 1 (APE1), a critical cancer diagnostic biomarker, we designed a DNA walker-based dual-mode biosensor, utilizing cellular endogenous dual enzymes (APE 1 and Flap endonuclease 1 (FEN 1)) to collaborate in activating and propelling DNA walker motion on DNA-functionalized Au nanoparticles. Incorporating both fluorescence and electrochemical detection modes, this system leverages signal amplification from DNA walker movement and cascade amplification through tandem hybridization chain reactions (HCR), achieving highly sensitive detection of APE 1. In the fluorescence mode, continuous DNA walker movement, initiated by APE1 and driven by FEN1, generates a robust signal response within a concentration range of 0.01–500 U mL−1, presenting a good linearity in the concentration range of 0.01–10 U mL−1, with a detection limit of 0.01 U mL−1. In the electrochemical detection module, the cascade upstream DNA walker and downstream HCR dual signal amplification strategy further enhances the sensitivity of APE1 detection, extending the linear range to 0.01–50 U mL−1 and reducing the detection limit to 0.002 U mL−1. Rigorous validation demonstrates the biosensor's specificity and anti-interference capability against multiple enzymes. Moreover, it effectively distinguishes cancer cells from normal cell lysates, exhibiting excellent stability and consistency in the dual-modes. Overall, our findings underscore the efficacy of the developed dual-mode biosensor for detecting APE1 in serum and cell lysates samples, indicating its potential for clinical applications in disease diagnosis.

IA-LSTM: Interaction-Aware LSTM for Pedestrian Trajectory Prediction.

Predicting the trajectory of pedestrians in crowd scenarios is indispensable in self-driving or autonomous mobile robot field because estimating the future locations of pedestrians around is beneficial for policy decision to avoid collision. It is a challenging issue because humans have different walking motions, and the interactions between humans and objects in the current environment, especially between humans themselves, are complex. Previous researchers focused on how to model human-human interactions but neglected the relative importance of interactions. To address this issue, a novel mechanism based on correntropy is introduced. The proposed mechanism not only can measure the relative importance of human-human interactions but also can build personal space for each pedestrian. An interaction module, including this data-driven mechanism, is further proposed. In the proposed module, the data-driven mechanism can effectively extract the feature representations of dynamic human-human interactions in the scene and calculate the corresponding weights to represent the importance of different interactions. To share such social messages among pedestrians, an interaction-aware architecture based on long short-term memory network for trajectory prediction is designed. Experiments are conducted on two public datasets. Experimental results demonstrate that our model can achieve better performance than several latest methods with good performance.

Design of flexible micro-porous fiber with double conductive network synergy for high-performance strain sensor

Flexible wearable strain sensor is a vital part of intelligent wearable systems due to their broad applications in human–machine interaction and smart clothes. Flexible strain sensor fabricated via conventional blending conductive polymer composites has some drawbacks, for example, low sensitivity, large hysteresis, and “shoulder-peak” phenomenon, which limit their practical applications greatly. Herein, we prepared a fiber-shaped strain sensor with a micro-porous, three-layer structure together with a dual conductive network based on reduced graphene oxide (rGO) and multi-walled carbon nanotubes (MWCNTs). The strain sensor demonstrates excellent response behaviors, including splendid stretchability (686 %), wide detection range (120 %), high sensitivity (gauge factor = 300), fast response time (300 ms) and excellent durability (over 9000 cycles). The unique dual conductive network structure endows the strain sensor with remarkable recoverability and reproducibility. The resistance of the strain sensor can recover to nearly initial conditions after the releasing process, exhibiting lower hysteresis. The “shoulder-peak” phenomenon has also been weakened based on the microstructure and conductive network design. Based on the high sensitivity and excellent response behavior, the fiber-shaped strain sensor performs real-time monitoring of breathing, finger bending, walking, and other human motions, providing a good candidate for its application in intelligent wearable devices.

EMOKINE: A software package and computational framework for scaling up the creation of highly controlled emotional full-body movement datasets

EMOKINE is a software package and dataset creation suite for emotional full-body movement research in experimental psychology, affective neuroscience, and computer vision. A computational framework, comprehensive instructions, a pilot dataset, observer ratings, and kinematic feature extraction code are provided to facilitate future dataset creations at scale. In addition, the EMOKINE framework outlines how complex sequences of movements may advance emotion research. Traditionally, often emotional-‘action’-based stimuli are used in such research, like hand-waving or walking motions. Here instead, a pilot dataset is provided with short dance choreographies, repeated several times by a dancer who expressed different emotional intentions at each repetition: anger, contentment, fear, joy, neutrality, and sadness. The dataset was simultaneously filmed professionally, and recorded using XSENS® motion capture technology (17 sensors, 240 frames/second). Thirty-two statistics from 12 kinematic features were extracted offline, for the first time in one single dataset: speed, acceleration, angular speed, angular acceleration, limb contraction, distance to center of mass, quantity of motion, dimensionless jerk (integral), head angle (with regards to vertical axis and to back), and space (convex hull 2D and 3D). Average, median absolute deviation (MAD), and maximum value were computed as applicable. The EMOKINE software is appliable to other motion-capture systems and is openly available on the Zenodo Repository. Releases on GitHub include: (i) the code to extract the 32 statistics, (ii) a rigging plugin for Python for MVNX file-conversion to Blender format (MVNX=output file XSENS® system), and (iii) a Python-script-powered custom software to assist with blurring faces; latter two under GPLv3 licenses.

