Walking with increased step length variability increases the metabolic cost of walking.

Older adults and neurological populations tend to walk with slower speeds, more gait variability, and a higher metabolic cost. This higher metabolic cost could be related to their increased gait variability, but this relationship is still unclear. The purpose of this study was to determine how increased step length variability affects the metabolic cost of waking. Eighteen healthy young adults completed a set of 5-minute trials of treadmill walking at 1.20 m/s while we manipulated their step length variability. Illuminated rectangles were projected onto the surface of a treadmill to cue step length variabilities of 0, 5 and 10% (coefficient of variation). Actual step lengths and their variability were tracked with reflective markers on the feet, while metabolic cost was measured using indirect calorimetry. Changes in metabolic cost across habitual walking (no projections) and the three variability conditions were analyzed using a linear mixed effects model. Metabolic power was largest in the 10% condition (4.30 ± 0.23 W/kg) compared to 0% (4.16 ± 0.18 W/kg) and habitual (3.98 ± 0.25 W/kg). The participant's actual step length variability did not match projected conditions for 0% (3.10%) and 10% (7.03%). For every 1% increase in step length variability, there is an 0.7% increase in metabolic cost. Our results demonstrate an association between the metabolic cost of walking and gait step length variability. This suggests that increased gait variability contributes to a portion of the increased cost of walking seen in older adults and neurological populations.

Longitudinal quantitative assessment of TMEV-IDD-induced MS phenotypes in two inbred mouse strains using automated video tracking technology.

Multiple sclerosis (MS) is a chronic disabling disease of the central nervous system affecting over 2.5 million people worldwide. Theiler's murine encephalomyelitis virus-induced demyelinating disease (TMEV-IDD) is a murine model that reproduces the progressive form of MS and serves as a reference model for studying virus-induced demyelination. Certain mouse strains such as SJL are highly susceptible to this virus and serve as a prototype strain for studying TMEV infection. Other strains such as SWR are also susceptible, but their disease course following TMEV infection differs from SJL's. The quantification of motor and behavioral deficits following the induction of TMEV-IDD could help identify the differences between the two strains. Motor deficits have commonly been measured with the rotarod apparatus, but a multicomponent assessment tool has so far been lacking. For that purpose, we present a novel way of quantifying locomotor deficits, gait alterations and behavioral changes in this well-established mouse model of multiple sclerosis by employing automated video analysis technology (The PhenoTyper, Noldus Information Technology). We followed 12 SJL and 12 SWR female mice and their mock-infected counterparts over a period of 9 months following TMEV-IDD induction. We demonstrated that SJL and SWR mice both suffer significant gait alterations and reduced exploration following TMEV infection. However, SJL mice also display an earlier and more severe decline in spontaneous locomotion, especially in velocity, as well as in overall activity. Maintenance behaviors such as eating and grooming are not affected in either of the two strains. The system also showed differences in mock-infected mice from both strains, highlighting an age-related decline in spontaneous locomotion in the SJL strain, as opposed to hyperactivity in the SWR strain. Our study confirms that this automated video tracking system can reliably track the progression of TMEV-IDD for 9 months. We have also shown how this system can be utilized for longitudinal phenotyping in mice by describing useful parameters that quantify locomotion, gait and behavior.

Analyzing Dual-Task Paradigms to Improve Postconcussion Assessment and Management.

CONTEXT: Dual-task (simultaneous cognitive-motor activities) assessments have been adapted into reliable and valid clinical concussion measures. However, abundant motor and cognitive variations leave researchers and clinicians uncertain about which combinations elicit the intended dual-task effect. Our objective was to examine differences between commonly employed dual-task motor and cognitive combinations among healthy, college-aged individuals. DESIGN: Cross-sectional laboratory study. METHODS: Twenty participants (age: 21.3 [2.4] y; height: 176.0 [9.1] cm; mass: 76.0 [16.4] kg; 20% with concussion history) completed 4 motor tasks (gait, tandem gait, single-leg balance, and tandem balance) under 5 cognitive conditions (single task, subtraction, month reversal, spelling backward, and visual Stroop) in a research laboratory. The motor performance outcomes were spatiotemporal variables for gait and tandem gait and center of pressure path length (in centimeters) for single-leg and tandem balance. Cognitive outcomes were response rate (responses/second) and cognitive accuracy. We used separate repeated-measures analyses of variance for each motor and cognitive outcome with post hoc Tukey t tests. RESULTS: Gait velocity, gait stride length, and tandem gait velocity demonstrated significant cognitive-motor interactions (P's < .001) such that all dual-task conditions resulted in varyingly slower or shorter movement than single task. Conversely, single-leg balance (P = .627) and tandem balance (P = .434) center of pressure path length did not significantly differ among the dual-task cognitive conditions or relative to single task. Statistically significant cognitive-motor interactions were observed only for spelling backward accuracy (P = .004) and response rates for spelling backward, month reversal, and visual Stroop (P's < .001) such that worse accuracy, but faster response rates, occurred during motor tasks. CONCLUSIONS: Gait and tandem gait motor tasks accompanied with spelling backward or subtraction cognitive tasks demonstrated consistently strong dual-task effects and, therefore, may be the best suited for clinical and research use following concussion.

