The effects of cognitive-motor interference on walking performance in adolescents with low balance.

BACKGROUND: Children with reduced motor competence (MC) have reported differences in their walking performance when compared to their typically developed peers, albeit, with inconsistent results. RESEARCH QUESTIONS: What is the effect of reduced balance on walking performance in adolescent boys and girls under cognitive-motor interference conditions? METHODS: This cross-sectional study assessed motor competence, in adolescents aged 13-14 years, using the Movement Assessment Battery for Children 2nd edition and walking performance from gait parameters derived from an inertial measurement unit placed over the estimated centre of mass. Each participant performed two 10 m straight-line walks at their self-selected speed. These consisted of a walk with no distractions and a cognitive-motor interference walk (reciting the alternate letters of the alphabet out loud). A two-way mixed ANOVA was used to assess for significant interactions. RESULTS: 365 adolescents, (low balance = 58, typical balance = 307) participated in this study (boys = 204, girls = 161). Significant interactions were reported between MC groups and walking condition for walking speed in boys (F(1,195) = 5.23, p= 0.02, ηp2 = 0.03) and girls (F(1,154) = 4.05, p= 0.046, ηp2 = 0.03). Both sexes with low balance reduced their walking speed to a greater extent than their typically developed peers under cognitive-motor interference conditions compared to the single-task walk. In addition, boys with low balance reported increased stride length variability (F(1,198)= 4.40, p= 0.037, ηp2= 0.02) compared to typically developed peers. SIGNIFICANCE: Adolescents with low balance report altered walking. Our data could support a better understanding of the relationship between balance and gait and may help the development of interventions to support those with difficulties.

Gait performance in older adults across the cognitive spectrum: Results from the GAIT cohort.

BACKGROUND: Gait performance can provide valuable insights into cognitive functioning in older adult and may be used to screen for cognitive impairment. However, the optimal test condition and spatiotemporal parameter for accuracy have not yet been determined. This study aims to determine the gait measure with the highest accuracy identifying cognitive decline. METHODS: A total of 711 participants were recruited, including 332 cognitively healthy individuals, 264 with mild cognitive impairment (MCI), and 115 with dementia, with a mean age of 72 years (interquartile range 69-76), and 43% (n = 307) of women. The participants underwent gait assessment in three different conditions, including a single task and dual tasks of counting backward by ones and naming animals. RESULTS: Gait performance was deteriorated as cognitive impairment progressed. The gait test performed during naming animals condition was the most accurate in differentiating between cognitive groups. Specifically, the naming animals gait speed was more accurate in discriminating control participants from those with cognitive impairment (area under the curve [AUC] = 76.9% for MCI and 99.7% for people with dementia with control group as reference). The coefficient of stride length variability while naming animals was the most effective parameter in discriminating between MCI and dementia groups (AUC = 96.7%). CONCLUSIONS: The naming animals dual-task gait test can be a valuable assessment for screening cognitive impairment in older adults, regardless of their cognitive abilities. The test is useful in clinical settings for subjects with a range of cognitive profiles.

Validation of a newly developed low-cost, high-accuracy, camera-based gait analysis system.

BACKGROUND: Gait analysis is essential for evaluating locomotor function and fall risk, particularly in the elderly and in various musculoskeletal disorders. Traditional gait analysis systems face challenges such as technical difficulties, high cost, and complexity of use. Therefore, there is a need for a more accessible and cost-effective system with a wider clinical applicability. RESEARCH QUESTION: This study aimed to validate the newly developed IB-gait® system (InBody, Republic of Korea), a camera-based gait analysis tool, by comparing it against the VICON system. METHODS: A total of 28 community-dwelling adults without gait abnormalities (mean age 24.9 years) were enrolled in this study. The participants underwent gait analysis at their self-selected speed using VICON and IB-gait® simultaneously. Nine spatiotemporal gait parameters, including stride length (m), step length (m), stride duration (s), double-limb duration (s), stance phase (s), swing phase (s), cadence (velocity × 120/stride length), and gait velocity (m/s) were measured. The agreement between the two systems was tested using Bland-Altman plots and intraclass correlation coefficients (ICC). RESULTS: The IB-gait® showed a high degree of agreement with the VICON system in most gait parameters. The ICC showed excellent reliability for stride length (0.97), step length (0.92), gait velocity (0.97), cadence (0.97), and stride duration (0.79). However, it showed lower reliability in time-based parameters, including double-limb duration (0.12), stance phase (0.54), swing phase (0.241), and stance/swing phase ratio (0.11). SIGNIFICANCE: The IB-gait® system appears to be a feasible and cost-effective alternative to VICON system for gait analysis, particularly showing a high level of agreement in the distance-based parameters. Its practicality in clinical settings makes it a valuable tool for widespread use in gait analysis. However, further refinement of time-based parameter measurements and validation in diverse patient populations are needed to enhance its applicability.

