To evaluate the effect of walking in a virtual world, particularly on a virtual plank at varying heights, on gait stability in children with cerebral palsy (CP) and typical development (TD).

Twenty-two participants with CP (13 males; 11 years 2 months) and twenty-four with TD (10 males; 11 years 11 months) were included. After undergoing clinical gait analysis, participants were immersed in VR via a head-mounted display and walked on a virtual plank at varying heights. Linear mixed-effect models were fitted for: margin of stability (MoS), whole-body angular momentum (WBAM), single limb support % (SLS), variability of step length (SLV), stride width (SWV), and kinematics (GaitSD). Fixed effects were group (CP vs. TD), condition (walking in the real world vs. virtual world) and virtual height.

GaitSD and SWV were positively correlated with VR, and lateral MoS was negatively correlated. Lateral MoS and coronal WBAM were positively correlated with CP and VR, while anterior MoS and sagittal WBAM were negatively correlated. Lateral MoS was positively correlated with height in CP, while anterior MoS, sagittal WBAM, SLS, SLV and SWV were negatively correlated.

Virtual heights induced gait instability, which was more pronounced in CP than TD. WBAM, SLV, and SLS were the most appropriate biomarkers for defining gait instability.

Children's motor development can be evaluated through the analysis of gait temporal parameters and their variability. This requires the detection of gait events in a real-world environment, which can be achieved using inertial measurement units. Algorithms have been previously developed for healthy adults; however, the performance of these algorithms in the detection of gait events in toddlers has not been analysed.

Can inertial measurement units be used to analyse gait temporal parameters in toddlers?

Fifteen previously published algorithms using sensors attached on the lower-back or the ankles were used to identify gait events and calculate gait temporal parameters. A total of 1388 initial and 1388 final foot contacts collected from 15 toddlers were included in the analysis. The performance of the algorithms was compared against a GAITRite mat in terms of accuracy and precision. Accuracy in the measurement of gait temporal parameters was evaluated using Bland Altman limits of agreement for repeated measurements, and precision was assessed through the evaluation of correctly identified, falsely identified and missed events.

From our results, no algorithm emerged as a best option from all those analysed. Algorithms using the ankle sensors provide higher accuracy and perfect precision when using only angular velocity about the medio-lateral axis. The best algorithms using the sensor attached at the lower-back use the resultant or global acceleration that reduces the effect of the sensor's alignment. These lower-back-based algorithms compared to the best ankle-based ones have similar accuracy for the calculation of stride time and higher accuracy for step time; however, they do not have perfect precision.

Inertial measurement units can support research analysing the temporal parameters of toddlers' gait in controlled environments, and may allow future studies in natural, free-living environments that can improve the monitoring of gait in young children.

The dynamic nature of gait heightens the risk of falling. Treadmill-based protocols are used to assess dynamic stability as they allow for uninterrupted gait. However, walking on treadmills differs from overground and individuals require time to adapt to the treadmill before reaching a steady-state gait. While familiarization was examined for gait kinematics, it remains uninvestigated for dynamic stability. As dynamic stability metrics quantify aspects of neuromuscular control, altered sensorimotor input from the treadmill would require familiarization to avoid confounding factors in the interpretation of fall risk. Dynamic stability metrics were assessed for twenty healthy young adults (18-30yrs) during two 10-min sessions of treadmill walking separated by one week. No familiarization in the mediolateral direction occurred but fluctuations in the anteroposterior direction for the Harmonic Ratio (session 1) and Margin of Stability (session 2) occurred. Fluctuations may reflect different strategies used to adjust to the treadmill. Specifically, participants altered step length and upper body posture in session 1 and 2, respectively. This may indicate that more than ten minutes are necessary for dynamic stability metrics to reach a steady-state. Further, treadmill exposure may modulate the motor strategies used to adjust dynamic stability during familiarization periods.

On average, people with dementia fall more often than their age-matched peers, with serious consequences, yet the underlying reasons remain poorly understood. This narrative review explores relevant psychological, physiological and neuroimaging studies to discuss whether diminished inhibitory control contributes to poor balance and falls in people with Alzheimer's Disease (AD), the most common form of dementia. Inhibitory control, a component of executive function, plays a vital role in suppressing dominant impulses or actions and regulating attention in favour of a desired outcome. Although objective tests of inhibitory control are not routinely used in clinical settings, research suggests inhibitory control declines early, and progressively, in AD. Postural tasks that require inhibitory control can improve the accuracy of distinguishing fallers from non-fallers beyond known factors. Neuroimaging studies link the prefrontal cortex to both inhibitory and postural control, and this region exhibits neuronal loss early in AD. Thus, emerging evidence suggests that accurately assessing inhibitory control could not only improve falls risk predictions but also aid AD detection.

This randomized clinical trial evaluated the impact of immediate weight-bearing as tolerated on spatiotemporal gait parameters and fall risk in patients undergoing postoperative rehabilitation for tibial plateau fractures. A total of 106 patients who had undergone open reduction and internal fixation (ORIF) for Schatzker I-IV tibial plateau fractures were recruited, with 39 meeting the inclusion criteria and 10 lost to follow-up. Patients were randomly assigned to a non-weight-bearing group (NWB), following a 6-week non-weight-bearing rehabilitation protocol, or a weight-bearing group (WB), allowed immediate weight-bearing. Both groups received the same therapeutic exercise program. Gait parameters were assessed three months post-surgery, including step length, stride length, single stance time, double stance time, step time, stride time, velocity, cadence, stride width, and gait and balance scores from Tinetti Performance Oriented Mobility Assessment (POMA). Of the 29 patients who completed the study, significant differences in favor of the WB group were observed for affected limb step length (p = 0.010), sound limb step length (p = 0.013), stride length (p = 0.010), affected single limb stance time (p = 0.001), sound single limb stance time (p = 0.007), velocity (p = 0.021), and POMA scores for balance (p = 0.021) and gait (p = 0.002). Immediate weight-bearing as tolerated after ORIF for Schatzker I-IV tibial plateau fractures resulted in improved spatiotemporal gait parameters and reduced fall risk.

BackgroundAnkle osteoarthritis (AOA) is a prevalent condition that affects joint function, often leading to pain, inflammation, and impaired mobility, significantly impacting patients' quality of life.ObjectiveThis study explores the effects of electroacupuncture treatment on clinical outcomes and gait characteristics in patients with ankle osteoarthritis (AOA).MethodsA total of 78 patients with AOA were randomly divided into a experiment group and a control group. The control group was treated with strength training, and the experiment group was additionally treated with electroacupuncture. The Ankle Osteoarthritis Scale (AOS), American Foot and Ankle Society's Ankle and Hindfoot Scale (AOFAS-AHS), and the 3D Motion Analysis System were used before and after the intervention to assess the clinical outcomes and changes in kinematic parameters in the AOA patients before and after therapy.ResultsAfter treatment, the differences in intra-group comparisons and inter-group comparisons of AOS scores, AOFAS-AHS scores, stride length, stride length, single-support phase, and gait variable scores (GVS) of ankle dorsiflexion-plantarflexion of the patients in the experiment group were statistically significant; the differences in inter-group comparisons of GVS scores of hip rotation of the patients in the experiment group were statistically significant; and the differences in the gait profile scores (GPS) and gait deviation indices of the patients in the experiment group were statistically significant. The differences in the intra-group comparisons were statistically significant. The within-group comparisons of AOS score, AOFAS-AHS score, GPS score and GVS score of ankle dorsiflexion-plantarflexion were statistically significant in the control group patients.ConclusionsElectroacupuncture has shown promise as an adjunctive therapy for patients with AOA, offering a more holistic rehabilitation strategy. By improving joint mobility and optimizing gait mechanics, electroacupuncture can effectively reduce pain, facilitate the restoration of normal gait patterns, and enhance patients' overall quality of life.