Skilled reaching test for shoulder function assessment in a rat model of rotator cuff tear: a pilot study.

BACKGROUND: Functional assessments are crucial to evaluate treatment outcomes in clinical and animal studies on rotator cuff injuries. While gait analysis is commonly used to assess animal models of rotator cuff tears, it is less relevant for human patients as the human shoulder is typically assessed in a non-weight-bearing condition. The present study introduces the skilled reaching test as a shoulder functional assessment tool for rats, which allows for evaluation without weight bearing. METHODS: In the control group, 8 male Sprague-Dawley rats received rotator cuff tear surgery without repair. In the rotator cuff repair group, 20 rats received rotator cuff repair at 4 weeks post rotator cuff tear. For the skilled reaching test, rats were trained to extend their forelimbs to fetch food pellets, and the number of trials, number of attempts and the success rate were recorded. The gait analysis and skilled reaching test were performed at baseline, 4 weeks post-tear, 1, 2, 4, and 8 weeks post-repair. The repeated measures analysis of variance was used to evaluate the effects of time on the shoulder function. The significance level was set at 0.05. RESULTS: The skilled reaching test required 216 h to conduct, while the gait analysis took 44 h. In the rotator cuff repair group, gait performance significantly deteriorated at 1 week post-repair and restored to 4 weeks post-tear levels at 4 weeks post-repair. Regarding the skilled reaching test, the number of attempts, number of trials and the success rate decreased at 1 week post-repair. Subsequently, there was a brief rebound in performance observed at 2 weeks post-repair, followed by a continued decline in the number of attempts and trials. By 8 weeks post-repair, only the success rate had restored to levels similar to those observed at 4 weeks post-tear. CONCLUSION: The skilled reaching test can detect functional deficiencies following rotator cuff tear and repair, while it requires high time and labour costs.

Enhancing fall risk assessment: instrumenting vision with deep learning during walks.

BACKGROUND: Falls are common in a range of clinical cohorts, where routine risk assessment often comprises subjective visual observation only. Typically, observational assessment involves evaluation of an individual's gait during scripted walking protocols within a lab to identify deficits that potentially increase fall risk, but subtle deficits may not be (readily) observable. Therefore, objective approaches (e.g., inertial measurement units, IMUs) are useful for quantifying high resolution gait characteristics, enabling more informed fall risk assessment by capturing subtle deficits. However, IMU-based gait instrumentation alone is limited, failing to consider participant behaviour and details within the environment (e.g., obstacles). Video-based eye-tracking glasses may provide additional insight to fall risk, clarifying how people traverse environments based on head and eye movements. Recording head and eye movements can provide insights into how the allocation of visual attention to environmental stimuli influences successful navigation around obstacles. Yet, manual review of video data to evaluate head and eye movements is time-consuming and subjective. An automated approach is needed but none currently exists. This paper proposes a deep learning-based object detection algorithm (VARFA) to instrument vision and video data during walks, complementing instrumented gait. METHOD: The approach automatically labels video data captured in a gait lab to assess visual attention and details of the environment. The proposed algorithm uses a YoloV8 model trained on with a novel lab-based dataset. RESULTS: VARFA achieved excellent evaluation metrics (0.93 mAP50), identifying, and localizing static objects (e.g., obstacles in the walking path) with an average accuracy of 93%. Similarly, a U-NET based track/path segmentation model achieved good metrics (IoU 0.82), suggesting that the predicted tracks (i.e., walking paths) align closely with the actual track, with an overlap of 82%. Notably, both models achieved these metrics while processing at real-time speeds, demonstrating efficiency and effectiveness for pragmatic applications. CONCLUSION: The instrumented approach improves the efficiency and accuracy of fall risk assessment by evaluating the visual allocation of attention (i.e., information about when and where a person is attending) during navigation, improving the breadth of instrumentation in this area. Use of VARFA to instrument vision could be used to better inform fall risk assessment by providing behaviour and context data to complement instrumented e.g., IMU data during gait tasks. That may have notable (e.g., personalized) rehabilitation implications across a wide range of clinical cohorts where poor gait and increased fall risk are common.

