Walking Speed Variability Research Articles

Towards a Unified Approach for Continuously-Variable Impedance Control of Powered Prosthetic Legs over Walking Speeds and Inclines.

Research in powered prosthesis control has explored the use of impedance-based control algorithms due to their biomimetic capabilities and intuitive structure. Modern impedance controllers feature parameters that smoothly vary over gait phase and task according to a data-driven model. However, these recent efforts only use continuous impedance control during stance and instead utilize discrete transition logic to switch to kinematic control during swing, necessitating two separate models for the different parts of the stride. In contrast, this paper presents a controller that uses smooth impedance parameter trajectories throughout the gait, unifying the stance and swing periods under a single, continuous model. Furthermore, this paper proposes a basis model to represent intertask relationships in the impedance parameters-a strategy that has previously been shown to improve model accuracy over classic linear interpolation methods. In the proposed controller, a weighted sum of Fourier series is used to model the impedance parameters of each joint as continuous functions of gait cycle progression and task. Fourier series coefficients are determined via convex optimization such that the controller best reproduces the joint torques and kinematics in a reference able-bodied dataset. Experiments with a powered knee-ankle prosthesis show that this simpler, unified model produces competitive results when compared to a more complex hybrid impedance-kinematic model over varying walking speeds and inclines.

Assessing a GPS-Based 6-Minute Walk Test for People With Persistent Pain: Validation Study.

The 6-minute walk test (6MWT) is a commonly used method to assess the exercise capacity of people with many health conditions, including persistent pain. However, it is conventionally performed with in-person supervision in a hospital or clinic, therefore requiring staff resources. It may also be difficult when in-person supervision is unavailable, such as during the COVID-19 pandemic, or when the person is geographically remote. A potential solution to these issues could be to use GPS to measure walking distance. The primary aim of this study was to assess the validity of a GPS-based smartphone app to measure walking distance as an alternative to the conventional 6MWT in a population with persistent pain. The secondary aim of this study was to estimate the difference between the pain evoked by the 2 test methods. People with persistent pain (N=36) were recruited to complete a conventional 6MWT on a 30-m shuttle track and a 6MWT assessed by a smartphone app using GPS, performed on outdoor walking circuits. Tests were performed in random order, separated by a 15-minute rest. The 95% limits of agreement were calculated using the Bland-Altman method, with a specified maximum allowable difference of 100 m. Pain was assessed using an 11-point numerical rating scale before and after each walk test. The mean 6-minute walk distance measured by the GPS-based smartphone app was 13.2 (SD 46; 95% CI -2.7 to 29.1) m higher than that assessed in the conventional manner. The 95% limits of agreement were 103.9 (95% CI 87.4-134.1) m and -77.6 (95% CI -107.7 to -61) m, which exceeded the maximum allowable difference. Pain increased in the conventional walk test by 1.1 (SD 1.0) points, whereas pain increased in the app test by 0.8 (SD 1.4) points. In individuals with persistent pain, the 2 methods of assessing the 6MWT may not be interchangeable due to limited validity. Potential reasons for the differences between the 2 methods might be attributed to the variation in track layout (shuttle track vs continuous circuit); poor GPS accuracy; deviations from the 30-m shuttle track; human variability in walking speed; and the potential impact of a first test on the second test due to fatigue, pain provocation, or a learning effect. Future research is needed to improve the accuracy of the GPS-based approach. Despite its limitations, the GPS-based 6MWT may still have value as a tool for remote monitoring that could allow individuals with persistent pain to self-administer frequent assessments of their functional capacity in their home environment.