Investigation of the utility of smartphone-based gait analyses in discrimination between patients with Alzheimer's disease and Parkinson's disease.

OBJECTIVE: To reveal the discriminative value of gait parameters between Alzheimer's disease (AD) and Parkinson's disease (PD) subjects. METHODS: We included all consecutive patients with newly diagnosed AD and those with a diagnosis of PD who applied to our polyclinic between March 2022 and June 2022. The demographic and clinical features were evaluated during interviews. The gait analyses were performed using a quantitative, smartphone-based gait analyses program. Using this program, the step time (ST), step length (SL), step number (SN), gait velocity (GV), and cadence were measured in all individuals. RESULTS: Overall, 31 patients with AD and 45 with PD were enrolled in the analyses. The mean age of the AD group was higher according to those with PD. As expected, the Mini-Mental State Examination (MMSE) values were lower in the AD group. The comparative analyses of the gait parameters between groups did not reveal differences in any of the measures. The correlation analyses to investigate the possible association between the disease severity and gait parameters revealed that the MDS-UPDRS showed low negative correlations with SL and GV. CONCLUSION: Our findings suggest that the evaluation of gait using the gait analyses program does not contribute to the discrimination between AD and PD in clinical practice.

Immediate effects of insoles applied to the sound side lower extremity of patients with chronic hemiplegia during walking.

Background: Asymmetric gait patterns are mostly observed in hemiplegic stroke patients. These abnormal gait patterns resulting in abnormal speed, and decreased ability in daily of activity living. Objective: This study aimed to determine the immediate changes in gait parameters and plantar pressure during elevation by wearing an insole on the sound side lower extremity of patients with hemiplegia. Methods: Thirty-six participants were recruited, comprising those with a post-stroke follow-up of ≥3 months and a functional ambulation category score of ≥2. The participants were asked to walk with and without a 1 cm insole in the shoe of their sound side, and the order of wearing or not wearing the insole was randomized. Gait parameters, bilateral gait parameters, and dynamic plantar pressure were measured using the GAITRite Walkway System. Results: Paired t-test was used to examine immediate changes in gait parameters and plantar pressure with and without insoles during walking in the same group. Overall, gait velocity and step length significantly decreased (p < 0.05), whereas step time significantly increased (p < 0.05). The swing phase of the affected sidelower extremities significantly increased (p < 0.05), and the stance phase significantly decreased (p < 0.05). Double-support unloading phase (pre-swing phase) significantly increased (p < 0.05). The changes in plantar pressure were significantly increased in some lateral zones and significantly decreased in the medial zone of the mid-hindfoot, both in terms of pressure per time and peak pressure (p < 0.05). Conclusion: Although this study did not show immediate positive effects on gait parameters and gait cycle, it is expected that sensory input from the sole of the foot through changes in plantar pressure may help improve gait asymmetry and regulate postural symmetry.

Probabilistic Estimation of Cadence and Walking Speed From Floor Vibrations.

