Short Step Research Articles

Examination of the prediction of the planar piecewise continuous lumped muscle parameter model for walking gait with ankle-foot orthosis

GoalThis work examines the use of a previously described piecewise continuous lumped muscle parameter (PPCLMP) model for predicting selected gait parameters for walking without and with ankle-foot orthoses (AFOs) of varying stiffnesses. MethodsTwo AFOs with low (3.4 Nm/deg) and high (6.9 Nm/deg) stiffnesses were tested on the left leg of six healthy subjects to examine the model prediction on the influence of different AFO stiffnesses on gait. ResultsThe model prediction errors ranged from 0 % to 70 % for step lengths with root mean square error (RMSE) of 0.15 m and ranged from 0 % to 67 % for swing time with RMSE of 0.07 s. The prediction precision of step length was more consistent among subjects than of swing time. Discussions and conclusionsThe model predicts the observed shortened step lengths and swing times, but there were significant differences between predicted and observed swing times and step lengths. The causes of these differences might be differences in the lumped muscle parameters taken from the literature and those of the subjects tested. Also, the model assumption that muscle stiffness is proportional to joint angle may not be corrected.

Development of rapid on-site detection of Salmonella Enteritidis, S. Typhimurium, and S. Thompson in food samples using an ultrafast PCR system

The conventional Salmonella serotyping methods are labor intensive and time-consuming; therefore, rapid identification method is needed to detect outbreaks early and prevent their spread. In this study, we developed for the first time a detection method for the rapid and accurate on-site serotyping of Salmonella enterica subsp. enterica serovar Enteritidis, Thompson, and Typhimurium, all of which have epidemiological importance worldwide. This method, which is based on specific marker genes, was constructed with portable equipment and a simple DNA extraction step. For the specific detection of the three serovars, genetic markers obtained via a bioinformatics analysis were selected and showed high specificity for 97 strains. The ultrafast polymerase chain reaction (PCR) showed high linearity (≥0.997) in both pure culture and a spiked food sample, with a detection specificity of 102 colony forming unit (CFU)/ml; these results are consistent with real-time PCR. Moreover, ultrafast PCR was further improved for developing direct ultrafast PCR method for field detection, the entire procedure of which could be completed within 25 min. The test results of an artificially contaminated food sample showed that the three serovars could be detected using the direct ultrafast PCR with a very short (3 h) enrichment step when inoculated at 1 CFU/g. This method exhibited various advantages, such as its ultrafast and labor-saving characteristics, which can be applied to the diagnosis of Salmonella serovar in the field.

A short-term prediction-based efficient optimization power control strategy for heavy-duty hybrid electric vehicle

This study proposes a short-term prediction-based power control strategy using the modified iteration sequential clustering quadratic programming (MISCQP) algorithm for heavy-duty series hybrid electric vehicles (SHEVs). In this strategy, a power preconditioning method is established on the basis of demand power prediction which guarantees the stable power output under transient high-power condition. Through the prediction sequence, MISCQP algorithm is proposed to solve receding horizon problem and achieve real-time control by improving the iteration efficiency. For this purpose, the clustering algorithm is designed to skip the unnecessary short step in the iteration which is too few to obtain sufficient descent. The iteration points in various iteration domains are clustered and the corresponding cluster centers are obtained. Next, the aforementioned clustering results are introduced to improve the termination criterion. The updated criterion turns to skip the short step when the distance of cluster centers of various iteration domains varies within the set threshold. Finally, the performance of the proposed strategy is validated both in simulation and hardware-in-loop tests. The results reveal that the proposed strategy achieves 5.00 %, 5.86 %, 6.27 % less fuel consumption while maintaining stable power output under all the driving cycles. And the average iteration number of proposed strategy is decreased by 10.36 %, 8.47 %, 9.21 %, respectively.