A COMPARATIVE ANALYSIS OF LOWER LIMB EXOSKELETON GAIT CONTROL SYSTEMS

The complexities associated with lower limb exoskeleton control systems stem from the dynamic and non-linear nature of human movement. Gait control, in particular, re-quires intricate coordination of joints and muscles, demanding precise adjustments to ac-commodate variations in walking speed, terrain, and user preferences. Additionally, the diverse physical characteristics among users, such as weight, height, and muscle develop-ment, introduce parametric uncertainties that amplify the challenge of designing control systems that can adapt seamlessly to individual differences. Addressing these complexities involves an advanced and multi-level control approach. In the pursuit of effective control strategies, a non-linear mathematical model for a single-leg lower limb exoskeleton is de-rived, considering mechanical and electromechanical components. Тhe gait control sys-tems of a lower limb exoskeleton were investigated, emphasizing the development of vari-ous control strategies, including proportional derivative (PD), PD with gain scheduling, hy-brid adaptive, and adaptive controllers with gain scheduling. The article ends with a com-prehensive comparative analysis of the developed controllers, presenting the integral abso-lute errors (IAE) during exoskeleton walking in both load and no-load conditions to evalu-ate their productivity, efficiency, and adaptability. Extensive testing was conducted using a physical test model representing the exoskeleton of the lower limbs. During a series of ex-periments, the functionality and operability of each control system were evaluated in vari-ous scenarios. The results of these tests give valuable information about the strengths and weaknesses of various control strategies, contributing to the development of lower limb exoskeleton technology.

The role of ankle and knee muscle characteristics in spatiotemporal gait parameters at different walking speeds: A cross-sectional study

BackgroundUnderstanding the intricate interplay between ankle and knee muscle characteristics and their impact on gait parameters is crucial for enhancing our comprehension of human locomotion, particularly in the context of varying walking speeds among healthy young adults. Research question: The study aimed to identify the relative importance of ankle and knee flexor and extensor muscle characteristics (e.g., strength estimated by peak torque [PT] and rate of torque development [RTD]) in the spatiotemporal gait parameters and variability in self-selected (SSWS) and fast walking speeds (FWS) in healthy young adults. MethodsOne hundred and thirty-nine adults (75 men – 54% and 64 women – 46%; 29.04 ± 9.55 years) were assessed about their muscle characteristics (PT and RTD by an isokinetic dynamometer) and spatiotemporal gait parameters at different walking speeds (SSWS and FWS by an instrumented walkway). ResultsData analysis indicated a weak relationship between the PT and RTD of the ankle and knee and spatiotemporal gait parameters and variability in both walking conditions (SSWS: R2 0.14–0.05; FWS: R2 0.40–0.05). The strength of the knee muscles was more relevant when walking at a self-selected speed, while the strength of the ankle muscles played a more prominent role when walking at a fast pace. SignificanceThe findings underscore the critical role of ankle muscles (plantar and dorsiflexors) at fast walking speeds. Therefore, targeted interventions for strength and optimization of these muscles are paramount.

The interaction between muscle pathophysiology, body mass, walking speed and ankle foot orthosis stiffness on walking energy cost: a predictive simulation study

BackgroundThe stiffness of a dorsal leaf AFO that minimizes walking energy cost in people with plantarflexor weakness varies between individuals. Using predictive simulations, we studied the effects of plantarflexor weakness, passive plantarflexor stiffness, body mass, and walking speed on the optimal AFO stiffness for energy cost reduction.MethodsWe employed a planar, nine degrees-of-freedom musculoskeletal model, in which for validation maximal strength of the plantar flexors was reduced by 80%. Walking simulations, driven by minimizing a comprehensive cost function of which energy cost was the main contributor, were generated using a reflex-based controller. Simulations of walking without and with an AFO with stiffnesses between 0.9 and 8.7 Nm/degree were generated. After validation against experimental data of 11 people with plantarflexor weakness using the Root-mean-square error (RMSE), we systematically changed plantarflexor weakness (range 40–90% weakness), passive plantarflexor stiffness (range: 20–200% of normal), body mass (+ 30%) and walking speed (range: 0.8–1.2 m/s) in our baseline model to evaluate their effect on the optimal AFO stiffness for energy cost minimization.ResultsOur simulations had a RMSE < 2 for all lower limb joint kinetics and kinematics except the knee and hip power for walking without AFO. When systematically varying model parameters, more severe plantarflexor weakness, lower passive plantarflexor stiffness, higher body mass and walking speed increased the optimal AFO stiffness for energy cost minimization, with the largest effects for severity of plantarflexor weakness.ConclusionsOur forward simulations demonstrate that in individuals with bilateral plantarflexor the necessary AFO stiffness for walking energy cost minimization is largely affected by severity of plantarflexor weakness, while variation in walking speed, passive muscle stiffness and body mass influence the optimal stiffness to a lesser extent. That gait deviations without AFO are overestimated may have exaggerated the required support of the AFO to minimize walking energy cost. Future research should focus on improving predictive simulations in order to implement personalized predictions in usual care.Trial Registration Nederlands Trial Register 5170. Registration date: May 7th 2015. http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=5170