OBJECTIVE: This research aims to extract human gait parameters from floor vibrations. The proposed approach provides an innovative methodology on occupant activity, contributing to a broader understanding of how human movements interact within their built environment. METHODS AND PROCEDURES: A multilevel probabilistic model was developed to estimate cadence and walking speed through the analysis of floor vibrations induced by walking. The model addresses challenges related to missing or incomplete information in the floor acceleration signals. Following the Bayesian Analysis Reporting Guidelines (BARG) for reproducibility, the model was evaluated through twenty-seven walking experiments, capturing floor vibration and data from Ambulatory Parkinson's Disease Monitoring (APDM) wearable sensors. The model was tested in a real-time implementation where ten individuals were recorded walking at their own selected pace. RESULTS: Using a rigorous combined decision criteria of 95% high posterior density (HPD) and the Range of Practical Equivalence (ROPE) following BARG, the results demonstrate satisfactory alignment between estimations and target values for practical purposes. Notably, with over 90% of the 95% HPD falling within the region of practical equivalence, there is a solid basis for accepting the estimations as probabilistically aligned with the estimations using the APDM sensors and video recordings. CONCLUSION: This research validates the probabilistic multilevel model in estimating cadence and walking speed by analyzing floor vibrations, demonstrating its satisfactory comparability with established technologies such as APDM sensors and video recordings. The close alignment between the estimations and target values emphasizes the approach's efficacy. The proposed model effectively tackles prevalent challenges associated with missing or incomplete data in real-world scenarios, enhancing the accuracy of gait parameter estimations derived from floor vibrations. CLINICAL IMPACT: Extracting gait parameters from floor vibrations could provide a non-intrusive and continuous means of monitoring an individual's gait, offering valuable insights into mobility and potential indicators of neurological conditions. The implications of this research extend to the development of advanced gait analysis tools, offering new perspectives on assessing and understanding walking patterns for improved diagnostics and personalized healthcare.Clinical and Translational Impact Statement: This manuscript introduces an innovative approach for unattended gait assessments with potentially significant implications for clinical decision-making. By utilizing floor vibrations to estimate cadence and walking speed, the technology can provide clinicians with valuable insights into their patients' mobility and functional abilities in real-life settings. The strategic installation of accelerometers beneath the flooring of homes or care facilities allows for uninterrupted daily activities during these assessments, reducing the reliance on specialized clinical environments. This technology enables continuous monitoring of gait patterns over time and has the potential for integration into healthcare platforms. Such integration can enhance remote monitoring, leading to timely interventions and personalized care plans, ultimately improving clinical outcomes. The probabilistic nature of our model enables uncertainty quantification in the estimated parameters, providing clinicians with a nuanced understanding of data reliability.

Post-Arthroplasty Spatiotemporal Gait Parameters in Patients with Hip Osteoarthritis or Developmental Dysplasia of the Hip: An Observational Study.

Total hip arthroplasty (THA) is a preferred treatment for primary osteoarthritis (OA) or secondary degenerative arthropathy due to developmental hip dysplasia (DDH). Gait analysis is considered a gold standard for evaluating post-arthroplasty walking patterns. This study compared post-THA spatiotemporal gait parameters (SGPs) between OA and DDH patients and explored correlations with demographic and clinical variables. Thirty patients (15 per group) were recorded during gait and their SGPs were analyzed. Functionality was evaluated with the Oxford Hip Score (OHS). The OA patients were significantly older than DDH patients (p < 0.005). Significant and moderate to strong were the correlations between SGPs, age, and four items of the OHS concerning hip pain and activities of daily life (0.31 < Pearson's r < 0.51 all p < 0.05). Following THA, both groups exhibited similar levels of the examined gait parameters. Post-arthroplasty SGPs and OHS correlations indicate limitations in certain activities. Given the absence of pre-operative data and the correlation between age and SGPs and OHS, ANCOVA testing revealed that age adjusts OHS and SGP values, while pre-operative diagnosis has no main effect. These findings indicate that hip OA or DDH do not affect postoperative SGPs and patients' functionality. Future studies should examine both kinematic and kinetic data to better evaluate the post-THA gait patterns of OA and DDH patients.

Extraction and Validation of Biomechanical Gait Parameters with Contactless FMCW Radar.

A 77 GHz frequency-modulated continuous wave (FMCW) radar was utilized to extract biomechanical parameters for gait analysis in indoor scenarios. By preprocessing the collected raw radar data and eliminating environmental noise, a range-velocity-time (RVT) data cube encompassing the subjects' information was derived. The strongest signals from the torso in the velocity and range dimensions and the enveloped signal from the toe in the velocity dimension were individually separated for the gait parameters extraction. Then, six gait parameters, including step time, stride time, step length, stride length, torso velocity, and toe velocity, were measured. In addition, the Qualisys system was concurrently utilized to measure the gait parameters of the subjects as the ground truth. The reliability of the parameters extracted by the radar was validated through the application of the Wilcoxon test, the intraclass correlation coefficient (ICC) value, and Bland-Altman plots. The average errors of the gait parameters in the time, range, and velocity dimensions were less than 0.004 s, 0.002 m, and 0.045 m/s, respectively. This non-contact radar modality promises to be employable for gait monitoring and analysis of the elderly at home.