Numerical Simulation of Ordinary Differential Equation by Euler and Runge–Kutta Technique

In this paper, we explore Modified Euler’s technique & classical Runge Kutta technique of order 4th. These numerical techniques are employed to provide an approximate solution to an initial value problem with ordinary differential equations. These approaches are certainly effective and practically good for solving ordinary differential equations, & they are all used to evaluate degree of accuracy of each approach. We create a table of approximate solution and exact solution comparisons to acquire and assess the level of accuracy of numerical data. The exact and approximate solutions show good agreement, and we compare the computational effort required by the proposed methods. Additionally, we observed that numerical solutions with very short step sizes provide more accurate results. We now identify the errors in the suggested methods and graphically illustrate them to demonstrate their superiority over one another. The Runge-Kutta 4th order approach is more effective in terms of results and also produces less error.

Efficiency of EEG-Guided Adaptive Neurostimulation Increases with the Optimization of the Parameters of Preliminary Resonant Scanning

The development and improvement of closed-loop methods for non-invasive brain stimulation is an actual and rapidly developing area of neuroscience. An innovative version of this approach, in which a person is presented with audiovisual therapeutic stimulation, automatically modulated by the rhythmic components of his electroencephalogram (EEG), is EEG-guided adaptive neurostimulation. The present study aims to experimentally test the assumption that the effectiveness of EEG-guided adaptive neurostimulation can be increased by optimizing the parameters of preliminary resonance scanning, which consists of LED photostimulation with stepwise increasing frequency in the range of θ-, α-, and β EEG-rhythms. In order to test this assumption, we compared the effects of two types of resonance scanning, which differ in the step length of the gradually increasing frequency of LED photostimulation. The experiments involved two equal groups of university students in a state of exam stress. Before EEG-guided adaptive stimulation, one of the groups underwent resonance scanning with a short (3 s), and the other with a long (6 s) step of a gradual increase in the frequency of photostimulation. Changes in the EEG and psychophysiological parameters were analyzed under the influence of combined (resonance scanning plus EEG-guided adaptive neurostimulation) interventions relative to the initial level. It was found that only with a short (3 s) step of increasing the frequency of photostimulation, significant increases in the power of EEG-rhythms are observed, accompanied by significant changes in subjective indicators – a decrease in the number of errors in the word recognition test, a decrease in the level of emotional maladaptation, and an increase in well-being scores. The revealed positive effects are already observed after single therapeutic procedures due to the optimal conditions for the involvement of the resonant and integration mechanisms of the brain and the mechanisms of neuroplasticity in the processes of normalization of body functions. The developed combined approach to neurostimulation after additional experimental studies can be used in a wide range of rehabilitation procedures.

Reinforcement learning for traffic signal control: Incorporating a virtual mesoscopic model for depicting oversaturated traffic conditions

Recently, with increasing urban traffic congestion, there has been an upsurge in studies on reinforcement learning for traffic signal control (RL-TSC), which enables efficient traffic management. However, most existing RL-TSC research relies on simulation, and instances of real-world application are relatively scarce. Furthermore, existing RL-TSC methods employ a step-based approach, controlling traffic signals every short step. This approach can be inefficient in oversaturated traffic conditions, where large deviations in traffic demand between movements prevent traffic signals from being effectively managed based on the overall traffic situation. In this study, we aim to transform simulation-based RL-TSC into a more practical and applicable model. We have developed a RL-TSC method capable of responding to oversaturated traffic conditions, designing an action space that enables the agent to derive an optimal signal set for every cycle length, thereby understanding the traffic situation across all movements. During each cycle length, our proposed model conducts traffic signal optimization and identifies the optimal signal through an iterative exploration. To facilitate a fast and accurate strategy search process, we developed a kinematic wave-based mesoscopic model. This model estimates the density across the entire link, based on collected traffic data and derives the state and reward values. We have validated the field applicability of the proposed RL-TSC method through a real-world demonstration at a congested intersection in Seoul, Korea. The results indicated a significant improvement in traffic congestion, with the average queue length at the intersection reduced by up to 11.4%.