Biomechanical modeling and experiments of energy harvesting backpacks

The energy harvesting backpack is a promising solution for powering wearable electronic devices by converting biomechanical energy from human motion into electricity. However, the majority of current energy harvesting backpack models primarily focus on harvester dynamics while neglecting the crucial role of humans as an energy source in the system. Some studies have utilized inverted pendulum models through active control torque and optimization-based methods to investigate human factors in bioenergy harvesting systems, but this is complex for the practical application of biomechanical models. To overcome these issues, a new biomechanical model of the energy harvesting backpack is proposed in this paper. The contribution of this study is not only modeling the cooperative dynamics between backpacks and humans which is easily solvable and comprehensible but also defining multi-indicators for model verification and backpack evaluation. The biomechanical model without load is established based on the D' Alembert principle and empirical gait phase division of 3:1 when it is assumed that the gait parameters remain constant regardless of the load being carried. Four sets of data from two distinct databases are utilized to analyze the dynamics of the bipedal walking model in both vertical and horizontal directions. The biomechanical model with energy harvesting backpacks is proposed to define four single direct indicators and two global indirect indicators for demonstrating the proposed model’s ability to predict the characteristics of backpacks. Finally, the foot-reaction force, the vibration of the center of mass, and the output power of three subjects are acquired in five experiments (i) with no backpack under variable walking speed; (ii) an ordinary backpack under variable walking speed; (iii) an ordinary backpack under variable loads; (iv) an energy harvesting backpack under variable walking speed; and (v) an energy harvesting backpack under variable loads. The comparison between the proposed model and experimental results demonstrates that the proposed model is easy to solve and comprehend, and can effectively capture the characteristics of both human and energy harvesting backpacks.

Evaluation of Pedestrians Walking Speeds in Baghdad City

This research examines the factors which influence pedestrian's walking speed in Baghdad. the variations in walking speed of pedestrians are related to pedestrian characteristics such as gender, age group, and clothing traditions. Using the established methodology, the counts of pedestrians were performed using manual and video counting. The case study was performed in two streets located in a highly crowded commercial zone at the city center of Baghdad: Al-Karada Dakhel and Al- Sina’a Street. Data were subjected to statistical analysis using IBM SPSS Statistics 19 software. It has been found that Iraqi pedestrians walk slower than other pedestrians in the developed countries or in the region with minimum walking speed of 29.85 m/min. Age, gender, and clothing traditions were found to significantly contribute to pedestrian speed. Pedestrians in the age range from 18-50 years old were the fastest group of pedestrians and pedestrians over 50 years old were the slowest. Male pedestrians had significantly faster walking speeds than female pedestrians did. Pedestrians wearing western style were found to be faster than those wearing Arabic style.

Experimental study on the effects of exercise intensity and thermal environment on thermal responses

Public exercise facilities have recently emerged across China. This study aims to investigate the impacts of exercise intensity and thermal environment on thermal responses as well as examine the dynamic changes and differences in thermal responses during exercise. Using a controlled thermal chamber, university students were assessed at varying walking speeds under six thermal conditions, from −15 to 35 °C. Their psychological and physiological responses were observed and recorded in real time during the thermal adaption, exercise, and rest phase. Firstly, the exercise intensity significantly affected heart rate and metabolic rate, whereas the thermal environment had no significant effect. The effect of the thermal environment on skin temperature, thermal sensation vote (TSV), and thermal comfort vote (TCV) was more pronounced than that of exercise intensity. Secondly, during exercise, the metabolic rate took 6–9 min to stabilise, and the psychological response lagged compared to the physiological response. Thermal sensation was closest to neutral at 15 °C, 5 °C, and −5 °C when walking at 3 km/h, 4.50 km/h, and 6 km/h, respectively. Thermal comfort vote (TCV) levels were greater for higher exercise intensities at −15 to 5 °C, lower for higher exercise intensities at 25–35 °C, and similar for varied exercise intensities at 15 °C. Finally, current thermal comfort guidelines underestimate the exercise metabolic rate by at least 9.7%. This study helps clarify the dynamic changes and influence factors of thermal responses during exercise and provides a reference for future research on exercisers’ thermal responses.