Dynamic gait stability in people with mild to moderate Parkinson's disease.

BACKGROUND: Falls are a serious health threat for people with Parkinson's disease. Dynamic gait stability has been associated with fall risk. Developing effective fall prevention interventions requires a sound understanding of how Parkinson's disease affects dynamic gait stability. This study compared dynamic gait stability within the Feasible Stability Region framework between people with and without Parkinson's disease during level walking at a self-selected speed. METHODS: Twenty adults with Parkinson's disease and twenty age- and gender-matched healthy individuals were recruited. Dynamic gait stability at two gait instants: touchdown and liftoff, was assessed as the primary outcome measurement. Spatiotemporal gait parameters, including stance phase duration, step length, gait speed, and cadence were determined as explanatory variables. FINDINGS: People with Parkinson's disease walked more slowly (p < 0.001) with a shorter step (p = 0.05), and prolonged stance phase (p = 0.04) than their healthy peers with moderate to large effect sizes. Dynamic gait stability did not show any group-associated differences (p > 0.36). INTERPRETATION: Despite the different gait parameters between groups, people with Parkinson's disease exhibited similar dynamic gait stability to their healthy counterparts. To compensate for the potential dynamic gait stability deficit resulting from slow gait speed, individuals with Parkinson's disease adopted a short step length to shift the center of mass motion state closer to the Feasible Stability Region. Our findings could provide insight into the impact of Parkinson's disease on the control of dynamic gait stability.

The Correlation between Malocclusion and Body Posture and Cervical Vertebral, Podal System, and Gait Parameters in Children: A Systematic Review.

Background: This study investigates the relationship between malocclusion and body posture, head posture, podal system, and gait parameters in children. Methods: A systematic review of observational studies from 2010 to 2023 was conducted and 24 cross-sectional studies involving 6199 participants were identified. These studies were categorized into those dealing with body posture (10 studies, 3601 participants), cervical vertebral column and head posture (6 studies, 644 participants), the podal system (5 studies, 1118 participants), and gait (3 studies, 836 participants). Results: Evidence suggests a significant association between malocclusion and body posture, balance, podal system, and gait parameters. Notably, eight studies found a significant relationship between malocclusion and body posture, while five studies identified this relationship with the cervical vertebral column and head posture, five with the podal system, and three with gait parameters. Conclusions: Overall, the quality of evidence was strong for the association between malocclusion and body posture and the podal system and moderate for head posture and gait parameters. These findings offer insights for therapists to design interventions tailored to children with malocclusion based on considerations of body posture, head posture, podal system, and gait parameters, though further longitudinal cohort studies are needed for better predictive understanding.

Wearable biofeedback device to assess gait features and improve gait pattern in people with parkinson's disease: a case series.