Habitual moderate intensity walking has disease-modifying benefits in Parkinson's disease (PD). However, the lack of sustainable gait interventions that collectively promote sufficient intensity, daily amount, and quality of walking marks a critical gap in PD rehabilitation. In this randomized controlled trial (clinicaltrials.gov#: NCT05421624, registered on June 6, 2022), we demonstrate the effectiveness of a real-world walking intervention delivered using an autonomous music-based digital rhythmic auditory stimulation (RAS) system. In comparison to an active-control arm (N = 20) of moderately intense brisk walking, the autonomous RAS system used in the experimental arm (N = 21) amplified moderate-to-vigorous walking intensities, increased daily steps, and improved (reduced) gait variability. While regular engagement in real-world walking with or without RAS each cultivated habits for walking, only the RAS intervention yielded a combination of strengthened habits and improved gait outcomes. Findings from this study supported the use of a personalized, autonomous RAS gait intervention that is effective, habit-forming and translatable to real-world walking in individuals with PD.

Few studies have adopted external focus strategies to mitigate the negative effects of conscious movement processing in older adults. We investigated whether a single-session intervention (SSI) using an external focus could improve gait stability and visual search behaviors during adaptive locomotion in older adults.

We randomly allocated 112 older adults to either an external focus (EXT, n = 56) or a control group (CON, n = 56). Participants performed an obstacle circumvention walking task along an 8-m walkway for five trials at pre-intervention (T0), post-intervention (T1), and retention (T2). The training phase consisted of 20 walking trials with obstacle circumvention. EXT focused on digits displayed on monitors at their path destinations during walking, while CON walked naturally without any specific instructions. Gait kinematics (i.e., gait variabilities and body sway) and visual search data were collected at T0, T1, and T2.

Only EXT reduced body sway and variability of spatial and temporal gait parameters, while increasing gait speed when comparing T1 and T2 to T0. EXT also reduced the number of visual fixation and fixation duration percentage on the ground while increasing fixation duration percentage on the destination when comparing T1 and T2 to T0.

This study is the first to explore SSI with an external focus in older adults, providing evidence of significant improvements in gait stability and visual search behaviors that facilitate feedforward planning. Practicing with an external focus strategy could be recommended as an adjunctive psychomotor approach in clinical settings to enhance visuomotor performance in older adults.

Several studies have observed a relationship between step length variability and the metabolic cost of walking. In those studies, changes in step length variability were secondary to changes in walking speed or step width variability. The purpose of this study was to determine how directly increasing step length variability affects the metabolic cost of walking. Eighteen healthy young adults completed 5 min trials of treadmill walking at 1.20 m s-1 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. Step length and its variability were tracked with reflective markers on the feet. Metabolic power across habitual (no projections) and the three variability conditions was measured using indirect calorimetry and analyzed using linear mixed effects modeling. Metabolic power was largest in the 10% condition (mean±s.d. 4.30±0.23 W kg-1) compared with 0% (4.16±0.18 W kg-1) and habitual (3.98±0.25 W kg-1). Actual step length variability was significantly different from prescribed conditions: 0%, 3.17±0.64%; 5%, 4.38±0.98% and 10%, 6.94±1.07%. For every 1% increase in step length variability, there was a 1.1% (0.05 W kg-1; P<0.001) increase in metabolic power. Our results demonstrate an association between the metabolic cost of walking and step length variability. This suggests that increased gait variability contributes to a small portion of the increased cost of walking seen in older adults and people with neurological impairments.

Multi-site pain is common in people aged 60 years and over and is associated with a high risk of falls. To prevent and treat pain-related disabilities, it is crucial to identify the mechanisms underlying these associations. There is some evidence that pain leads to changes in walking, such as slower gait speed and shorter walking distance, which impair mobility and may increase the risk of falls. This review evaluated evidence on the relationship between pain and gait characteristics in older people. A comprehensive search on PubMed and Embase included observational studies and clinical trials assessing objective measures of walking, such as gait speed, cadence, stride length, and double-limb support time, in older people with and without pain. Of the 1218 studies screened, thirteen met the inclusion criteria from the primary search. An additional study was identified through the secondary search, resulting in fourteen studies included in this systematic review and meta-analysis. None of these studies investigated the relationship between fear of pain and gait characteristics in older people. Results showed that older people with pain had slower gait speed than those without pain, with a small effect size (Hedge's g = -0.30, 95% CI = -0.41 to -0.19, p < 0.0001). There were no statistically significant differences in cadence, stride length, and double-limb support time. These findings suggest that pain impacts walking speed in older people, highlighting the importance of addressing this association to manage mobility deficits and fall risk. PERSPECTIVE: This systematic review and meta-analysis show that pain is associated with reduced gait speed in older people. Recognising and addressing the impact of pain on walking may be important for preventing mobility-related disorders and falls in this population.

The human knee joint is crucial for mobility, especially in older adults who are susceptible to conditions like osteoarthritis (OA). Traditionally, assessing knee health requires complex gait analysis in clinical settings, which limits opportunities for convenient and continuous monitoring. This study leverages advancements in wearable technology to explore the adaptation of models based on in-shoe motion sensors (IMS), initially trained on young adults, for evaluating knee function in older populations, both healthy and with OA. Data were collected from 44 older OA patients, presenting various levels of severity, and 20 healthy older adults, with a focus on key knee indicators: knee angle measures (S1 to S3), temporal gait parameters (S4 and S5), and knee angular jerk cost metrics (S6 to S8). The models effectively identified trends and differences across these indicators between the healthy group and the OA group. Notably, in indicators S1, S2, S3, S7, and S8, the models exhibited a large effect size in correlation with true values. These findings suggest that gait models derived from younger, healthy individuals are possible to be robustly adapted for non-invasive, everyday monitoring of knee health in older adults, offering valuable insights for the early detection and management of knee impairments. However, limitations such as fixed biases due to differences in measurement systems and sensor placement inaccuracies were identified. Future research will aim to enhance model precision by addressing these limitations through domain adaptation techniques and improved sensor calibration.

This narrative review examines the utility of gait digital biomarkers in Parkinson's disease (PD) research and clinical trials across four contexts: disease susceptibility/risk, disease progression, response to exercise, and fall prediction. The review of the literature to date suggests that upper body characteristics of gait (e.g., arm swing, trunk motion) may indicate susceptibility/risk of PD, while pace aspects (e.g., gait speed, stride length) are informative for tracking disease progression, exercise response, and fall likelihood. Dynamic stability aspects (e.g., trunk regularity, double-support time) worsen with disease progression but can improve with exercise. Gait variability emerges as a sensitive biomarker across all 4 contexts but with low specificity. The lack of standardized gait testing protocols and the lack of a minimum set of quantified digital gait biomarkers limit data harmonization across studies. Future studies, using a commonly agreed upon protocol, could be used to demonstrate the utility of specific gait biomarkers for clinical practice.

Neurodegenerative disorders (NDD) represent a broad spectrum of diseases that progressively impact neurological function, yet available therapeutics remain conspicuously limited. They lead to altered rhythms and dynamics of walking, which are evident in the sequential footfall contact times measured from one stride to the next. Early detection of aberrant walking patterns can prevent the progression of risks associated with neurodegenerative diseases, enabling timely intervention and management. In this study, we propose a new methodology based on a parallel-path ConvMixer neural network for neurodegenerative disease classification from gait analysis. Earlier research in this field depended on either gait parameter-derived features or the ground reaction force signal. This study has emerged to combine both ground reaction force signals and extracted features to improve gait pattern analysis. The study is being carried out on the gait dynamics in the NDD database, i.e., on the benchmark dataset Physionet gaitndd. Leave one out cross-validation is carried out. The proposed model achieved the best average rates of accuracy, precision, recall, and an F1-score of 97.77 % , 96.37 % , 96.5 % , and 96.25 % , respectively. The experimental findings demonstrate that our approach outperforms the best results achieved by other state-of-the-art methods.