Decrease in the usual walking speed and body fat percentage associated with a deterioration in long-term care insurance certification levels.

Background: In Japan, the number of older adults requiring long-term care insurance (LTCI) is increasing and the cost is becoming a social problem. In these fields, the role of geriatric rehabilitation includes maintaining the physical function and LTCI certification levels. The prevalence of sarcopenia is high among older adults requiring LTCI certification, and there are many opportunities to assess the handgrip strength, walking speed, and muscle mass. This study aimed to identify sarcopenia-related assessments sensitive to transitions in LTCI certification levels and determine cut-off values to predict them. Methods: This prospective cohort study analyzed 98 daycare users (mean age ± standard error: 78.5 ± 0.8 years) between March 2019 and 2023. The participants received LTCI certification before the study, and their levels were renewed between baseline and follow-up (six months later). The measurements included handgrip strength, usual walking speed, body composition, and SARC-F score. Participants were classified into maintenance, deterioration, and improvement groups according to the changes in their LTCI certification levels. We identified factors contributing to the deterioration of LTCI certification levels using baseline and before and after comparisons, multivariate analyses, and receiver operating characteristic analyses. Results: No significant differences were observed in the baseline data among the groups. Only the deterioration group showed significant changes in the usual walking speed (baseline: 0.64 ± 0.25 m/s, follow-up: 0.53 ± 0.21 m/s, P = 0.008) and body fat percentage (baseline: 29.2 ± 9.9%, follow-up: 27.7 ± 10.3%, P = 0.047). Binomial logistic regression showed that changes in usual walking speed (P = 0.042) and body fat percentage (P = 0.011) were significantly associated with the deterioration of LTCI certification levels, even after adjustment. The cutoff values of change to discriminate the deterioration of LTCI certification levels were -0.14 m/s at the usual walking speed (P = 0.047) and -1.0% for body fat percentage (P = 0.029). Conclusions: Decreases in usual walking speed and body fat percentage may predict worse certification levels in older adults requiring LTCI.

Assessment of stabilizing feedback control of walking: A tutorial.

Walking without falling requires stabilization of the trajectory of the body center of mass relative to the base of support. Model studies suggest that this requires active, feedback control, i.e., the nervous system must process sensory information on the state of the body to generate descending motor commands to the muscles to stabilize walking, especially in the mediolateral direction. Stabilization of bipedal gait is challenging and can be impaired in older and diseased individuals. In this tutorial, we illustrate how gait analysis can be used to assess the stabilizing feedback control of gait. We present methods ranging from those that require limited input data (e.g. position data of markers placed on the feet and pelvis only) to those that require full-body kinematics and electromyography. Analyses range from simple kinematics analyses to inverse dynamics. These methods assess stabilizing feedback control of human walking at three levels: 1) the level of center of mass movement and horizontal ground reaction forces, 2) the level of center of mass movement and foot placement and 3) the level of center of mass movement and the joint moments or muscle activity. We show how these can be calculated and provide a GitHub repository (https://github.com/VU-HMS/Tutorial-stabilizing-walking) which contains open access Matlab and Python code to calculate these. Finally, we discuss what information on feedback control can be learned from each of these.

Assessment of lumbar disc herniation-impaired gait by using IMU data fusion method.