INTRODUCTION: People with Parkinson's Disease (PD) show abnormal gait patterns compromising their independence and quality of life. Among all gait alterations due to PD, reduced step length, increased cadence, and decreased ground-reaction force during the loading response and push-off phases are the most common. Wearable biofeedback technologies offer the possibility to provide correlated single or multi-modal stimuli associated with specific gait events or gait performance, hence promoting subjects' awareness of their gait disturbances. Moreover, the portability and applicability in clinical and home settings for gait rehabilitation increase the efficiency in the management of PD. The Wearable Vibrotactile Bidirectional Interface (BI) is a biofeedback device designed to extract gait features in real-time and deliver a customized vibrotactile stimulus at the waist of PD subjects synchronously with specific gait phases. The aims of this study were to measure the effect of the BI on gait parameters usually compromised by the typical bradykinetic gait and to assess its usability and safety in clinical practice. METHODS: In this case series, seven subjects (age: 70.4 ± 8.1 years; H&Y: 2.7 ± 0.3) used the BI and performed a test on a 10-meter walkway (10mWT) and a two-minute walk test (2MWT) as pre-training (Pre-trn) and post-training (Post-trn) assessments. Gait tests were executed in random order with (Bf) and without (No-Bf) the activation of the biofeedback stimulus. All subjects performed three training sessions of 40 min to familiarize themselves with the BI during walking activities. A descriptive analysis of gait parameters (i.e., gait speed, step length, cadence, walking distance, double-support phase) was carried out. The 2-sided Wilcoxon sign-test was used to assess differences between Bf and No-Bf assessments (p < 0.05). RESULTS: After training subjects improved gait speed (Pre-trn_No-Bf: 0.72(0.59,0.72) m/sec; Post-trn_Bf: 0.95(0.69,0.98) m/sec; p = 0.043) and step length (Pre-trn_No-Bf: 0.87(0.81,0.96) meters; Post-trn_Bf: 1.05(0.96,1.14) meters; p = 0.023) using the biofeedback during the 10mWT. Similarly, subjects' walking distance improved (Pre-trn_No-Bf: 97.5 (80.3,110.8) meters; Post-trn_Bf: 118.5(99.3,129.3) meters; p = 0.028) and the duration of the double-support phase decreased (Pre-trn_No-Bf: 29.7(26.8,31.7) %; Post-trn_Bf: 27.2(24.6,28.7) %; p = 0.018) during the 2MWT. An immediate effect of the BI was detected in cadence (Pre-trn_No-Bf: 108(103.8,116.7) step/min; Pre-trn_Bf: 101.4(96.3,111.4) step/min; p = 0.028) at Pre-trn, and in walking distance at Post-trn (Post-trn_No-Bf: 112.5(97.5,124.5) meters; Post-trn_Bf: 118.5(99.3,129.3) meters; p = 0.043). SUS scores were 77.5 in five subjects and 80.3 in two subjects. In terms of safety, all subjects completed the protocol without any adverse events. CONCLUSION: The BI seems to be usable and safe for PD users. Temporal gait parameters have been measured during clinical walking tests providing detailed outcomes. A short period of training with the BI suggests improvements in the gait patterns of people with PD. This research serves as preliminary support for future integration of the BI as an instrument for clinical assessment and rehabilitation in people with PD, both in hospital and remote environments. TRIAL REGISTRATION: The study protocol was registered (DGDMF.VI/P/I.5.i.m.2/2019/1297) and approved by the General Directorate of Medical Devices and Pharmaceutical Service of the Italian Ministry of Health and by the ethics committee of the Lombardy region (Milan, Italy).

Spatiotemporal Gait Analysis of Patients with Spinocerebellar Ataxia Types 3 and 10 Using Inertial Measurement Units: A Comparative Study.

Given the high morbidity related to the progression of gait deficits in spinocerebellar ataxias (SCA), there is a growing interest in identifying biomarkers that can guide early diagnosis and rehabilitation. Spatiotemporal parameter (STP) gait analysis using inertial measurement units (IMUs) has been increasingly studied in this context. This study evaluated STP profiles in SCA types 3 and 10, compared them to controls, and correlated them with clinical scales. IMU portable sensors were used to measure STPs under four gait conditions: self-selected pace (SSP), fast pace (FP), fast pace checking-boxes (FPCB), and fast pace with serial seven subtractions (FPS7). Compared to healthy subjects, both SCA groups had higher values for step time, variability, and swing time, with lower values for gait speed, cadence, and step length. We also found a reduction in speed gain capacity in both SCA groups compared to controls and an increase in speed dual-task cost in the SCA10 group. However, there were no significant differences between the SCA groups. Swing time, mean speed, and step length were correlated with disease severity, risk of falling and functionality in both clinical groups. In the SCA3 group, fear of falling was correlated with cadence. In the SCA10 group, results of the Montreal cognitive assessment test were correlated with step time, mean speed, and step length. These results show that individuals with SCA3 and SCA10 present a highly variable, short-stepped, slow gait pattern compared to healthy subjects, and their gait quality worsened with a fast pace and dual-task involvement.

Gait-modifying effects of augmented-reality cueing in people with Parkinson's disease.