Bird movement patterns in an agricultural landscape are mediated by both habitat conditions and traits

AbstractAlterations of the landscape following agricultural expansion and intensification affect animal movement patterns in the resulting mosaic of fragments and surrounding matrix. Here, we analyze the observed movement patterns of 34 individuals from nine tropical bird species from a rapidly changing agricultural landscape in Kenya. We deconstructed the movement patterns into their three components: step length, turn angles, and displacement and categorized them into two states: area restrictive and expansive movement. Using hidden Markov models, mixed models, and species traits, we showed that movement of birds in the fragments comprised of short step lengths and small displacements, characteristic of area restrictive movement to exploit high‐quality habitats. On the contrary, movement in the matrix comprised of long step lengths and large displacements, characteristic of area expansive movement to explore or pass‐through poorer habitats. The responses of movement components to fragments and the surrounding matrix were mediated by species traits. Habitat specialists showed stronger boundary response, shorter step lengths, and smaller displacements than habitat generalists in both the fragments and the matrix. Their strong preferences for the fragments, coupled with low flight capabilities can make movement in the matrix particularly difficult. Whereas, at the landscape scale, habitat generalist omnivores and habitat specialist frugivores had larger step lengths than the other guilds, as they use the matrix for resources or as a conduit to movement. Therefore, the habitat fragments are intensely utilized and of conservation importance. The matrix quality and permeability can promote animal space use and movement.

Spray characteristics of a liquid fuel jet in a tandem backward-facing step cavity in supersonic flow

The influences of the backward-facing step and injection schemes on the spray characteristics of a liquid jet in a tandem backward-facing step cavity were investigated experimentally using high-speed photography and Schlieren techniques under a Mach 2.0 supersonic crossflow for the first time. The instantaneous structures of the spray and wave structures of the air-flow field were accurately captured by the high temporal resolution experimental technology. It is found that exist complex wave structures in the tandem backward-facing step cavity. A dimensionless spray factor was defined to describe the concentration of spray inside the cavity qualitatively. As a result, it is revealed that for the short backward-facing step cavity, the injection scheme near the upstream inlet has a higher penetration depth but a lower spray distribution, while the injection scheme near the cavity has a more spray distribution. For the long backward-facing step cavity, the injection scheme near the upstream inlet also has a higher penetration depth and the injection scheme near the sharp corner of the backward-facing step has a more sufficient spray distribution. The long backward-facing step cavity-based combustor with the upstream wall transverse injection is an optimized injection scheme to both improve penetration and spray distribution inside the cavity. Finally, a penetration depth formula is proposed to explain the spray distribution behaviors in a tandem backward-facing step cavity.

An Epistemic Approach to Ground

AbstractRecent enthusiasm for grounding often begins by observing that inquiry in metaphysics (and other areas) features a distinctive species of noncausal explanation. Having labeled this species “grounding explanation,” it’s a short step to the conclusion that we need a philosophical theory of grounding itself: an allegedly fundamental relation of metaphysical dependency between facts, such that a “grounding explanation” of some fact succeeds by providing information about what “grounds” that fact. This short step is hasty. For another live option is to accept that grounding explanation is a legitimate form of explanation, but to give it a thoroughly epistemic treatment, one that does not see it as involving any sort of special metaphysical relationship or structure at all. This paper sketches such a treatment, drawing inspiration from reflections on explanatory structure in mathematics.

Multiscale Methods Framework with the 3D-RISM-KH Molecular Solvation Theory for Supramolecular Structures, Nanomaterials, and Biomolecules: Where Are We Going?