Walking instability increases the risk of falls and compromises mobility safety. This study aimed to explore the impact of various percentages of preferred walking speed (PWS)-specifically, 40%, 55%, 70%, 85%, 100%, 115%, 130%, and 145%-along with age-related changes, on walking stability during treadmill walking. Kinematic marker data from all walking speed trials were pooled for analysis, involving a total of 26 participants (13 young adults aged 24.7 ± 2.4 years and 13 older adults aged 60.8 ± 6.4 years). These pooled data were then decomposed into various movement components (i.e., movement strategies), known as principal movements (PMs), using principal component analysis (PCA). These PMs, which resemble the phases of a gait cycle, collectively contribute to the accomplishment of the walking task. The participant-specific largest Lyapunov exponent (LyE) was employed to assess the local dynamic stability of individual PMs, with lower LyE values indicating higher stability, thereby allowing for the examination of walking speed and age effects. The main findings revealed that only the effects of altered walking speeds were observed; specifically, the LyE value for the midstance phase (PM3) at 100% of PWS was significantly lower than at 40% of PWS (p=0.001), and there was a trend indicating that the LyE value at 100% of PWS was also lower than at 140% of PWS (p=0.027). These results suggest that PWS enhances the stability of the mid-stance-phase movement component of the gait cycle more than the slower and faster walking speeds during treadmill walking.

Gait complexity is considered an indicator of adaptability, reflecting the complex interaction between multiple components of the neuromuscular system. Previous research provided evidence that chronobiology, which reflects the individual expression of circadian rhythms, affects the regulation of gait dynamics. The literature also suggests the disruption of these circadian rhythms affects multiple human physiological systems. Considering the association between chronobiology and gait complexity, and its clinical relevance, it would be important to investigate whether the disruption of sleep-wake cycle could affect gait complexity. This study aimed to investigate the effect of 1 night of sleep deprivation on gait complexity and variability of healthy individuals, exploring potential implications for motor control. Seventeen healthy and young male adults underwent an in-lab supervised 24-hr sleep deprivation protocol, with gait complexity and variability assessed using detrended fluctuation analysis and coefficient of variation, respectively. Chronotype was also assessed through the Morningness-Eveningness Questionnaire. We observed a loss of gait complexity with sleep deprivation (PRE: 0.8 ± 0.13; POST24: 0.62 ± 0.08, p < 0.001), while gait variability remained unaltered (p = 0.132). Additionally, we demonstrated an association between gait complexity's relative changes and chronotype (r = -0.665, p = 0.004). Overall, our findings suggest sleep deprivation induces a decrease in the neuromuscular system's ability to flexibly adapt gait output. Moreover, we also highlight the importance of chronobiology in motor control, as we observed the more morning-type an individual is, the greater the loss of complexity following 1 night of sleep deprivation. Altogether, our findings underscore the potential impact of sleep deprivation on central processes underlying gait complexity.

Background: Gait disturbances characterized by asymmetries in lower limb strength and gait patterns are frequently observed in stroke patients, which increases gait variability and fall risk. However, the extent to which lower limb strength asymmetry influences gait asymmetry and variability in this population remains unclear. Methods: This cross-sectional study included 84 participants, comprising stroke survivors and age- and sex-matched healthy older adults. A portable dynamometer was used to assess lower limb strength, and inertial measurement units to analyze gait parameters. Asymmetry indices were used to quantify strength and gait asymmetries. Statistical analyses included Pearson correlations and stepwise regression to examine the relationships among lower limb strength asymmetry, gait asymmetry, and gait variability. Results: Stroke survivors exhibited significantly greater lower limb strength and gait asymmetries than healthy older adults (p < 0.001). Knee extension (KE) strength asymmetry was a significant predictor of increased gait variability in stroke survivors (R2 = 0.448, p < 0.001) but not in healthy controls. Moreover, longer poststroke duration was associated with greater asymmetry in KE strength (r = 0.42, p < 0.05) and double support time (r = 0.45, p < 0.05). Conclusions: Lower limb strength asymmetry, specifically in knee extensors, is a critical determinant of gait asymmetry and variability in stroke survivors. The association between poststroke duration and increased asymmetry indicates the progressive nature of these impairments. These findings emphasize the importance of targeted interventions to reduce strength asymmetry and address chronic impairments in poststroke rehabilitation to improve gait stability and reduce fall risk.

Instrumented gait analysis is widely used in clinical settings for the early detection of neurological disorders, monitoring disease progression, and evaluating fall risk. However, the gold-standard marker-based 3D motion analysis is limited by high time and personnel demands. Advances in computer vision now enable markerless whole-body tracking with high accuracy. Here, we present vGait, a comprehensive 3D gait assessment method using a single RGB-D sensor and state-of-the-art pose-tracking algorithms. vGait was validated in healthy participants during frontal- and sagittal-perspective walking. Performance was comparable across perspectives, with vGait achieving high accuracy in detecting initial and final foot contacts (F1 scores > 95%) and reliably quantifying spatiotemporal gait parameters (e.g., stride time, stride length) and whole-body coordination metrics (e.g., arm swing and knee angle ROM) at different levels of granularity (mean, step-to-step variability, side asymmetry). The flexibility, accuracy, and minimal resource requirements of vGait make it a valuable tool for clinical and non-clinical applications, including outpatient clinics, medical practices, nursing homes, and community settings. By enabling efficient and scalable gait assessment, vGait has the potential to enhance diagnostic and therapeutic workflows and improve access to clinical mobility monitoring.

Huntington's disease (HD) is characterized by motor, cognitive, and neuropsychiatric symptoms. Oculomotor impairments and gait variability have been independently considered as potential markers in HD. However, an integrated analysis of eye movement and gait is lacking. We performed multiple examinations of eye movement and gait variability in HTT mutation carriers, analyzed the consistency between these parameters and clinical severity, and then examined the associations between oculomotor impairments and gait deficits.

We included 7 patients with pre-HD, 30 patients with HD and 30 age-matched controls. We collected demographic data and assessed the Unified Huntington's Disease Rating Scale (UHDRS) score. Examinations, including saccades, smooth pursuit tests, and optokinetic (OPK) tests, were performed to evaluate eye movement function. The parameters of gait include stride length, walking velocity, step deviation, step length, and gait phase.

HD patients have significant impairments in the latency and velocity of saccades, the gain of smooth pursuit, and the gain and slow phase velocities of OPK tests. Only the speed of saccades significantly differed between pre-HD patients and controls. There are significant impairments in stride length, walking velocity, step length, and gait phase in HD patients. The parameters of eye movement and gait variability in HD patients were consistent with the UHDRS scores. There were significant correlations between eye movement and gait parameters.

Our.

show that eye movement and gait are impaired in HD patients and that the speed of saccades is impaired early in pre-HD. Eye movement and gait abnormalities in HD patients are significantly correlated with clinical disease severity.

The Gait Deviation Index (GDI) is a metric clinicians use to assess overall gait pathology in children with cerebral palsy (CP) by comparing kinematic data to a normative sample. The Gait Variability Index (GVI) is a related metric that quantifies the variability in spatio-temporal variables during gait. The GDI and GVI have been verified using marker-based motion capture approaches, but video-based markerless motion capture has not been compared using these tools in children with CP.