Introduction: External cueing can improve gait in people with Parkinson's disease (PD), but there is a need for wearable, personalized and flexible cueing techniques that can exploit the power of action-relevant visual cues. Augmented Reality (AR) involving headsets or glasses represents a promising technology in those regards. This study examines the gait-modifying effects of real-world and AR cueing in people with PD. Methods: 21 people with PD performed walking tasks augmented with either real-world or AR cues, imposing changes in gait speed, step length, crossing step length, and step height. Two different AR headsets, differing in AR field of view (AR-FOV) size, were used to evaluate potential AR-FOV-size effects on the gait-modifying effects of AR cues as well as on the head orientation required for interacting with them. Results: Participants modified their gait speed, step length, and crossing step length significantly to changes in both real-world and AR cues, with step lengths also being statistically equivalent to those imposed. Due to technical issues, step-height modulation could not be analyzed. AR-FOV size had no significant effect on gait modifications, although small differences in head orientation were observed when interacting with nearby objects between AR headsets. Conclusion: People with PD can modify their gait to AR cues as effectively as to real-world cues with state-of-the-art AR headsets, for which AR-FOV size is no longer a limiting factor. Future studies are warranted to explore the merit of a library of cue modalities and individually-tailored AR cueing for facilitating gait in real-world environments.

Accuracy, concurrent validity, and test-retest reliability of pressure-based insoles for gait measurement in chronic stroke patients.

Introduction: Wearables are potentially valuable tools for understanding mobility behavior in individuals with neurological disorders and how it changes depending on health status, such as after rehabilitation. However, the accurate detection of gait events, which are crucial for the evaluation of gait performance and quality, is challenging due to highly individual-specific patterns that also vary greatly in movement and speed, especially after stroke. Therefore, the purpose of this study was to assess the accuracy, concurrent validity, and test-retest reliability of a commercially available insole system in the detection of gait events and the calculation of stance duration in individuals with chronic stroke. Methods: Pressure insole data were collected from 17 individuals with chronic stroke during two measurement blocks, each comprising three 10-min walking tests conducted in a clinical setting. The gait assessments were recorded with a video camera that served as a ground truth, and pressure insoles as an experimental system. We compared the number of gait events and stance durations between systems. Results and discussion: Over all 3,820 gait events, 90.86% were correctly identified by the insole system. Recall values ranged from 0.994 to 1, with a precision of 1 for all measurements. The F1 score ranged from 0.997 to 1. Excellent absolute agreement (Intraclass correlation coefficient, ICC = 0.874) was observed for the calculation of the stance duration, with a slightly longer stance duration recorded by the insole system (difference of -0.01 s). Bland-Altmann analysis indicated limits of agreement of 0.33 s that were robust to changes in walking speed. This consistency makes the system well-suited for individuals post-stroke. The test-retest reliability between measurement timepoints T1 and T2 was excellent (ICC = 0.928). The mean difference in stance duration between T1 and T2 was 0.03 s. We conclude that the insole system is valid for use in a clinical setting to quantitatively assess continuous walking in individuals with stroke.

Effects of motor imagery training on gait performance in individuals after stroke: a systematic review and meta-analysis.

PURPOSE: This review systematically explores and summarise the effects of motor imagery training (MIT) compared to conventional therapy on gait performance in individuals after stroke. MATERIALS AND METHODS: Randomised controlled trials (RCTs) were systematically searched in five electronic databases (PubMed, EMBASE, PsycINFO, OVID Nursing and CINAHL) from inception to 30 December 2022. Studies investigating MITs, targeted at individuals after stroke were eligible. Data were extracted related to study and intervention characteristics. RESULTS: Sixteen studies were included. Compared with 'routine methods of treatment or training', the meta-analyses showed that MIT was more effective in improving cadence immediately post intervention (SMD: 1.22, 95% CI: 0.59, 1.85, p = 0.0001, I2 = 25%) and at 1- or 2-months post intervention (SMD: 0.78, 95% CI: 0.35, 1.20, p = 0.0004, I2 = 46%). The results also showed that MIT improves the step length of the affected side and the unaffected side at 1- or 2-months post intervention. Separate meta-analyses were also conducted on different tests of walking endurance (assessed by the 6-Minute Walk Test) and functional mobility (assessed by the Timed-Up-and-Go test). CONCLUSIONS: MIT effectively improved gait performance. The findings in individuals after stroke remain inconclusive due to significant heterogeneity in included studies.