Gait Phase Classification of Lower Limb Exoskeleton Based on a Compound Network Model

The classification of lower limb gait phase is very important for the control of exoskeleton robots. In order to enable the exoskeleton to determine gait phase and provide appropriate assistance to the wearer, we propose a compound network based on CNN-BiLSTM. The method uses data from inertial measurement units placed on the leg and pressure sensor arrays placed on the sole as inputs to the model. The convolutional neural network (CNN) is used to obtain the local key features of gait data, and then the bidirectional long short-term memory (BiLSTM) network is used to extract the serialized gait phase information from the local key features to obtain the high-level feature expression. Finally, the seven phases of both feet were obtained through the classification of the softmax layer. We designed a gait acquisition system and collected the gait data from seven subjects at varying walking speeds. In the test set, the highest gait phase classification accuracy can reach 95.09%. We compared the proposed model with the long short-term memory (LSTM) network and gated recurrent unit (GRU) network. The experimental results show that the average accuracy of CNN-BiLSTM network from seven subjects is 0.417% higher than that of the LSTM network and 0.596% higher than that of the GRU network. Therefore, the ability of the CNN-BiLSTM network to classify gait phases can be applied in designing exoskeleton controllers that can better assist for different gait phases correctly to assist the wearer to walk.

COMPLEXITY-BASED ANALYSIS OF MUSCLE ACTIVATION DURING WALKING AT DIFFERENT SPEEDS

In this research, we investigated the effect of changes in walking speed on variations of the complexity of electromyogram (EMG) signals recorded from the right and left legs of subjects. We specifically employed fractal theory and approximate entropy to analyze the changes in the complexity of EMG signals recorded from 13 subjects walked at 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, and 4.0 km/h on a flat surface. The results showed that by increasing of walking speed, the complexity of EMG signals decreases. The statistical analysis also indicated the significant effect of variations in walking speed on the variations of the complexity of EMG signals. This method analysis can be applied to other physiological signals of humans (e.g. electroencephalogram (EEG) signals) to investigate the effect of walking speed on other organs’ activations (e.g. brain).

Investigating walking speed variability of young adults in the real world

Background:Walking speed strongly correlates with health outcomes, making accurate assessment essential for clinical evaluations. However, assessments tend to be conducted over short distances, often in a laboratory or clinical setting, and may not capture natural walking behavior. To address this gap, the following questions are investigated in this work: Is walking speed significantly influenced by the continuity and duration of a walking bout? Can preferred walking speed be inferred by grouping walking bouts using duration and continuity? Methods:We collected two weeks of continuous data from fifteen healthy young adults using a thigh-worn accelerometer and a heart rate monitor. Walking strides were identified and grouped into walking periods. We quantified the duration and the continuity of each walking period. Continuity is used to parameterize changes in stepping rate related to pauses during a bout of walking. Finally, we analyzed the influence of duration and continuity on estimates of stride speed, and examined how the distribution of walking speed varies depending on different walking modes (defined by duration and continuity). Results:We found that continuity and duration can be used to explain some of the variability in real-world walking speed (p<0.001). Speeds estimated from long continuous walks with many strides (42% of all recorded strides) had the lowest standard deviation. Walking speed during these bouts was 1.41ms−1 (SD = 0.26ms−1). Significance:Walking behavior in the real world is largely variable. Features of real-world walks, like duration and continuity, can be used to explain some of the variability observed in walking speed. As such, we recommend using long continuous walks to confidently isolate the preferred walking behavior of an individual.

Intra-class variations with deep learning-based gait analysis: A comprehensive survey of covariates and methods

Gait recognition is an essential biometric technique that recognizes humans at a distance through their unique walking style. In the present era of deep learning, automated gait analysis has become easier with an increase in processing power. However, the recognition accuracy is affected by many covariates such as clothing conditions, carrying objects, varying viewing angles, occlusion, walking speed variations, and thus, it remains a challenging problem. For this complex problem, huge datasets are required to train for given conditions and predict new situations; thus, deep learning is preferred. In this review paper, we categorize various gait covariates which have been recently handled. There are various approaches, but the most effective approach is deep learning; hence in this paper, we include the most used deep learning approaches for each covariate condition found in the literature. Further, we highlight open problem areas handling these covariates and offer some suggestions about their better handling. Based on the review and our understanding of all the gait pipelines employed in deep learning, we have suggested a comprehensive and universal deep learning pipeline that can handle most gait covariates rather than customized deep learning pipelines. The methods of handling gait covariates are summarized according to appearance, pose, and sensors. A comprehensive comparison of reviewed approaches for real-time scenarios in terms of their novelty, benefits, and limitations is then offered, which led us to identify open research problems related to gait covariates. In the end, the paper concludes with the challenges identified and prospects.