Do GDI and GVI scores differ when measured using markerlessTheia3D and a marker-based approach between the more and less affected legs in children with CP?

Fifteen children with CP (GMFCS levels I-IV) and 24 typically developing children aged 6-18 years were recruited for this study. Overground walking was performed at a self-selected pace while the pelvis and lower limb kinematics were simultaneously recorded using both motion capture systems. Differences in GDI and GVI scores when considering the effect of system and limb impairment were analyzed using two-way repeated-measures ANOVAs.

GDI scores were 6.9 points lower (p < 0.05) when measured using Theia3D compared to the marker-based approach and 6.8 points lower (p < 0.05) in the more affected limbs than in the less affected limbs. These GDI score differences are considered clinically significant. No differences were identified in GVI scores between systems or limb impairment. Differences in kinematic measurements were found in children with CP, including pelvic tilt, hip flexion/extension, hip rotation, and foot progression angle, where root mean square differences between systems exceeded 10°.

Theia3D can adequately measure variability in spatio-temporal gait parameters for quantifying GVI scores in children with CP compared to the marker-based approach. However, caution is needed when quantifying lower limb kinematics and interpreting GDI and GVI scores using Theia3D in children with CP.

While over 60 % of adults with stroke fall each year, the risk is greatest in high-functioning individuals with mild motor impairments and greater physical mobility. We lack sensitive predictors of falls in this population. Therefore, our study aimed to determine the relative contribution of gait variability and widely used tests of balance and mobility in predicting real-life falls in high-functioning adults with stroke.

Twenty-four adults with stroke who had the ability to walk independently, Fugl-Meyer lower-extremity score of ≥19/34, and Frenchay Activities Index ≥16/45 performed overground walking, Timed-up and go, and Berg balance scale. We quantified the gait speed, and gait variability for stride length and stride time. We recorded the history of falls in the past one year.

The incidence rate of past falls was 50 %. Stride length variability and Berg balance scale score were associated with previous falls in univariate analyses and were subsequently included in the multivariate model. Multivariate analyses showed that only stride length variability significantly predicted past falls (OR = 2.73, 95 % CI 1.05-7.08, p = 0.03). A cut-off of 3.98 % for stride length variability had 75 % sensitivity and 91.7 % specificity in predicting previous falls (AUC = 0.83, 95 % CI 0.64-1.00, p < 0.001).

In high-functioning adults with stroke, stride length variability during overground walking is a strong predictor of the past incidence of falls compared with traditional balance and mobility tests. Our findings highlight the importance of gait variability in accurately determining fall risk among high-functioning post-stroke individuals.

This study presents a novel framework that utilizes instrumented footwear to predict fall risk in institutionalized older adults by leveraging stride-to-stride gait data. The older adults are categorized into fallers and non-fallers using three distinct criteria: retrospective fall history, prospective fall occurrence, and a combination of both retrospective and prospective data. Three types of data collected from N=95 institutionalized older adults are analyzed: traditional timed mobility tests, gait data collected from a validated electronic walkway, and gait data collected with instrumented footwear developed by our team. The importance of each type of data is assessed using a brute-force search method, through which the optimal features are selected. AdaBoost algorithms are then utilized to develop predictive models based on the selected features. The models are evaluated using leave-one-out cross-validation and 10-fold cross-validation. The results show that models using gait data from the instrumented footwear outperformed those based on traditional tests and walkway data, with area under the receiver operating characteristic curve (AUC) values for predicting prospective falls being 0.47, 0.66, and 0.80, respectively. The sensitivity of the models increases when they are trained using both past and future falls data, rather than relying solely on past or future falls data. This study demonstrates the potential of instrumented footwear for fall risk assessment in elderly individuals. The findings provide valuable insights for fall prevention and care, highlighting the superior predictive capabilities of the developed system compared to traditional methods.

Traditionally, canoeing analyses have always been carried out by calculating mean values over a certain distance. However, a simple analysis of mean values in cyclic actions, such as canoe paddling, can lead to the loss of decisive data. Therefore, the objective of this study was to investigate whether the relationship between the periods of consecutive strokes can be an important aspect in all-out 200-m canoeing performance. A total of 22 young male canoeists aged 13.51 ± 0.43 years old voluntarily participated in the present study. Lateral recordings were taken from all participants when performing an all-out 200 m to subsequently analyse the kinematic variables of time, speed and cycle frequency, length and index. The time of each stroke was calculated by the frame differencesbetween two successive paddles. Likewise, short-term and long-term steadiness of consecutive strokes were analysed. Positive correlations were observed between all-out 200-m time and inter-stroke steadiness, identifying significant associations with long-term (r ≈ 0.8; p < 0.05) and short-term steadiness (r ≈ 0.7; p < 0.05). In conclusion, the present investigation defines and identifies inter-stroke steadiness as a new original kinematic variable that might be considered for performance analysis since it seems to be a key factor in all-out 200-m canoeing.

Patients presenting with ataxia are at high risk of falling, however, there are limited studies evaluating fall factors restricted to patients presenting with ataxia due to stroke.

This study aimed to examine the characteristics of patients with ataxia after stroke based on their motor function to identify variables associated with fall occurrence.

We divided 33 participants who presented with ataxia after stroke into fall and non-fall groups. Data on motor function, cognitive function, and daily functionality were extracted from their admission records. Walking ability was measured as comfortable walking speed and Stride Time Variability (STV). Independent sample t-tests, Mann - Whitney U tests, and multiple logistic regression analysis were performed.

There were significant differences between fallers and non-fallers in the STV (p < .001), Mini-Balance Evaluations Systems Test score (p < .014) and Scale for Assessment and Rating of Ataxia score assessment (p < .028). In the multiple logistic regression analysis, only STV was associated with an increased risk of falls (p < .02). The area under the receiver operating characteristic curve was 0.839; the cutoff value of gait cycle variability for falls was 6.345% (sensitivity, 80.0%; specificity, 74.0%).

Increased stride time variability is a useful indicator that sensitively captures fall risk in patients with ataxia after stroke.

Dual-task paradigms, which involve performing cognitive and motor tasks simultaneously, are commonly used to study how attentional resources are allocated and managed under varying task demands. This study aimed to investigate cognitive-motor interferences (CMI) under different levels of cognitive and motor task difficulty without instruction on task prioritization. 17 healthy young adults performed an auditory oddball task with increasing cognitive and motor (walking vs. sitting) difficulty. Cognitive and motor performances, along with P3 (P3a and P3b) brainwave components, were analysed. Increasing cognitive difficulty resulted in more errors and increased P3a amplitude, reflecting enhanced attentional demand, while P3b remained unaffected. This suggests a threshold effect on attentional resources. Motor complexity lengthened P3a and P3b latencies without affecting amplitude, indicating delayed attentional resource recruitment. Additionally, walking with the most difficult cognitive task increased cognitive error, suggesting attentional resource limits. With increased motor and cognitive complexity, CMI emerged, leading to cognitive error increase and improved gait stability without amplitude changes in P3a and P3b. Two hypotheses were proposed: motor prioritization and motor facilitation. Our study suggests managing attentional resources to balance cognitive and motor tasks rather than linearly increasing task complexity. Viewing dual tasks as a new, integrated task is proposed, supported by previous neural network integration studies. Thus, understanding how the brain organizes tasks in response to constraints is crucial for comprehending complex task execution.