The inertial motion unit (IMU) is an effective tool for monitoring and assessing gait impairment in patients with lumbar disc herniation(LDH). However, the current clinical assessment methods for LDH gait focus on patients' subjective scoring indicators and lack the assessment of kinematic ability; at the same time, individual differences in the motor function degradation of the healthy and affected lower limbs of LDH patients are also ignored. To solve this problem, we propose an LDH gait feature model based on multi-source adaptive Kalman data fusion of acceleration and angular velocity. The gait phase is segmented by using an adaptive Kalman data fusion algorithm to estimate the attitude angle, and obtaining gait events through a zero-velocity update technique and a peak detection algorithm. Two IMUs were used to analyze the gait characteristics of lumbar disc patients and healthy gait people, including 12 gait characteristics such as gait spatiotemporal parameters, kinematic parameters, gait variability and stability. Statistical methods were used to analyze the characteristic model and verify the biological differences between the healthy affected side of LDH and healthy subjects. Finally, feature engineering and machine learning technology were used to identify the gait pattern of inertial movement units in patients with lumbar intervertebral disc disease, and achieved a classification accuracy of 95.50%, providing an effective gait feature set and method for clinical evaluation of LDH.

Assessment of Gait Patterns during Crutch Assisted Gait through Spatial and Temporal Analysis.

The use of crutches is a common method of assisting people during recovery from musculoskeletal injuries in the lower limbs. There are several different ways to walk with crutches depending on the patient's needs. The structure of crutch gaits or crutch gait patterns varies based on the delay between the aid and foot placement, the number of concurrent points of contact, and laterality. In a rehabilitation process, the prescribed pattern may differ according to the injury, the treatment and the individual's condition. Clinicians may improve diagnosis, assessment, training, and treatment by monitoring and analyzing gait patterns. This study aimed to assess and characterize four crutch walking patterns using spatial and temporal parameters obtained from the instrumented crutches. For this purpose, 27 healthy users performed four different gait patterns over multiple trials. Each trial was recorded using a portable system integrated into the crutches, which measured force, position, and acceleration. Based on the data angle, an algorithm was developed to segment the trials into gait cycles and identify gait phases. The next step was to determine the most appropriate metrics to describe each gait pattern. Several metrics were used to analyze the collected data, including force, acceleration, angle, and stride time. Among 27 participants, significant differences were found between crutch gait patterns. Through the use of these spatial and temporal parameters, promising results were obtained for monitoring assisted gait with crutches. Furthermore, the results demonstrated the possibility of using instrumented crutches as a clinical tool.

ExoBand, A Passive Wearable Device as a Walking Aid in Neuromuscular Patients: First Quantitative Assessment.

Objective: Exoband (by Moveo, Padova, Italy) functions as a walking brace, comprising a belt and two leg loops connected by a mechanism that stores energy during the initial phase of the gait cycle and releases it in the subsequent phase. This enhances hip flexor thrust, leading to functional improvement in walking for individuals with conditions characterized by proximal weakness. It has been approved as a passive wearable device for individuals with impaired walking abilities. Objective of this study was to establish a protocol to assess the use of Exoband in patients with various neuromuscular disorders. Methods: This exploratory retrospective study includes consecutive patients diagnosed with neuromuscular disorders (CIDP, motor polyneuropathy, MND), exhibiting a proximal involvement and gait abnormalities. The evaluation protocol incorporated specific walking-related outcome measures, the 10-meter walk test (10mWT), Time-up-and-go test (TUG), and 2-minute walking test (2MWT). The assessments were conducted both with and without the Exoband under standard conditions. Results: Eight patients (6 males, aged 60-78 years) were tested. An increase in velocity was observed in the 10mWT (median 13.4 sec, IQR 12.0-15.7 vs. 12.2 sec, IQR 11.3-14.2 seconds, p < 0.05) and the TUG (14.0 sec, IQR 13-16.2 vs 13.35 sec, IQR 11-13.8; p < 0.05, by non-parametric Wilcoxon test), and a trend of increase in 2MWT (median 88.2 vs 92.6 m, n.s.). Six out of 8 patients reported subjective benefits from the very first use, including improved walking stability, speed, confidence, and reduced fatigue. Conclusions: Our protocol provides a quantitative assessment of Exoband usefulness for patients affected by neuropathies with gait abnormalities. Further investigations are warranted to assess the long-term effects of its regular Exoband use, its efficacy in specific neuromuscular diseases, and its potential role as a rehabilitation device.

Beyond gait speed: exploring the added value of Inertial Measurement Unit-based measurements of gait in the estimation of the walking ability in daily life.