Random walk with Brownian motion: Theory and application

While physical models are classical, random walks are statistical models. Historically, Karl Pearson was the first to propose the random walk. This study will focus on the essential of the random walk, and additionally, this essay will offer some application guidelines and explain when the random walk is applicable. The random walk means that the performance of an event is based on the past, as people cannot predict the future development steps and directions. The random walk, which dates to the 16th century, is a significant area of probability with many applications. The random walk and Brownian motion are the main topics of this paper. The four applications of random walks that are highlighted in this paper are the following. They include the multi-particle random walk immune algorithm, the density peak clustering algorithm, a review of random walk-based community discovery techniques, and interactive image segmentation that combines random walk and random forest. This work paves the way to unravel the importance of random walk.

Real-time motion-enabling positron emission tomography of the brain of upright ambulatory humans

BackgroundMobile upright PET devices have the potential to enable previously impossible neuroimaging studies. Currently available options are imagers with deep brain coverage that severely limit head/body movements or imagers with upright/motion enabling properties that are limited to only covering the brain surface.MethodsIn this study, we test the feasibility of an upright, motion-compatible brain imager, our Ambulatory Motion-enabling Positron Emission Tomography (AMPET) helmet prototype, for use as a neuroscience tool by replicating a variant of a published PET/fMRI study of the neurocorrelates of human walking. We validate our AMPET prototype by conducting a walking movement paradigm to determine motion tolerance and assess for appropriate task related activity in motor-related brain regions. Human participants (n = 11 patients) performed a walking-in-place task with simultaneous AMPET imaging, receiving a bolus delivery of F18-Fluorodeoxyglucose.ResultsHere we validate three pre-determined measure criteria, including brain alignment motion artifact of less than <2 mm and functional neuroimaging outcomes consistent with existing walking movement literature.ConclusionsThe study extends the potential and utility for use of mobile, upright, and motion-tolerant neuroimaging devices in real-world, ecologically-valid paradigms. Our approach accounts for the real-world logistics of an actual human participant study and can be used to inform experimental physicists, engineers and imaging instrumentation developers undertaking similar future studies. The technical advances described herein help set new priorities for facilitating future neuroimaging devices and research of the human brain in health and disease.

Investigating the impact of initial parameters on autonomous robot swarm movement strategies for enhanced exploration efficiency: a comprehensive study

Swarm robots are frequently preferred for the exploration of harsh environments and search and rescue operations. This study explores the factors that influence the movement strategies of autonomous robot swarms and their impact on swarm distribution in the field, employing simulation-based analysis. The research consists of two parts: initially, robots undergo free-fall as passive entities, followed by a phase where they employ predefined movement strategies from their fall positions. The study aims to investigate how the initial position and related parameters affect movement characteristics and the ultimate swarm distribution. To achieve this objective, four parameters-radius, height, mass, and the Coefficient of Restitution-were identified, each assigned three different values. The study observes the effects of these parameters on robot motion, considering motion strategies such as Random Walk, Levy Walk, Markov Process, and Brownian Motion. Results indicate that increasing parameter values induce changes in the position values of the free-falling swarm in the first part, which is the initial position for the second part, influencing movement strategies in diverse ways. The outcomes are analyzed concerning the radial and angular spread of the robots. Radial spread measures how far swarm elements spread from their initial positions, while angular spread indicates how homogeneously the robots are distributed according to the polar angle. The study comprehensively investigates how the movement strategies of autonomous robot swarms are impacted by parameters and how these effects manifest in the results. The findings are anticipated to enhance the effective utilization of autonomous robot swarms in exploration missions.

Investigation of Bio-materials Used in Knee İmplant

Knee joint is formed one of the most serious parts of his body for human beings throughout history. An increase in young people, traffic accidents, walking, running, and other similar movements have led to increased proliferation of the knee orthopedic disorders. Rapid growth and diversification of knee diseases as well an accurate recognition of this region has created a need for treatment. Human knee in case of encountered pain, functional limitation and wear, in total knee arthroplasty due to knee replacement surgery performed on the initiative of medically normal and comfortable walking recovered. In our country is like all over the world, degradation of the knee joint against deficiencies that adversely affect the daily life applications are made with increasing frequency. With the advancement of technological development, researches on this area have been continuing today as a result of producing implants more suitable for bone structure and connective tissue.

Detection of Dangerous Human Behavior by Using Optical Flow and Hybrid Deep Learning

Dangerous human behavior in the driving sense may cause traffic accidents and even cause economic losses and casualties. Accurate identification of dangerous human behavior can prevent potential risks. To solve the problem of difficulty retaining the temporal characteristics of the existing data, this paper proposes a human behavior recognition model based on utilized optical flow and hybrid deep learning model-based 3D CNN-LSTM in stacked autoencoder and uses the abnormal behavior of humans in real traffic scenes to verify the proposed model. This model was tested using HMDB51 datasets and JAAD dataset and compared with the recent related works. For a quantitative test, the HMDB51 dataset was used to train and test models for human behavior. Experimental results show that the proposed model achieved good accuracy of about 86.86%, which outperforms recent works. For qualitative analysis, we depend on the initial annotations of walking movements in the JAAD dataset to streamline the annotating process to identify transitions, where we take into consideration flow direction, if it is cross-vehicle motion (to be dangerous) or if it is parallel to vehicle motion (to be of no danger). The results show that the model can effectively identify dangerous behaviors of humans and then test on the moving vehicle scene.