The attractor complexity index (ACI) is a recently developed gait analysis tool based on nonlinear dynamics. This study assesses ACI's sensitivity to attentional demands in gait control and its potential for characterizing age-related changes in gait patterns. Furthermore, we compare ACI with classical gait metrics to determine its efficacy relative to established methods. A 4 × 200 m indoor walking test with a triaxial accelerometer attached to the lower back was used to compare gait patterns of younger (N = 42) and older adults (N = 60) during normal and metronome walking. The other linear and non-linear gait metrics were movement intensity, gait regularity, local dynamic stability (maximal Lyapunov exponents), and scaling exponent (detrended fluctuation analysis). In contrast to other gait metrics, ACI demonstrated a specific sensitivity to metronome walking, with both young and old participants exhibiting altered stride interval correlations. Furthermore, there was a significant difference between the young and old groups (standardized effect size: -0.77). Additionally, older participants exhibited slower walking speeds, a reduced movement intensity, and a lower gait regularity. The ACI is likely a sensitive marker for attentional load and can effectively discriminate age-related changes in gait patterns. Its ease of measurement makes it a promising tool for gait analysis in unsupervised (free-living) conditions.

Understanding the intricacies of human movement coordination and variability during running is crucial to unraveling the dynamics of locomotion, identifying potential injury mechanisms and understanding skill development. Identification of minimum number of cycles for calculation of reliable coordination and its variability could help with better test organization and efficient assessment time. By adopting a cross-sectional study design, this study investigated the minimum required cycles for calculating hip-knee, hip-ankle and knee-ankle coordination and their variability using a continuous relative phase (CRP) method. Twenty-nine healthy adults ran on a treadmill at speeds of 9, 12.5, and 16 km.h-1 while 3D kinematic data of their lower limbs were recorded using 6 optoelectronic cameras. Using Intraclass Correlation Coefficient (ICC) analysis, reliability between CRP and its variability (CRPv) in different gait cycles (3, 5, 10, 20, 30) was assessed for each speed. A minimum of 10 cycles was required for CRP calculation across all speeds, whereas CRPv necessitated a minimum of 30 cycles for moderate to good reliability. While increasing the number of cycles improved ICC values for inter-joint CRP, the same trend was not consistently observed for CRPv, emphasizing the importance of separately assessing CRP and its variability metrics.

Although high-frequency deep brain stimulation (DBS) is effective at relieving many motor symptoms of Parkinson's disease (PD), its effects on gait can be variable and unpredictable. This is due to 1) a lack of standardized and robust metrics for gait assessment in PD patients, 2) the challenges of performing a thorough evaluation of all the stimulation parameters space that can alter gait, and 3) a lack of understanding for impacts of stimulation on the neurophysiological signatures of walking. In this study, our goal was to develop a data-driven approach to identify optimal, personalized DBS stimulation parameters to improve gait in PD patients and identify the neurophysiological signature of improved gait. Local field potentials from the globus pallidus and electrocorticography from the motor cortex of three PD patients were recorded using an implanted bidirectional neural stimulator during overground walking. A walking performance index (WPI) was developed to assess gait metrics with high reliability. DBS frequency, amplitude, and pulse width on the "clinically-optimized" stimulation contact were then systemically changed to study their impacts on gait metrics and underlying neural dynamics. We developed a Gaussian Process Regressor (GPR) model to map the relationship between DBS settings and the WPI. Using this model, we identified and validated personalized DBS settings that significantly improved gait metrics. Linear mixed models were employed to identify neural spectral features associated with enhanced walking performance. We demonstrated that improved walking performance was linked to the modulation of neural activity in specific frequency bands, with reduced beta band power in the pallidum and increased alpha band pallidal-motor cortex coherence synchronization during key moments of the gait cycle. Integrating WPI and GPR to optimize DBS parameters underscores the importance of developing and understanding personalized, data-driven interventions for gait improvement in PD.

The dynamical nature of gait increases fall risk for older adults as the Center of Mass (COM) is constantly displaced inside and outside the Base of Support (BOS). Foot placement and leg joint moments are the primary mechanisms controlling dynamic balance. The Margin of Stability (MOS) quantifies the distance between the COM dynamical state and the BOS. While research examined how aging affects the relationship between foot placement and MOS, the relationship to leg moments is unexamined. Examining this relationship would elucidate whether aging increases fall risk from changes in the joint moments controlling the COM. Fourteen older (66.9 ± 4.3 years) and sixteen young (26.3 ± 3.6 years) adults walked along a 12m path for three trials. The MOS, hip and ankle moments in sagittal and frontal planes were analyzed. For the knee, only the sagittal plane was analyzed. MOS was calculated as the distance between the extrapolated-COM and the Center of Pressure per step. Statistical Parametric Mapping independent t-tests assessed group differences. Cross-correlation quantified MOS and joint moment relationships per plane during single-stance. No group differences in walking speed were observed. A larger frontal plane MOS, hip abduction and ankle eversion moment occurred for older adults. Cross-correlation demonstrated moderate and strong relationships for the hip-MOS for both groups in the sagittal plane. Older adults had a larger sagittal plane hip-MOS correlation than young adults. The larger mediolateral MOS in older adults may indicate attempts to avoid lateral balance loss by shifting their COM away from their BOS lateral boundaries during single-stance. However, this strategy moves the COM toward the BOS medial borders potentially pre-maturely terminating the contralateral swing phase during medial destabilization. The stronger sagittal plane hip-MOS relationship in older adults may reflect increased coupling between hip moments and the COM to control dynamic balance.

This research aimed to develop an algorithm for classifying scalogram images generated from electromyography data of patients with Rheumatoid Arthritis and Prolapsed Intervertebral Disc. Electromyography is valuable for assessing muscle function and diagnosing neurological disorders, but limitations, such as background noise, cross-talk, and inter-subject variability complicate the interpretation and assessment. To mitigate this, the present study uses scalogram images and attention-network architecture. The algorithm utilises a combination of features extracted from an attention module and a convolution feature module, followed by classification using a Convolutional Neural Network classifier. A comparison of eight alternative architectures, including individual implementations of attention and convolution filters and a Convolutional Neural Network-only model, shows that the hybrid Convolutional Neural Network model proposed in this study outperforms the others. The model exhibits excellent discriminatory ability between gait abnormalities with an accuracy of 96.7%, a precision of 95.2%, a recall of 94.8%, and an Area Under Curve of 0.99. These findings suggest that the proposed model is highly accurate in classifying scalogram images of electromyography signals and may have significant clinical implications for early diagnosis and treatment planning.

Aim Older inpatients have reduced physical function and walking ability with a higher risk of falls after being discharged home. Gait variability can assess ambulation and is strongly related to the risk of falls. However, the clinical factors affecting gait variability in inpatients have not been identified. The purpose of this study was to investigate the predictive factors affecting gait variability in older inpatients. Methods A total of 42 older orthopedic inpatients with fractures of the hip, spine, and other segments and 18 healthy volunteers as the control group were enrolled in this study. Inpatients wore tri-axial accelerometers for a 10m walk before discharge. Gait variability was assessed by the coefficient of variation (CV) based on five consecutive stride times. Clinical assessment of muscle strength, joint mobility, balance, pain, and activities of daily living were also evaluated. Results The CV in inpatients was higher than that in healthy elderly. Quadriceps muscle strength, ankle dorsiflexion range of motion, and balance described the CV. When model 2 (adjusted R2 = 0.473) was compared with model 1 (adjusted R2 = 0.293), the quadriceps muscle strength and ankle dorsiflexion range of motion had a major effect on CV, while balance had not a greater influence than these two factors when compared with model 3 (adjusted R2 = 0.537). Conclusions Poor knee extension strength, balance, and restriction of ankle dorsiflexion mobility have influenced gait variability in older inpatients.

The inpatient Parkinson's Disease Multimodal Complex Treatment (PD-MCT) is an important therapeutical approach to improving gait and activities of daily living (ADL) of people with PD (PwP). Wearable device-based parameters (DBP) are new options for specific gait analyses toward individualized treatments.