3D-RISM-KH molecular solvation theory based on statistical mechanics has been an engine of the multiscale methods framework, which also includes molecular simulation techniques. Its applications range from the solvation energy of small molecules to the phase behavior of polymers and biomolecules. Molecular solvation theory predicts and explains the molecular mechanisms and functioning of a variety of chemical and biomolecular systems. This includes the self-assembly and conformational stability of synthetic organic rosette nanotubes (RNTs), the aggregation of peptides and proteins related to neurodegeneration, the binding of ligands to proteins, and the solvation properties of biomolecules related to their functions. The replica RISM-KH-VM molecular solvation theory predicts and explains the structure, thermodynamics, and electrochemistry of electrolyte solutions sorbed in nanoporous carbon supercapacitor electrodes, and is part of recent research and development efforts. A new quasidynamics protocol couples multiple time step molecular dynamics (MTS-MD) stabilized with an optimized isokinetic Nosé–Hoover (OIN) thermostat driven by 3D-RISM-KH mean solvation forces at gigantic outer time steps of picoseconds, which are extrapolated forward at short inner time steps of femtoseconds with generalized solvation force extrapolation (GSFE). The OIN/3D-RISM-KH/GSFE quasidynamics is implemented in the Amber Molecular Dynamics package. It is validated on miniprotein 1L2Y and protein G in ambient aqueous solution, and shows the rate of sampling 150 times faster than in standard MD simulations on these biomolecules in explicit water. The self-consistent field version of Kohn–Sham DFT in 3D-RISM-KH mean solvation forces is implemented in the Amsterdam Density Functional (ADF) package. Its applications range from solvation thermochemistry, conformational equilibria, and photochemistry to activation barriers of different nanosystems in solutions and ionic liquids.

Two-level eddy covariance measurements reduce bias in land-atmosphere exchange estimates over a heterogeneous boreal forest landscape

Estimates of land-atmosphere exchanges of carbon, energy, water vapor, and other greenhouse gases based on the eddy covariance (EC) technique rely on the fundamental assumption that the flux footprint area is homogeneous. We investigated the impact of source area heterogeneity on flux estimates in single-level EC measurements over a managed boreal forest landscape. For this purpose, we compared single-level measurements with those from a two-level approach consisting of concurrent EC measurements at 60 and 85 m above the ground. This two-level set-up provided a unique opportunity to obtain nearly congruent diel footprint areas by combining data from the higher and lower levels during day- and nighttime, respectively.We found that the variation in the averaged footprint area between day- and nighttime was reduced by up to 89% in the two-level approach compared to the single-level data at the higher level (85 m). Considering spring, summer, and fall months, the resulting relative potential bias in flux observations due to landscape heterogeneity was highest at short time steps (≤ daily) ranging between 35% and 325% for half-hourly data. During winter months, when stable atmospheric regimes prevailed during day and night, the footprints within the diel course nearly overlapped also at a given single level and hence no improvement of flux estimates was found. The absolute cumulated sums for the study period (excluding winter months) of gross primary production, ecosystem respiration, latent heat, and sensible heat flux were underestimated by about 28%, 52%, 5%, and 3%, respectively, whereas that of net ecosystem CO2 exchange was overestimated by about 109% in the single-level approach. Overall this study suggests that footprint heterogeneity may introduce considerable bias in single-level flux estimates — particularly at short time scales — with large implications for model-data fusion studies, site comparisons, and up- or downscaling of land-atmosphere exchange processes.

High-detection-efficiency stereo microscope system based on a mobile phone.

Most stereoscopic microscopes used for industrial component detection are large and have low detection efficiencies. The use of mobile phones as imaging systems (rather than conventional sensors) in industrial fields would make industrial testing more convenient. In this study, an external stereo microscope for mobile phones is designed. The proposed system can resolve details up to 0.01 mm with an 11 mm object field of view, -6.34× angular magnification, and quantitative 3D feature measurement. The combined system proposed in this paper is suitable for the microscopic observation of industrial components, with its low cost, high detection efficiency, and short installation steps.