Analysis of lower limb prosthetic socket interface based on stress and motion measurements.

The study was designed to establish a biomechanical assessment platform for the lower limb residuum/socket interface as a function of duration and speed of movement. The approach exploits an interface sensor which measures multi-directional stresses at the interface. The corresponding interface coupling motion was assessed using a 3D motion capture system. A longitudinal study, involving a trans-femoral amputee, was conducted with nine repeated level walking sessions over a 12-month period. The effect of walking speed on interface biomechanics was also assessed. Interface peak pressures and shear stresses in the range of 55–59 kPa and 12–19 kPa were measured, respectively, over all sessions in the 12 months study period at the posterior-proximal location of the residuum. The peak pressure and longitudinal shear values were found to fluctuate approximately 11% and 40% as against its maximum value, respectively, over 12 months. In addition, up to 12° of angular coupling and up to 28 mm of pistoning were recorded over a gait cycle, which was found to change by 29% and 45% respectively over the study period. The variation in walking speed, by altering self-selected cadence, resulted in changes of pressure and shear stresses at mid-stance of the gait cycle. In particular, as compared with self-selected cadence, for fast speed, peak pressure and peak longitudinal shear stress decreased by 5% and 33%, respectively. For slow speed, peak pressure and peak longitudinal shear stress increased by 7% and 17%, respectively. The corresponding angular and pistoning revealed a variation of up to 29% and 45%, respectively. This biomechanical assessment approach shows promise in the quantitative assessment of interface kinematics and kinetics for lower limb prosthetics, the usage of which could assist the clinical assessment of prosthetic socket fit.

A Piecewise Monotonic Smooth Phase Variable for Speed-Adaptation Control of Powered Knee-Ankle Prostheses

Researchers are currently making progress in unifying the entire gait cycle of powered prostheses by using a human-inspired phase variable, but constructing a robust phase variable to more accurately estimate the gait phase and desired joint trajectories of prostheses during varying walking speeds remains an open problem. Firstly, this study proposed a piecewise monotonic and smooth phase variable to predict the gait phase from only the measured thigh angle. Compared to the two most widely used methods, thigh angle integral vs. thigh angle and thigh angular rate vs. thigh angle, the proposed method addressed the drift problem of the integral-based method and the noise problem of the angular rate-based. Then a predictive tuning-free model that represents gait kinematics as a continuous function of proposed phase variable and walking speed was constructed to generate desired joint virtual constraints for speed-adaptation control of powered knee-ankle prosthesis during continuously varying walking speeds. Experiments were conducted with six able-bodied participants wearing inertial measurement units and performing periodic and non-periodic (quick start and stop) tasks, and one transfemoral amputee wearing a powered knee-ankle prosthesis walking on a treadmill. Experimental results illustrated that the root mean squared error of the gait phase estimated by the proposed approach was 75% lower than the integral-based and angular rate-based methods, and the proposed approach performed well in the continuous phase control of a powered knee-ankle prosthesis during varying walking speeds.

A Wearable Mixed Reality Platform to Augment Overground Walking: A Feasibility Study.

Humans routinely modify their walking speed to adapt to functional goals and physical demands. However, damage to the central nervous system (CNS) often results in abnormal modulation of walking speed and increased risk of falls. There is considerable interest in treatment modalities that can provide safe and salient training opportunities, feedback about walking performance, and that may augment less reliable sensory feedback within the CNS after injury or disease. Fully immersive virtual reality technologies show benefits in boosting training-related gains in walking performance; however, they lack views of the real world that may limit functional carryover. Augmented reality and mixed reality head-mount displays (MR-HMD) provide partially immersive environments to extend the virtual reality benefits of interacting with virtual objects but within an unobstructed view of the real world. Despite this potential advantage, the feasibility of using MR-HMD visual feedback to promote goal-directed changes in overground walking speed remains unclear. Thus, we developed and evaluated a novel mixed reality application using the Microsoft HoloLens MR-HMD that provided real-time walking speed targets and augmented visual feedback during overground walking. We tested the application in a group of adults not living with disability and examined if they could use the targets and visual feedback to walk at 85%, 100%, and 115% of each individual’s self-selected speed. We examined whether individuals were able to meet each target gait speed and explored differences in accuracy across repeated trials and at the different speeds. Additionally, given the importance of task-specificity to therapeutic interventions, we examined if walking speed adjustment strategies were consistent with those observed during usual overground walking, and if walking with the MR-HMD resulted in increased variability in gait parameters. Overall, participants matched their overground walking speed to the target speed of the MR-HMD visual feedback conditions (all p-values > 0.05). The percent inaccuracy was approximately 5% across all speed matching conditions and remained consistent across walking trials after the first overall walking trial. Walking with the MR-HMD did not result in more variability in walking speed, however, we observed more variability in stride length and time when walking with feedback from the MR-HMD compared to walking without feedback. The findings offer support for mixed reality-based visual feedback as a method to provoke goal-specific changes in overground walking behavior. Further studies are necessary to determine the clinical safety and efficacy of this MR-HMD technology to provide extrinsic sensory feedback in combination with traditional treatments in rehabilitation.