We sought to identify predictors of perceived ADL benefit taking clinical scores and DBP into account. Additionally, we analyzed DBP and clinical scores before and after PD-MCT.

Exploratory observational cohort study.

Clinical scores and DBP of 56 PwP (mean age: 66.3 years, median Hoehn and Yahr (H&Y) stage: 2.5) were examined at the start and the end of a 14-day inpatient PD-MCT in a German University Medical Center. Participants performed four straight walking tasks under single- and dual-task conditions for gait analyses. Additionally, clinical scores of motor and nonmotor functions and quality of life (QoL) were assessed. Using dichotomized data of change in Movement Disorders Society Unified Parkinson's Disease Rating Scale Part II (MDS-UPDRS II) as a dependent variable and clinical and DBP as independent variables, a binomial logistic regression model was implemented.

Young age, high perceived ADL impairment at baseline, high dexterity skills, and a steady gait were significant predictors of ADL benefit after PD-MCT. DBP like gait speed, number of steps, step time, stance time, and double limb support time were improved after PD-MCT. In addition, motor functions (e.g., MDS-UPDRS III and IV), QoL, perceived ADL (MDS-UPDRS II), and experience of nonmotor functions (MDS-UPDRS I) improved significantly.

The logistic regression model identified a group of PwP who had the most probable perceived ADL benefit after PD-MCT. Additionally, gait improved toward a faster and more dynamic gait. Using wearable technology in context of PD-MCT is promising to offer more personalized therapeutical concepts.

German Clinical Trial Register, https://drks.de; DRKS00020948 number, 30 March 2020, retrospectively registered.

Hip and knee osteoarthritis (OA) patients demonstrate distinct gait patterns, yet detecting subtle abnormalities with wearable sensors remains uncertain. This study aimed to assess a predictive model's efficacy in distinguishing between hip and knee OA gait patterns using accelerometer data.

Participants with hip or knee OA underwent overground walking assessments, recording lower limb accelerations for subsequent time and frequency domain analyses. Logistic regression with regularization identified associations between frequency domain features of acceleration signals and OA, and k-nearest neighbor classification distinguished knee and hip OA based on selected acceleration signal features.

We included 57 knee OA patients (30 females, median age 68 [range 49-89], median BMI 29.7 [range 21.0-45.9]) and 42 hip OA patients (19 females, median age 70 [range 47-89], median BMI 28.3 [range 20.4-37.2]). No significant difference could be found in the time domain's averaged shape of acceleration signals. However, in the frequency domain, five selected features showed a diagnostic ability to differentiate between knee and hip OA. Using these features, a model achieved a 77 % accuracy in classifying gait cycles into hip or knee OA groups, with average precision, recall, and F1 score of 77 %, 76 %, and 78 %, respectively.

The study demonstrates the effectiveness of wearable sensors in differentiating gait patterns between individuals with hip and knee OA, specifically in the frequency domain. The results highlights the promising potential of wearable sensors and advanced signal processing techniques for objective assessment of OA in clinical settings.

Many children with cerebral palsy (CP) show impairments in trunk posture control, one crucial factor contributing to impairments in gait and arm manipulation.

The goal of this study was to test the feasibility of the cable-driven hippotherapy system on improving trunk posture control and walking function in children with CP.

Ten children were recruited in this study with average age 6.4 ± 3.0 years old, and were randomly assigned to the robotic group and the conventional balance training group. A custom designed cable-driven robotic hippotherapy system was used to deliver controlled pelvis perturbations while children were sitting astride. Participants from both groups underwent intensive robotic hippotherapy training or conventional balance training, depending on their group assignment, for 6 weeks (3 time/week). Outcome measures were assessed pre and post 6 weeks of robotic or conventional balance training, and 8 weeks after the end of training (follow-up test). The primary outcome measure was trunk control, which was measured using the Trunk Control Measurement Scale (TCMS). In addition, the Gross Motor Function Measure (GMFM-66), self-selected overground walking speed, and 6-minute walking distance were also assessed.

The change in TCMS score from baseline to the post intervention was significantly greater for the robotic group than that for the conventional group (i.e., 6 ± 3 vs. -1 ± 5, p = 0.048, Mann-Whitney test). GMFM scores, self-selected overground walking speed, and 6-minute walking distance showed no significant improvement after robotic hippotherapy and after conventional balance training (p > 0.05).

Results from this study indicated that it was feasible to improve trunk posture control in children with CP using a cable-driven robotic hippotherapy system. Knowledge obtained from this study may provide an insight for the development of new perturbation-based intervention approaches for improving trunk posture control in children with CP.

Changes in spatio-temporal gait parameters and their variability during balance-challenging tasks are markers of motor performance linked to fall risk. Radio frequency (RF) sensors hold great promise towards achieving continuous remote monitoring of these parameters.

To establish the concurrent validity of RF-based gait metrics extracted using micro-Doppler (µD) signatures and to determine whether these metrics are sensitive to gait modifications created by multidirectional visual perturbations.

Fifteen participants walked overground in a virtual environment (VE) and VE with medio-lateral (ML) and antero-posterior (AP) perturbations. An optoelectronic motion capture system and one RF sensor were used to extract the linear velocity of the trunk and estimate step time (ST), step velocity (SV), step length (SL), and their variability (STV, SVV, and SLV). Intra-class coefficient for consistency (ICC), mean and standard deviation of the differences (MD), 95 % limits of agreement, and Pearson correlation coefficients (r) were used to determine concurrent validity. One-way repeated-measures analysis of variance was used to analyze the main and interaction effects of visual conditions.

All outcomes showed good to excellent reliability (r>0.795, ICC>0.886). Average gait parameters showed good to excellent agreement, with values obtained with the RF sensor systematically smaller than the values obtained with the markers (MD of 0.001 s, 0.09 m/s, and 0.06 m). Gait variability parameters showed poor to moderate agreement, with values obtained with the RF sensor systematically larger than those obtained with the markers (MD of 1.9 %-3.9 %). Both measurement systems reported decreased SL and SV during ML perturbations, but the gait variability parameters extracted with the radar were not able to detect the higher STV and SLV during this condition.

The radar µD signature is a valid and reliable method for the assessment of average spatio-temporal gait parameters but gait variability measures need to be viewed with caution because of the lower levels of agreement and sensitivity to ML visual perturbations. This work represents an initial investigation for the development of a low-cost system that will facilitate aging-in-place by providing remote monitoring of gait in natural settings.

A different interlimb coordination and higher variability in movement patterns is evident in children with Developmental Coordination Disorder (DCD). The impact of DCD on interlimb coordination during walking and running is unknown.

To assess interlimb coordination and spatiotemporal variability during overground walking and running in children with and without DCD.

Children with DCD and typically developing children (TDC), from 8 to 12 years participated. Children were equipped with portable sensors. Participants walked and ran for 3 min in an oval-path at their comfortable pace. Interlimb coordination, expressed by the phase coordination index (PCI), and spatiotemporal variability (coefficient of variance (CoV)) were collected.

Twenty-one children with DCD and 23 TDC participated. During walking, PCI showed similar values in both groups, but a higher spatiotemporal variability was observed in children with DCD. During running, PCI was higher (reduced coordination) in children with DCD than TDC and a higher spatiotemporal variability was shown.

Only during running, interlimb coordination of children with DCD was lower than TDC. During both walking and running tasks, spatiotemporal variability was higher in DCD. Current results implicate that difficulties in children with DCD is more prominent when motor coordination is more challenged.