Effect of antiparkinsonian medication on spatiotemporal gait parameters of individuals with Parkinson’s disease: comparison between individuals with and without freezing of gait

BACKGROUND: The gait of individuals with Parkinson’s disease (PD) tends to be slower, characterized by narrow and short steps. During the medication, the self-selected gait speed of individuals with PD increases. However, when looking individually at the spatiotemporal parameters of gait, the medication induces different and not always consistent effects. However, the effects of medication and freezing of gait during walking in individuals with PD are unknown. AIM: To analyze the effect of antiparkinsonian medication and freezing of gait (FoG) on spatiotemporal gait parameters in individuals with PD. METHOD: For this purpose, we compared gait parameters in individuals with FoG (freezers, n=11) and without FoG (non-freezers, n=11). Spatiotemporal gait parameters (speed, cadence, step length, step time, step width, stride length, stride time, swing phase, and double support) and clinical scales (parts II and III of the Unified Parkinson’s Disease Rating Scale, Hoehn and Yahr, Montreal Cognitive Assessment questionnaire and Mini-Test scale of Balance Assessment System) were analyzed in two experimental sessions, counterbalanced between individuals: one in the ON medication state (1h after ingestion), and another in the OFF medication state (~12h after the last ingestion). Linear mixed effects models 2-groups X 2-conditions were used. RESULTS: We found that gait speed, stride, and step length were significantly higher in the ON than in the OFF condition for both freezers and non-freezers, and significantly lower in the freezers than in the non-freezers, regardless of the medication state. CONCLUSION: These results indicate that medication improved gait performance equivalently in both freezers and non-freezers.

Spiking neural network with working memory can integrate and rectify spatiotemporal features.

In the real world, information is often correlated with each other in the time domain. Whether it can effectively make a decision according to the global information is the key indicator of information processing ability. Due to the discrete characteristics of spike trains and unique temporal dynamics, spiking neural networks (SNNs) show great potential in applications in ultra-low-power platforms and various temporal-related real-life tasks. However, the current SNNs can only focus on the information a short time before the current moment, its sensitivity in the time domain is limited. This problem affects the processing ability of SNN in different kinds of data, including static data and time-variant data, and reduces the application scenarios and scalability of SNN. In this work, we analyze the impact of such information loss and then integrate SNN with working memory inspired by recent neuroscience research. Specifically, we propose Spiking Neural Networks with Working Memory (SNNWM) to handle input spike trains segment by segment. On the one hand, this model can effectively increase SNN's ability to obtain global information. On the other hand, it can effectively reduce the information redundancy between adjacent time steps. Then, we provide simple methods to implement the proposed network architecture from the perspectives of biological plausibility and neuromorphic hardware friendly. Finally, we test the proposed method on static and sequential data sets, and the experimental results show that the proposed model can better process the whole spike train, and achieve state-of-the-art results in short time steps. This work investigates the contribution of introducing biologically inspired mechanisms, e.g., working memory, and multiple delayed synapses to SNNs, and provides a new perspective to design future SNNs.

Molecular Latent Space Simulators for Distributed and Multimolecular Trajectories.

All atom molecular dynamics (MD) simulations offer a powerful tool for molecular modeling, but the short time steps required for numerical stability of the integrator place many interesting molecular events out of reach of unbiased simulations. The popular and powerful Markov state modeling (MSM) approach can extend these time scales by stitching together multiple short discontinuous trajectories into a single long-time kinetic model but necessitates a configurational coarse-graining of the phase space that entails a loss of spatial and temporal resolution and an exponential increase in complexity for multimolecular systems. Latent space simulators (LSS) present an alternative formalism that employs a dynamical, as opposed to configurational, coarse graining comprising three back-to-back learning problems to (i) identify the molecular system's slowest dynamical processes, (ii) propagate the microscopic system dynamics within this slow subspace, and (iii) generatively reconstruct the trajectory of the system within the molecular phase space. A trained LSS model can generate temporally and spatially continuous synthetic molecular trajectories at orders of magnitude lower cost than MD to improve sampling of rare transition events and metastable states to reduce statistical uncertainties in thermodynamic and kinetic observables. In this work, we extend the LSS formalism to short discontinuous training trajectories generated by distributed computing and to multimolecular systems without incurring exponential scaling in computational cost. First, we develop a distributed LSS model over thousands of short simulations of a 264-residue proteolysis-targeting chimera (PROTAC) complex to generate ultralong continuous trajectories that identify metastable states and collective variables to inform PROTAC therapeutic design and optimization. Second, we develop a multimolecular LSS architecture to generate physically realistic ultralong trajectories of DNA oligomers that can undergo both duplex hybridization and hairpin folding. These trajectories retain thermodynamic and kinetic characteristics of the training data while providing increased precision of folding populations and time scales across simulation temperature and ion concentration.