Restoring gait performance and daily functional abilities is an important goal of post-stroke rehabilitation.Motor imagery training (MIT) may be a promising method to improve gait restoration and is expected to provide another option for the effective rehabilitation of stroke patients.This review highlights the limited research on MIT and thus the limited evidence to guide clinical rehabilitation.In the stroke rehabilitation, clinical specialists may consider incorporating MIT into the treatment programme to improve patients' gait performance and ensure effective early lower limb rehabilitation.

Gait Parameters can Reflect Cognitive Performance in Older Adults with Cerebral Small Vessel Disease: A Cross-sectional Research.

BACKGROUND: Cerebral small vessel disease (CSVD) is a common chronic progressive disease. It remains unclear whether high gait variability is a marker of cognitive cortical dysfunction. METHODS: This study included 285 subjects (aged from 60 to 85 years, 60.3% female) including 37 controls, 179 presented as Fazekas II, and 69 presented as Fazekas III. The severity of white matter hyperintensities was assessed by the Fazekas Rating Scale. Gait parameters were assessed using a vision-based artificial intelligent gait analyzer. Cognitive function was tested by MMSE, MoCA, DST, and VFT. RESULTS: Three gait parameters including gait speed, gait length, and swing time were associated with cognitive performance in patients with CSVD. Gait speed was associated with cognitive performance, including MMSE (ß 0.200; 95%CI 1.706-6.018; p <.001), MoCA (ß 0.183; 95%CI 2.047-7.046; p <.001), DST (order) (ß 0.204; 95%CI 0.563-2.093; p =.001) and VFT (ß 0.162; 95%CI 0.753-4.865; p =.008). Gait length was associated with cognitive performance, including MMSE (ß 0.193; 95%CI 3.475-12.845; p =.001), MoCA (ß 0.213; 95%CI 6.098-16.942; p <.001), DST (order) (ß 0.224; 95%CI 1.056-4.839; P <.001) and VFT (ß 0.149; 95%CI 1.088- 10.114; p =.015). Swing time was associated with cognitive performance, including MMSE (ß - 0.242; 95%CI -2.639 to -0.974; p<.001), MoCA (ß -0.211; 95%CI -2.989 to -1.034; p <.001) and DST (reverse order) (ß -0.140; 95%CI -0.568 to -0.049; p =.020). CONCLUSION: This study revealed that the relationship between gait parameters and cognitive performance in patients with CSVD and the deteriorated gait parameters can reflect cognitive impairment and even dementia in older people with CSVD.

Markerless gait analysis through a single camera and computer vision.

The assessment of gait performance using quantitative measures can yield crucial insights into an individual's health status. Recently, computer vision-based human pose estimation has emerged as a promising solution for markerless gait analysis, as it allows for the direct extraction of gait parameters from videos. This study aimed to compare the lower extremity kinematics and spatiotemporal gait parameters obtained from a single-camera-based markerless method with those acquired from a marker-based motion tracking system across a healthy population. Additionally, we investigated the impact of camera viewing angles and distances on the accuracy of the markerless method. Our findings demonstrated a robust correlation and agreement (Rxy > 0.75, Rc > 0.7) between the markerless and marker-based methods for most spatiotemporal gait parameters. We also observed strong correlations (Rxy > 0.8) between the two methods for hip flexion/extension, knee flexion/extension, hip abduction/adduction, and hip internal/external rotation. Statistical tests revealed significant effects of viewing angles and distances on the accuracy of the identified gait parameters. While the markerless method offers an alternative for general gait analysis, particularly when marker use is impractical, its accuracy for clinical applications remains insufficient and requires substantial improvement. Future investigations should explore the potential of the markerless system to measure gait parameters in pathological gaits.

Accuracy of unilateral and bilateral gait assessment using a mobile gait analysis system at different walking speeds.