Adaptive Pedestrian Stride Estimation for Localization: From Multi-Gait Perspective.

Accurate and reliable stride length estimation modules play a significant role in Pedestrian Dead Reckoning (PDR) systems, but the accuracy of stride length calculation suffers from individual differences. This paper presents a stride length prediction strategy for PDR systems that can be adapted across individuals and broad walking velocity fields. It consists of a multi-gait division algorithm, which can divide a full stride into push-off, swing, heel-strike, and stance based on multi-axis IMU data. Additionally, based on the acquired gait phases, the correlation between multiple features of distinct gait phases and the stride length is analyzed, and multi regression models are merged to output the stride length value. In experimental tests, the gait segmentation algorithm provided gait phases division with the F-score of 0.811, 0.748, 0.805, and 0.819 for stance, push-off, swing, heel-strike, respectively, and IoU of 0.482, 0.69, 0.509 for push-off, swing, heel-strike, respectively. The root means square error (RMSE) of our proposed stride length estimation was 151.933, and the relative error for total distance in varying walking speed tests was less than 2%. The experimental results validated that our proposed gait phase segmentation algorithm can accurately recognize gait phases for individuals with wide walking speed ranges. With no need for parameter modification, the stride length method based on the fusion of multiple predictions from different gait phases can provide better accuracy than the estimations based on the full stride.

Online Adaptive PID Control for a Multi-Joint Lower Extremity Exoskeleton System Using Improved Particle Swarm Optimization

Robotic exoskeletons have great potential in the medical rehabilitation and augmentation of human performance in a variety of tasks. Proposing effective and adaptive control strategies is one of the most challenging issues for exoskeleton systems to work interactively with the user in dynamic environments and variable tasks. This research, therefore, aims to advance the state of the art of the exoskeleton adaptive control by integrating the excellent search capability of metaheuristic algorithms with the PID feedback mechanism. Specifically, this paper proposes an online adaptive PID controller for a multi-joint lower extremity exoskeleton system by making use of the particle swarm optimization (PSO) algorithm. Significant improvements, including a ‘leaving and re-searching mechanism’, are introduced into the PSO algorithm for better and faster update of the solution and to prevent premature convergence. In this research, a 9-DOF lower extremity exoskeleton with seven controllable joints is adopted as a test-bench, whose first-principle dynamic model is developed, which includes as many uncertain factors as possible for generality, including human–exoskeleton interactions, environmental forces, and joint unilateral constraint forces. Based upon this, to illustrate the effectiveness of the proposed controller, the human–exoskeleton coupled system is simulated in four characteristic scenarios, in which the following factors are considered: exoskeleton parameter perturbations, human effects, walking terrain switches, and walking speed variations. The results indicate that the proposed controller is superior to the standard PSO algorithm and the conventional PID controller in achieving rapid convergence, suppressing the undesired chattering of PID gains, adaptively adjusting PID coefficients when internal or external disturbances are encountered, and improving tracking accuracy in both position and velocity. We also demonstrate that the proposed controller could be used to switch the working mode of the exoskeleton for either performance or an energy-saving consideration. Overall, aiming at a multi-joint lower extremity exoskeleton system, this research proposes a PSO-based online adaptive PID controller that can be easily implemented in applications. Through rich and practical case studies, the excellent anti-interference capability and environment/task adaptivity of the controller are exemplified.