Application of the Ridden Horse Pain Ethogram to 150 Horses with Musculoskeletal Pain before and after Diagnostic Anaesthesia.

The Ridden Horse Pain Ethogram (RHpE) was developed to facilitate the recognition of musculoskeletal pain. The aim of this study was to document changes in RHpE scores before and after diagnostic anaesthesia was performed to alleviate pain ± when the saddle was changed. One hundred and fifty horses underwent ridden exercise as part of an investigation of poor performance. The RHpE was applied before and after the interventions. Fifty-two (34.7%) horses exhibited a bilaterally symmetrical short step length and/or restricted hindlimb impulsion and engagement. Fifty-three (35.3%) horses had episodic lameness; only forty-five (30.0%) horses were continuously lame. The median maximum lameness grade when ridden was 2/8 (interquartile range [IQR]: 0-3; range: 0-4). Fifty-six (37.3%) horses had an ill-fitting saddle, which was considered likely to influence performance. The median RHpE scores after the interventions (2/24 [IQR: 1-3, range: 0-12]) were significantly lower than before the interventions (9/24 [IQR: 8-11, range: 2-15]) (Wilcoxon signed-rank z = 10.6, p < 0.001). There was no correlation between the RHpE score and maximum lameness grade before diagnostic anaesthesia (Spearman's rho = 0.09, p = 0.262). It was concluded that the absence of overt lameness does not preclude primary musculoskeletal pain. Gait quality and performance can be improved by diagnostic anaesthesia, with substantial reductions in RHpE scores.

Natural Ventilation in Low-Cost Housing: An Evaluation by CFD

With the spread of sustainability concepts, due to the global energy crisis and the unrestrained consumption of natural resources, the importance of rational use and reduction of energy consumption has been intensified, albeit in short steps. This concern has taken up bioclimatic concepts in buildings, especially housing buildings for low-income people, aiming at improving the quality of housing production in terms of its habitability. The objective of this research is to theoretically and experimentally study the natural ventilation in a housing building, which fits into the low-cost pattern. The use of Computational Fluid Dynamics to predict natural ventilation was adopted as a tool to perform these analyses. Experiments were carried out by visiting the study area to collect data on air velocity, temperature, and relative humidity in all rooms of the residence in order to carry out the intended analyses. Computer simulation of natural ventilation considering the building with the doors and windows open and with fixed geometry was performed. Results proved that the proposed mathematical model was able to reproduce, with rich details and physical coherence, the internal and external air flow inside the building, indicating better internal ventilation performance in environments that have door and window openings, with recesses, revealing the importance of cross ventilation to reduce the internal temperature and consequent improvement in thermal comfort. The idea is to help civil engineers and specialists in the economically viable design of low-cost buildings from the economic, social, and thermal comfort points of view.

The effect of mobility-related anxiety on walking across the lifespan: a virtual reality simulation study.