BACKGROUND: Previous research on the accuracy of mobile measurement systems has focused on parameters related to the whole gait cycle. Specifically, bilateral gait characteristics were primarily used as outcome measures. RESEARCH QUESTION: How accurate are unilateral gait characteristics detected using a mobile system at various fixed walking speeds? METHODS: Gait analysis during treadmill walking at velocities (VEL) of 2.5 (v1), 4.5 (v2) and 6.5 km/h (v3) was performed in a population of 47 healthy young adults, consisting of 27 females (age: 23 ± 2 years, BMI: 21.4 ± 2.2 kg/m²) and 20 males (age: 22 ± 1 years, BMI: 23.3 ± 3.4 kg/m²). Spatiotemporal gait data were simultaneously determined using an instrumented treadmill (gaitway 3D) and a mobile gait analysis system (RehaGait). Besides VEL, bilateral (stride length [SL], cadence [CAD]) and unilateral (contact duration [CON], single [SS] and double support duration [DS]) outcomes were validated. RESULTS: Across the three VEL investigated, the correlations between both measurement systems were almost perfect in SL and CAD (r > 0.97). In addition, SL significantly differed (p < 0.01) with moderate to large effects, whereby the root mean squared error (RMSE) did not exceed 1.8 cm. RMSE in CAD was not higher than 0.33 spm and statistically significant differences were only present at v1 (d = 0.63). DS was the most erroneous unilateral parameter with values for %RMSE ranging from 9% at v1 to 14% at v3. In CON and SS %RMSE was in a magnitude of 2-4% across all VEL. Furthermore, VEL affected measurement accuracy in unilateral outcomes with moderate to large effects (F (2, 45) > 6.0, p < 0.01, ηp2 > 0.11) with consistently higher differences at lower velocities. SIGNIFICANCE: Based on the results presented the validity of the mobile gait analysis system investigated to detect gait asymmetries must be questioned.

Estimation of Gait Parameters for Adults with Surface Electromyogram Based on Machine Learning Models.

Gait analysis has been studied over the last few decades as the best way to objectively assess the technical outcome of a procedure designed to improve gait. The treating physician can understand the type of gait problem, gain insight into the etiology, and find the best treatment with gait analysis. The gait parameters are the kinematics, including the temporal and spatial parameters, and lack the activity information of skeletal muscles. Thus, the gait analysis measures not only the three-dimensional temporal and spatial graphs of kinematics but also the surface electromyograms (sEMGs) of the lower limbs. Now, the shoe-worn GaitUp Physilog® wearable inertial sensors can easily measure the gait parameters when subjects are walking on the general ground. However, it cannot measure muscle activity. The aim of this study is to measure the gait parameters using the sEMGs of the lower limbs. A self-made wireless device was used to measure the sEMGs from the vastus lateralis and gastrocnemius muscles of the left and right feet. Twenty young female subjects with a skeletal muscle index (SMI) below 5.7 kg/m2 were recruited for this study and examined by the InBody 270 instrument. Four parameters of sEMG were used to estimate 23 gait parameters. They were measured using the GaitUp Physilog® wearable inertial sensors with three machine learning models, including random forest (RF), decision tree (DT), and XGBoost. The results show that 14 gait parameters could be well-estimated, and their correlation coefficients are above 0.800. This study signifies a step towards a more comprehensive analysis of gait with only sEMGs.

Determination of Gait Events and Temporal Gait Parameters for Persons with a Knee-Ankle-Foot Orthosis.

Gait event detection is essential for controlling an orthosis and assessing the patient's gait. In this study, patients wearing an electromechanical (EM) knee-ankle-foot orthosis (KAFO) with a single IMU embedded in the thigh were subjected to gait event detection. The algorithm detected four essential gait events (initial contact (IC), toe off (TO), opposite initial contact (OIC), and opposite toe off (OTO)) and determined important temporal gait parameters such as stance/swing time, symmetry, and single/double limb support. These gait events were evaluated through gait experiments using four force plates on healthy adults and a hemiplegic patient who wore a one-way clutch KAFO and a pneumatic cylinder KAFO. Results showed that the smallest error in gait event detection was found at IC, and the largest error rate was observed at opposite toe off (OTO) with an error rate of -2.8 ± 1.5% in the patient group. Errors in OTO detection resulted in the largest error in determining the single limb support of the patient with an error of 5.0 ± 1.5%. The present study would be beneficial for the real-time continuous monitoring of gait events and temporal gait parameters for persons with an EM KAFO.