BACKGROUND: Gait speed is often used to estimate the walking ability in daily life in people after stroke. While measuring gait with inertial measurement units (IMUs) during clinical assessment yields additional information, it remains unclear if this information can improve the estimation of the walking ability in daily life beyond gait speed. OBJECTIVE: We evaluated the additive value of IMU-based gait features over a simple gait-speed measurement in the estimation of walking ability in people after stroke. METHODS: Longitudinal data during clinical stroke rehabilitation were collected. The assessment consisted of two parts and was administered every three weeks. In the first part, participants walked for two minutes (2MWT) on a fourteen-meter path with three IMUs attached to low back and feet, from which multiple gait features, including gait speed, were calculated. The dimensionality of the corresponding gait features was reduced with a principal component analysis. In the second part, gait was measured for two consecutive days using one ankle-mounted IMU. Next, three measures of walking ability in daily life were calculated, including the number of steps per day, and the average and maximal gait speed. A gait-speed-only Linear Mixed Model was used to estimate the association between gait speed and each of the three measures of walking ability. Next, the principal components (PC), derived from the 2MWT, were added to the gait-speed-only model to evaluate if they were confounders or effect modifiers. RESULTS: Eighty-one participants were measured during rehabilitation, resulting in 198 2MWTs and 135 corresponding walking-performance measurements. 106 Gait features were reduced to nine PCs with 85.1% explained variance. The linear mixed models demonstrated that gait speed was weakly associated with the average and maximum gait speed in daily life and moderately associated with the number of steps per day. The PCs did not considerably improve the outcomes in comparison to the gait speed only models. CONCLUSIONS: Gait in people after stroke assessed in a clinical setting with IMUs differs from their walking ability in daily life. More research is needed to determine whether these discrepancies also occur in non-laboratory settings, and to identify additional non-gait factors that influence walking ability in daily life.

The Difference in the Assessment of Knee Extension/Flexion Angles during Gait between Two Calibration Methods for Wearable Goniometer Sensors.

Frontal and axial knee motion can affect the accuracy of the knee extension/flexion motion measurement using a wearable goniometer. The purpose of this study was to test the hypothesis that calibrating the goniometer on an individual's body would reduce errors in knee flexion angle during gait, compared to bench calibration. Ten young adults (23.2 ± 1.3 years) were enrolled. Knee flexion angles during gait were simultaneously assessed using a wearable goniometer sensor and an optical three-dimensional motion analysis system, and the absolute error (AE) between the two methods was calculated. The mean AE across a gait cycle was 2.4° (0.5°) for the on-body calibration, and the AE was acceptable (<5°) throughout a gait cycle (range: 1.5-3.8°). The mean AE for the on-bench calibration was 4.9° (3.4°) (range: 1.9-13.6°). Statistical parametric mapping (SPM) analysis revealed that the AE of the on-body calibration was significantly smaller than that of the on-bench calibration during 67-82% of the gait cycle. The results indicated that the on-body calibration of a goniometer sensor had acceptable and better validity compared to the on-bench calibration, especially for the swing phase of gait.

Foot Posture Index Does Not Correlate with Dynamic Foot Assessment Performed via Baropodometric Examination: A Cross-Sectional Study.

BACKGROUND: Clinicians employ foot morphology assessment to evaluate the functionality of the method and anticipate possible injuries. This study aims to correlate static foot posture and the dynamic barefoot evaluation in a sample of healthy adult participants. METHODS: The foot posture was evaluated using the Foot Posture Index-6 (FPI-6) and the dynamics were evaluated through baropodometric examination. Two operators independently assessed the participants' foot posture through FPI-6, and then a dynamic evaluation was performed by asking them to walk 8 times across a platform. One hundred participants (mean age: 32.15 ± 7.49) were enrolled. RESULTS: The inter-rater agreement between the two assessors was found to be excellent. The majority of the feet belonged to the 0 < FPI < 4 class (32%), followed by the 4 < FPI < 8 (31%) and the FPI > 8 ranges (19.5%). Our "area of contact" analysis showed a significant poor correlation between FPI and total foot, midfoot, and the second metatarsophalangeal joint (MTPJ) (-0.3 < r < 0). Regarding "force" parameters, the analysis showed a poor correlation between the midfoot, hallux, and the second toe (-0.2 < r < 2); finally the "pressure" analysis showed a poor correlation between FPI, the fourth MTPJ, and the second toe (-0.2 < rs < 0.3) and a moderate correlation between the hallux (r = 0.374) and the fifth MTPJ (r = 0.427). CONCLUSIONS: This study emphasizes the constrained correlation between static foot posture observation and dynamic barefoot examination.

[A study of cognitive impairment quantitative assessment method based on gait characteristics].