Gait changes among older adults during a virtual wayfinding task: The role of spatial disorientation and heart rate variability

AbstractBackgroundAging has been associated with decline in cognitive and motor performance, often expressed in dual‐task walking situations, such as wayfinding. A major barrier to successful wayfinding is spatial disorientation, occurring mostly at intersections. Although gait changes have been observed in various walking conditions, little is known about the effect of spatial disorientation on gait in wayfinding among older adults.Method24 participants (16 female) were measured in 3 conditions: 6 young controls (mean age = 23.50 years, SD = 0.54), 9 old controls (mean age = 68.77 years, SD = 2.72; mean MMSE = 28.85, SD = 0.89) and 8 old experimental (mean age = 71.87 years, SD = 5.38; mean MMSE = 29.14, SD = 0.80). Participants performed a wayfinding task consisting of about 14 decision points (i.e. intersections) within a virtual environment (VE), on a self‐paced treadmill equipped with a marker‐based optical motion‐capture system. The VE was held constant for the controls, and manipulated (e.g. moved landmarks) for the experimental participants. Heart rate was continuously monitored as a measure of psychophysiological response. Statistical analyses using Linear Mixed Effects Models and t‐tests, focused on hypothesis‐driven outcome measures extracted from decision points.ResultInitial evaluation of the outcome measures show considerable effects of the disorientation intervention on gait; both the old control (constant VE) and experimental (manipulated VE) participants showed some variation in walking speed during instances of disorientation. This effect was, however, more pronounced, t(39) = ‐3.66, p&lt;.001, for the old experimental condition participants (Figure 1).ConclusionUnderstanding underlying motion and psychophysiological signatures of spatial disorientation among older adults is a necessary first step, towards developing efficient situation‐aware assistive devices necessary for independent mobility. Initial findings show increased gait variability during instances of disorientation. The association of the disorientation effect with psychophysiological response is also being investigated. Further results will be presented by July 2021.

Predictive Thin Plate Spline Model for Estimation of Load Carriage at Varying Gradient and Speed

Laboratory-based experimental studies are being carried out for Indian soldiers to estimate optimal load carriage at different gradients and walking speeds. These experiments involve the recording of Cardio-respiratory responses such as Heart Rate (HR), Oxygen Consumption (VO2), Energy Expenditure (EE), Respiratory Frequency (RF), Minute Ventilation (VE), and Maximal Aerobic capacity (%VO2max). Due to limitations in the data sample size that can be obtained in laboratory-based experiments, there is a need for mathematical interpolation to obtain intermediate values in the study. Load carriage can be affected by factors that can be controlled, such as the speed of marching, and also by external factors that cannot be controlled like ambient temperature. Real-time interactions of all the factors also have an impact on the load-carrying capacity. Planning of the mission operations requires the specification of well-defined work-rest schedules and indication of total load limits, to ensure the operational effectiveness of the military personnel. In this paper, we present a Predictive 3-Dimensional Thin Plate Spline Model for efficient estimation of load. We developed a Multiple Linear Regression Model for predicting %VO2max for combinations of load and gradient. The accuracy of the model was 85 per cent and the maximum permissible loads were derived from the prediction model for the physiological limits of 50 per cent, 60 per cent, and 75 per cent of VO2max. A Thin Plate Spline based interpolation technique was used on this Multiple Linear Regression Model to generate optimal load at intermediate values for the experimental study. A similar predictive Interpolation Model was also developed for estimating load for varying walking speeds at level ground.

INVESTIGATION OF THE EFFECT OF WALKING SPEED ON LEG MUSCLE REACTION BY COMPLEXITY-BASED ANALYSIS OF ELECTROMYOGRAM (EMG) SIGNALS

An important research area in physiological and sport sciences is the analysis of the variations of the muscle reaction due to changes in walking speed. In this paper, we investigated the effect of walking speed variations on leg muscle reaction by the analysis of Electromyogram (EMG) signals at different walking inclines. For this purpose, we benefited from fractal theory and sample entropy to analyze how the complexity of EMG signals changes at different walking speeds. According to the results, although fractal theory could not show a clear trend between the variations of the complexity of EMG signals and the variations of the walking speed, however, based on the results, increasing the speed of walking in the case of different inclines is mapped on to the decrement of the sample entropy of EMG signals. Therefore, sample entropy could decode the effect of walking speed on the reaction of leg muscle. This analysis method could be applied to analyze the variations of other physiological signals of humans durin walking.