Older adults who report a fear of falling are more likely to subsequently fall, yet, some gait anxiety-related alterations may protect balance. We examined the effect of age on walking in anxiety-inducing virtual reality (VR) settings. We predicted a high elevation-related postural threat would impair gait in older age, and differences in cognitive and physical function would relate to the observed effects. Altogether, 24 adults (age (y) = 49.2 (18.7), 13 women) walked on a 2.2-m walkway at self-selected and fast speeds at low (ground) and high (15m) VR elevation. Self-reported cognitive and somatic anxiety and mental effort were greater at high elevations (all p < 0.001), but age- and speed-related effects were not observed. At high VR elevations, participants walked slower, took shorter steps, and reduced turning speed (all p < 0.001). Significant interactions with age in gait speed and step length showed that relatively older adults walked slower (β = - 0.05, p = 0.024) and took shorter steps (β = - 0.05, p = 0.001) at self-selected speeds at high compared to low elevation settings. The effect of Age on gait speed and step length disappeared between self-selected and fast speeds and at high elevation. At self-selected speeds, older adults took shorter and slower steps at high elevation without changing step width, suggesting that in threatening settings relatively older people change gait parameters to promote stability. At fast speeds, older adults walked like relatively younger adults (or young adults walked like older adults) supporting the notion that people opt to walk faster in a way that still protects balance and stability in threatening settings.

A novel green preparation process for N-acetyl-L-cysteine: More efficient, economical and environmentally friendly

N-acetyl-L-cysteine (NAC) is widely used in the biochemistry, pharmaceuticals and food additives. At present, the synthesis methods of NAC are difficult to achieve high-quality NAC production due to multi-step separation, poor product quality and heavy pollution. In this study, a new green process was developed combined with organic synthesis, electrochemical reduction and electrodialysis technology to efficiently produce high quality NAC. This new process adopted the strategy of acylation followed by electrolysis. Firstly, L-cystine was acetylated to N, N’-diacetyl-L-cystine (DiNAC), and then DiNAC was electrochemically directly reduced to NAC using carbon cathode. Finally, the high quality NAC was extracted after desalting by electrodialysis. The novel preparation process avoids racemization and oxidation of intermediate products, achieves the removal of impurities, desalting and recycling of electrolyte, and largely reduces wastes discharge. The pilot scale test showed that the total yield was 73.1%, the electrochemical yield was 96.2%, and the energy consumption was 2.56 kW·h kg−1. The brief environmental and tech-economic analysis demonstrated that the process had the advantages of mild reaction conditions, short synthesis steps, high product quality, low waste emissions. It indicates that this novel process is efficient, economical, environmentally friendly, and suitable for the industrial production.

Carrying children, groceries, and water across varying terrain: Changes in gait and comfortable walking speed

BackgroundCarrying items of substantial weight and value in one’s arms while traversing challenging terrain is a common task that requires considerable care. Carrying valuable (e.g., child) or variable (e.g., water) items, compared to stable ones (e.g., groceries) demands increased coordination, and is likely to lead to slower comfortable walking speed (CWS) and altered gait mechanics, especially on difficult terrain. Research questionHow are gait parameters altered by carrying items of substantial weight and varying value and dynamics across more and less demanding terrain? MethodsIn two experiments, participants carried their child, an equally weighted sack of groceries, or an open bucket of water in the same manner across level floor and across uneven stairs of varying heights with gaps between them. Kinematics were assessed for both terrains; kinetics were measured for one step up and one step down on stairs. ResultsMixed models ANOVAs with repeated measures revealed that CWS on uneven stairs was approximately 65 % of CWS for level floor, regardless of the item carried. Step-to-step coefficients of variation for step length and CWS were also greater. Water was carried most slowly, with shorter steps on level floor and reduced accelerations on uneven stairs. CWS with children and groceries did not differ. SignificanceCarrying items of weight and worth with varying dynamics across more and less challenging terrain illustrates the ecological complexity of walking. Terrain requiring greater flexibility, strength, and coordination reduced CWS substantially, a complexity-speed tradeoff. More variable, difficult to control items altered CWS and other gait patterns regardless of terrain difficulty, suggesting terrain and item dynamics contributed independently to gait adjustments. More valuable items were not carried more slowly than less valuable ones. Carrying tasks deserve greater attention in research and clinical assessment.