Alzheimer's disease (AD) is a common and serious form of elderly dementia, but early detection and treatment of mild cognitive impairment can help slow down the progression of dementia. Recent studies have shown that there is a relationship between overall cognitive function and motor function and gait abnormalities. We recruited 302 cases from the Rehabilitation Hospital Affiliated to National Rehabilitation Aids Research Center and included 193 of them according to the screening criteria, including 137 patients with MCI and 56 healthy controls (HC). The gait parameters of the participants were collected during performing single-task (free walking) and dual-task (counting backwards from 100) using a wearable device. By taking gait parameters such as gait cycle, kinematics parameters, time-space parameters as the focus of the study, using recursive feature elimination (RFE) to select important features, and taking the subject's MoCA score as the response variable, a machine learning model based on quantitative evaluation of cognitive level of gait features was established. The results showed that temporal and spatial parameters of toe-off and heel strike had important clinical significance as markers to evaluate cognitive level, indicating important clinical application value in preventing or delaying the occurrence of AD in the future.

A Computer Vision-Based System to Help Health Professionals to Apply Tests for Fall Risk Assessment.

The increase in life expectancy, and the consequent growth of the elderly population, represents a major challenge to guarantee adequate health and social care. The proposed system aims to provide a tool that automates the evaluation of gait and balance, essential to prevent falls in older people. Through an RGB-D camera, it is possible to capture and digitally represent certain parameters that describe how users carry out certain human motions and poses. Such individual motions and poses are actually related to items included in many well-known gait and balance evaluation tests. According to that information, therapists, who would not need to be present during the execution of the exercises, evaluate the results of such tests and could issue a diagnosis by storing and analyzing the sequences provided by the developed system. The system was validated in a laboratory scenario, and subsequently a trial was carried out in a nursing home with six residents. Results demonstrate the usefulness of the proposed system and the ease of objectively evaluating the main items of clinical tests by using the parameters calculated from information acquired with the RGB-D sensor. In addition, it lays the future foundations for creating a Cloud-based platform for remote fall risk assessment and its integration with a mobile assistant robot, and for designing Artificial Intelligence models that can detect patterns and identify pathologies for enabling therapists to prevent falls in users under risk.

Differences in running technique between runners with better and poorer running economy and lower and higher milage: An artificial neural network approach.

BACKGROUND: Prior studies investigated selected discrete sagittal-plane outcomes (e.g., peak knee flexion) in relation to running economy, hereby discarding the potential relevance of running technique parameters during noninvestigated phases of the gait cycle and in other movement planes. PURPOSE: Investigate which components of running technique distinguish groups of runners with better and poorer economy and higher and lower weekly running distance using an artificial neural network (ANN) approach with layer-wise relevance propagation. METHODS: Forty-one participants (22 males and 19 females) ran at 2.78 m∙s-1 while three-dimensional kinematics and gas exchange data were collected. Two groups were created that differed in running economy or weekly training distance. The three-dimensional kinematic data were used as input to an ANN to predict group allocations. Layer-wise relevance propagation was used to determine the relevance of three-dimensional kinematics for group classification. RESULTS: The ANN classified runners in the correct economy or distance group with accuracies of up to 62% and 71%, respectively. Knee, hip, and ankle flexion were most relevant to both classifications. Runners with poorer running economy showed higher knee flexion during swing, more hip flexion during early stance, and more ankle extension after toe-off. Runners with higher running distance showed less trunk rotation during swing. CONCLUSION: The ANN accuracy was moderate when predicting whether runners had better, or poorer running economy, or had a higher or lower weekly training distance based on their running technique. The kinematic components that contributed the most to the classification may nevertheless inform future research and training.

Increasing midtarsal joint stiffness reduces triceps surae metabolic costs in walking simulations but has little effect on total stance limb metabolic cost.

The human foot's arch is thought to be beneficial for efficient gait. This study addresses the extent to which arch stiffness changes alter the metabolic energy requirements of human gait. Computational musculoskeletal simulations of steady state walking using direct collocation were performed. Across a range of foot arch stiffnesses, the metabolic cost of transport decreased by less than 1% with increasing foot arch stiffness. Increasing arch stiffness increased the metabolic efficiency of the triceps surae during push-off, but these changes were almost entirely offset by other muscle groups consuming more energy with increasing foot arch stiffness.