This paper adds to the literature on coordination and gait pattern in children with Developmental Coordination Disorder (DCD) through a cross-sectional analysis of interlimb coordination and variability of spatiotemporal measures of overground walking and running. Overground walking and running were performed in a large oval-path allowing the assessment of coordination and gait patterns in an ecological valid set-up. Our results indicate that during a more demanding task, namely running, children with DCD display a less coordinated running pattern, expressed by a significantly higher phase coordination index, than typically developing peers. During walking, the interlimb coordination was similar between both groups. The current result is in accordance with the hybrid model of DCD that states that motor coordination difficulties in DCD are dpendent on the interaction of the task, individual and environment. This highlights the importance of implementing running assessments in children with DCD and the need for task-oriented running training in clinical practice The study also supports previous findings that children with DCD show a higher variability in their gait pattern of both walking and running, expressed by higher coefficient of variance of spatiotemporal measures, than typically developing peers. Further understanding in the normal development of interlimb coordination during walking and running from childhood into adulthood will enhance interpretations of the phase coordination index in children with and without DCD.

To detect early cognitive impairment in community-dwelling older adults, this study explored the viability of artificial intelligence (AI)-assisted linear acceleration and angular velocity analysis during walking.

This cross-sectional study included 879 participants without dementia (female, 60.6%; mean age, 73.5 years) from the 2011 Comprehensive Gerontology Survey. Sensors attached to the pelvis and left ankle recorded the triaxial linear acceleration and angular velocity while the participants walked at a comfortable speed. Cognitive impairment was determined using Mini-Mental State Examination scores. Deep learning models were used to discern the linear acceleration and angular velocity data of 12,302 walking strides.

The models' average sensitivity, specificity, and area under the curve were 0.961, 0.643, and 0.833, respectively, across 30 testing datasets.

AI-enabled gait analysis can be used to detect signs of cognitive impairment. Integrating this AI model into smartphones may help detect dementia early, facilitating better prevention.

Artificial intelligence (AI)-enabled gait analysis can be used to detect the early signs of cognitive decline.This AI model was constructed using data from a community-dwelling cohort.AI-assisted linear acceleration and angular velocity analysis during gait was used.The model may help in early detection of dementia.

Dual tasks (DTs) combining walking with a cognitive task can cause various levels of cognitive-motor interference, depending on which brain resources are recruited in each case. However, the brain activation and functional connectivity underlying cognitive-motor interferences remain to be elucidated. Therefore, this study investigated the neural correlation during different DT conditions in 40 healthy young adults (mean age: 27.53 years, 28 women). The DTs included walking during subtraction or N-Back tasks. Cognitive-motor interference was calculated, and brain activation and functional connectivity were analysed. Portable functional near-infrared spectroscopy was utilized to monitor haemodynamics in the prefrontal cortex (PFC), motor cortex and parietal cortex during each task. Walking interference (decrease in walking speed during DT) was greater than cognitive interference (decrease in cognitive performance during DT), regardless of the type of task. Brain activation in the bilateral PFC and parietal cortex was greater for walking during subtraction than for standing subtraction. Furthermore, brain activation was higher in the bilateral motor and parietal and PFCs for walking during subtraction than for walking alone, but only increased in the PFC for walking during N-Back. Coherence between the bilateral lateral PFC and between the left lateral PFC and left motor cortex was significantly greater for walking during 2-Back than for walking. The PFC, a critical brain region for organizing cognitive and motor functions, played a crucial role in integrating information coming from multiple brain networks required for completing DTs. Therefore, the PFC could be a potential target for the modulation and improvement of cognitive-motor functions during neurorehabilitation.

This study investigates the effects of varying loading conditions on excitability in neural pathways and gait dynamics. We focussed on evaluating the magnitude of the Hoffman reflex (H-reflex), a neurophysiological measure representing the capability to activate motor neurons and the timing and placement of the foot during walking. We hypothesized that weight manipulation would alter H-reflex magnitude, footfall and lower body kinematics. Twenty healthy participants were recruited and subjected to various weight-loading conditions. The H-reflex, evoked by stimulating the tibial nerve, was assessed from the dominant leg during walking. Gait was evaluated under five conditions: body weight, 20% and 40% additional body weight, and 20% and 40% reduced body weight (via a harness). Participants walked barefoot on a treadmill under each condition, and the timing of electrical stimulation was set during the stance phase shortly after the heel strike. Results show that different weight-loading conditions significantly impact the timing and placement of the foot and gait stability. Weight reduction led to a 25% decrease in double limb support time and an 11% narrowing of step width, while weight addition resulted in an increase of 9% in step width compared to body weight condition. Furthermore, swing time variability was higher for both the extreme weight conditions, while the H-reflex reduced to about 45% between the extreme conditions. Finally, the H-reflex showed significant main effects on variability of both stance and swing phases, indicating that muscle-motor excitability might serve as feedback for enhanced regulation of gait dynamics under challenging conditions.

The use of mobile exoskeletons as assistive walking devices has the potential to affect the biomechanics of the musculoskeletal system due to their weight and restricted range of motion. This may result in physical and cognitive load for the user. Understanding how lower extremity loading affects cognitive-motor interference is crucial for the design of wearable devices, including powered exoskeletons, and the development of effective training interventions.

This study aims to examine the effects of modified leg mechanics on cognitive-motor interference in dual-task walking. Gait variability, as an indicator of motor control, was analyzed to investigate its relation to cognitive task difficulty and to determine whether lower extremity loading modifies this relationship. Additionally, the impact on the gait pattern, as represented by the mean values of spatio-temporal gait parameters were investigated.

Fifteen healthy young adults walked on a treadmill with and without weight cuffs bilaterally attached to their thighs and shanks while performing a visual-verbal Stroop test (simple task) and a serial subtraction task (difficult task). Dependent variables include mean values and variability (coefficients of variation) of step length, step width, stride time and double support time. Additionally, secondary task performance as correct response rates and perceived workload were assessed.

Double support time variability decreased during dual-task walking, but not during walking with modified leg mechanics while performing the difficult secondary task. Walking with modified leg mechanics resulted in increased gait variability compared to normal walking, regardless of cognitive load. During walking with modified leg mechanics, step length, step width, and stride time increased, while double support time decreased. The secondary tasks did not affect the gait pattern.

The interplay between an external focus of attention and competition for attentional resources may influence the variability of double support time. The findings suggest that walking with modified leg mechanics could increase cognitive-motor interference for healthy young adults in demanding dual-task situations. Therefore, it is important to analyze the underlying mechanisms of cognitive-motor interference in the context of human-exoskeleton interaction.

The effect of using smartphones while walking on the cognitive and physical abilities of the "digital native" generation, i.e., individuals who have grown up in a digital media-centric environment, remains poorly understood. This study evaluated the effects of cognitive-motor interference on the use of smartphones while walking in children and young adults. The study involved 50 individuals from the digital age generation, including 24 children and 26 young adults. The study encompassed three experimental conditions, in which participants were instructed to traverse a distance of 60 m. The initial condition functioned as a control, wherein the participants walked without supplementary stimuli. In the second condition, the participants were provided with explicit instructions to grasp the smartphone device and position it in front of their chest by using both hands. This manipulation introduced a postural component into the experimental setup. The third condition required participants to be ambulatory while concurrently engaging in a cognitive task, namely, participating in a game that necessitated focused attention. Gait parameters were obtained by using inertial measurement unit sensors. Subsequently, the acquired gait characteristics were converted into dual-task costs (DTC). In the cognitive condition, children exhibited significantly greater DTC values for gait speed (76%), stride length (79%), stride time (102%), and stride length coefficient of variation (CV) than the young adults (p < 0.025). Moreover, as shown by the increased CV, a significant association exists between poor performance in smartphone games among children and increased variability in stride length. In children, the DTC of stride time CV decreased as smartphone game scores increased (R2 = 16.5%), and the DTC of stride length CV decreased more markedly as smartphone game scores increased (R2 = 28.2%). In conclusion, children are at a higher risk of pedestrian accidents when using smartphones while walking compared to young adults.

Among older people, walking difficulty results from actual and perceived declines in physical capacities and environmental requirements for walking. We investigated whether the physiological complexity of the gait cycle covaries with experience of walking difficulty. Walking difficulty, gait speed, and gait cycle complexity were evaluated among 702 community-dwelling older people aged 75, 80, and 85 years who took part in the six-minute walking test in the research laboratory. Walking difficulty for 500 m was self-reported. Complexity was quantified as trunk acceleration multiscale entropy during the gait cycle. Complexity was then compared between those with no reported walking difficulty, walking with modifications but no difficulty, and those reporting walking difficulty. Higher entropy differentiated those reporting no difficulty walking from those reporting walking difficulties, while those reporting having modified their walking, but no difficulty formed an intermediate group that could not be clearly distinguished from the other categories. The higher complexity of the gait cycle is associated with slower gait speed and the presence of self-reported walking difficulty. Among older people, gait cycle complexity which primarily reflects the biomechanical dimensions of gait quality, could be a clinically meaningful measure reflecting specific features of the progression of walking decline. This encourages further investigation of the sensitivity of gait cycle complexity to detect early signs of gait deterioration and to support targeted interventions among older people.

A distal radius fracture (DRF) is a common initial fragility fracture among women in their early postmenopausal period, which is associated with an increased risk of subsequent fractures. Gait assessments are valuable for evaluating fracture risk; inertial measurement units (IMUs) have been widely used to assess gait under free-living conditions. However, little is known about long-term changes in patients with DRF, especially concerning daily-life gait. We hypothesized that, in the long term, the daily-life gait parameters in patients with DRF could enable us to reveal future risk factors for falls and fractures.

This study assessed the spatiotemporal characteristics of patients with DRF at 4 weeks and 6 months of recovery.

We recruited 16 women in their postmenopausal period with DRF as their first fragility fracture (mean age 62.3, SD 7.0 years) and 28 matched healthy controls (mean age 65.6, SD 8.0 years). Daily-life gait assessments and physical assessments, such as hand grip strength (HGS), were performed using an in-shoe IMU sensor. Participants' results were compared with those of the control group, and their recovery was assessed for 6 months after the fracture.

In the fracture group, at 4 weeks after DRF, lower foot height in the swing phase (P=.049) and higher variability of stride length (P=.03) were observed, which improved gradually. However, the dorsiflexion angle in the fracture group tended to be lower consistently during 6 months (at 4 weeks: P=.06; during 6 months: P=.07). As for the physical assessments, the fracture group showed lower HGS at all time points (at 4 weeks: P<.001; during 6 months: P=.04), despite significant improvement at 6 months (P<.001).

With an in-shoe IMU sensor, we discovered the recovery of spatiotemporal gait characteristics 6 months after DRF surgery without the participants' awareness. The consistently unchanged dorsiflexion angle in the swing phase and lower HGS could be associated with fracture risk, implying the high clinical importance of appropriate interventions for patients with DRF to prevent future fractures. These results could be applied to a screening tool for evaluating the risk of falls and fractures, which may contribute to constructing a new health care system using wearable devices in the near future.

Variability is one of the most crucial outcomes in human movement studies: variance and standard deviation of various parameters have been reported in numerous studies. However, in many of these studies, the numbers of trials and subjects have been intuitively determined and not justified with statistical considerations. Here, we investigated the impact of the numbers of trials and subjects on statistical power, based on the assumption that results per trial follow a normal distribution, using mathematical analysis and numerical simulation. An inverse-like relationship was observed between the number of trials and subjects required to ensure the statistical power for detecting differences in variance between subject groups or conditions. For instance, assuming a 1.2-times difference in population variance between pre-and post-training sessions as an alternative hypothesis, our simulation demonstrated that combinations of the number of subjects and trials, such as measuring 100 trials from each of 12 subjects under each condition, or measuring 20 trials from each of 60 subjects, can guarantee an 80 % of statistical power. Planning research based on such mathematical considerations will enable meaningful statistical interpretations in studies focusing on movement variability, such as gait studies.

Children with hypermobility spectrum disorder/hypermobile Ehlers-Danlos syndrome (HSD/hEDS) have a high prevalence of chronic pain, which may influence gait dynamics. However, little is known about pain outcomes and their association with gait spatiotemporal parameters in children with HSD/hEDS.

Does pain correlate with gait spatiotemporal parameters in children with HSD/hEDS?

Eighteen children with HSD/hEDS and eighteen typically developing (TD) children participated in the study. The current level of pain (0-10 on the numeric rating scale), modified Brief Pain Inventory, and Pain Catastrophizing Scale-Child version were implemented to assess pain in children with HSD/hEDS. All children completed a gait analysis at a self-selected speed. Mean and variability (measured using the coefficient of variation) of gait spatiotemporal parameters were analyzed. Gait parameters included stride length, stride time, gait speed, percent stance time, and step width. A Mann-Whitney U-test was used to compare the gait parameters between children with HSD/hEDS and TD children. Spearman correlations were used to examine the relationships between pain and gait spatiotemporal parameters in children with HSD/hEDS.

Children with HSD/hEDS had a longer percent stance time compared to TD children (p = 0.03). Lower pain interference in relationships with other people was significantly associated with faster gait speeds (ρ = -0.55, p = 0.03). Children with HSD/hEDS also had greater pain interference during mobility (ρ = 0.5, p = 0.05) and going to school (ρ = 0.65, p = 0.01), which were significantly correlated with greater stride length variability. Greater pain interference during enjoyment of life was significantly associated with greater percent stance time variability (ρ = 0.5, p = 0.05). Greater pain catastrophizing was correlated with decreased step width variability in children with HSD/hEDS (ρ = -0.49, p = 0.05).

Pain interference and catastrophe were significantly associated with gait spatiotemporal variability. Our findings suggest that assessing pain-associated gait alterations may help understand the clinical features and gait kinematics of children with HSD/hEDS.

Chronic neck pain (CNP) can lead to altered gait which is worse when combined with head movement. Gait parameters for indicating speed and symmetry have not been thoroughly investigated in older adults with CNP. This study aimed to compare gait performance in term of speed and symmetry in older adults with and without CNP during walking with head movement.

Fifty young older adults, consisting of 36 healthy controls without neck pain (OLDs) and 14 older adults with CNP, participated in the study. Participants completed the Neck Disability Index and Activities-specific Balance Confidence Scale. The 10-Meter Walk Test (10MWT) was used to assess gait performance. Participants were instructed to walk at preferred speed under three different head movement patterns: no head movement (NM), horizontal head movement (HM), and vertical head movement (VM). The Inertial Measurement Unit was used to capture gait performance, and its software was used to analyze gait variables; gait speed, Locomotor Rehabilitation Index (LRI), gait asymmetry index, Phase Coordination Index (PCI).

The CNP group reported moderate neck pain with mild disability in activities of daily living, and less balance confidence than the OLD group (p < 0.05). The CNP group showed significantly slower gait speed and lower LRI during walking with both the HM and VM (p < 0.05), which corresponded to lower stride length and cadence. The gait asymmetry index in the CNP group was significantly higher than the OLD group during walking with VM (p < 0.05), whereas the PCI was significantly higher than the OLD group during walking with both HM and VM (p < 0.05).

Chronic neck pain affects both speed and symmetry when walking with head movement. Gait parameters in this study could be implemented to identify changes in speed and symmetry of gait in older adults with CNP who have mild disability and high physical functioning.