Human-in-the-loop optimization of rocker shoes via different cost functions during walking.

Personalised footwear could be used to enhance the function of the foot-ankle complex to a person's maximum. Human-in-the-loop optimization could be used as an effective and efficient way to find a personalised optimal rocker profile (i.e., apex position and angle). The outcome of this process likely depends on the selected optimization objective and its responsiveness to the rocker parameters being tuned. This study aims to explore whether and how human-in-the-loop optimization via different cost functions (i.e., metabolic cost, collision work as measure for external mechanical work, and step distance variability as measure for gait stability) affects the optimal apex position and angle of a rocker profile differently for individuals during walking. Ten healthy individuals walked on a treadmill with experimental rocker shoes in which apex position and angle were optimized using human-in-the-loop optimization using different cost functions. We compared the obtained optimal apex parameters for the different cost functions and how these affected the selected gait related objectives. Optimal apex parameters differed substantially between participants and optimal apex positions differed between cost functions. The responsiveness to changes in apex parameters differed between cost functions. Collision work was the only cost function that resulted in a significant improvement of its performance criteria. Improvements in metabolic cost or step distance variability were not found after optimization. This study showed that cost function selection is important when human-in-the-loop optimization is used to design personalised footwear to allow conversion to an optimum that suits the individual.

Objective Assessment of Equine Locomotor Symmetry Using an Inertial Sensor System and Artificial Intelligence: A Comparative Study.

In horses, quantitative assessment of gait parameters, as with the use of inertial measurement units (IMUs) systems, might help in the decision-making process. However, it requires financial investment, is time-consuming, and lacks accuracy if displaced. An innovative artificial intelligence marker-less motion tracking system (AI-MTS) may overcome these limitations in the field. Our aim was to compare the level of agreement and accuracy between both systems and visual clinical assessment. Twenty horses underwent locomotion analysis by visual assessment, IMUs, and AI-MTS systems, under the following conditions: straight hard (SH), straight soft (SS), left and right circle hard (LCH, RCH), and soft (LCS, RCS). A greater number of horses were considered sound by clinical examination, compared to those identified as symmetric by the two gait analysis systems. More limbs were considered asymmetric by the AI-MTS compared to IMUs, suggesting its greater sensitivity. The greatest agreement between the two systems was found for the difference between two minima in vertical head position in SH, while the lowest for the difference between two minima in vertical pelvis position in SS, reflecting the difficulties in assessing asymmetry of the hindlimbs. It is unknown what degree of asymmetry is clinically relevant, suggesting that more consistent use in training horses may help determine the thresholds for asymmetry. Some degree of asymmetry may be clinically relevant, suggesting its regular use in training horses.

Using an Arabic Version of the Life-Space Assessment to Evaluate How Gait Speed and Gender Predict Mobility Restrictions among Older Adults.

Background and Objectives: The Life-Space Assessment (LSA) serves as an assessment tool for evaluating mobility and participation in older adults. To date, no studies have investigated the validity and reliability of the LSA within Arabic-speaking communities. The purpose of this study was to examine the reliability and validity of an Arabic version of the LSA and to investigate the potential predictors of mobility restrictions in older Arabic-speaking adults. Materials and Methods: This study involved a cohort of 75 Arabic-speaking older adults (with a mean age of 67.2 ± 5.9). The LSA was administered twice, with a one-week interval, to assess its test-retest reliability. The internal consistency and test-retest reliability of the LSA were assessed using Cronbach's alpha and intra-class correlation coefficients (ICCs), respectively. The validity of the LSA was determined by analyzing its correlation with outcome measures related to the fear of falling, depression, quality of life, lower limb strength, physical performance, and gait speed. Results: The test-retest reliability of the LSA composite score demonstrated good results (ICC = 0.83). The validity of the LSA was supported by significant correlations between its scores and factors such as gender, education level, and all other outcome measures. Notably, being female and having a lower gait speed emerged as significant predictors of mobility restrictions in older Arabic-speaking adults, accounting for 49% of the variance (R2 = 49%) in the multiple logistic regression analysis conducted. Conclusions: The Arabic version of the LSA has proven to be a reliable and valid measure of mobility and participation among older Arabic-speaking adults. This study endorses the application of the Arabic LSA in both research and clinical settings involving older adults and emphasizes the need for further investigation to fully understand its psychometric features in other Arabic-speaking individuals afflicted with neurological and musculoskeletal conditions.