Spatiotemporal Gait Characteristics Research Articles

Attention and sensory integration for gait in young adults with autism spectrum disorder

IntroductionAltered sensorimotor function is a common feature of autism spectrum disorder (ASD). As a result, spatiotemporal walking patterns are typically affected. Attentional processes relevant for locomotion may be altered in people with ASD. This study assessed the extent to which gait alterations observed under sensory challenging conditions are due to reduced attention-related processes in young adults with ASD. MethodsTwenty-one adults with ASD and 21 age- and sex-matched neurotypical participants walked at a self-selected pace on a 10-m walkway under 12 sensory/attention conditions: hard or carpet flooring; well-lit or dim lighting; no attention task, an auditory choice-reaction time information-processing task, or a simple reaction time information-processing task. Gait data were collected with a 12-marker motion capture set and a trunk accelerometer. Spatiotemporal characteristics of gait were derived and compared between the two groups across gait conditions. ResultsFloor/light conditions impacted gait speed, average step length, average stance time, average step width, and step width variability similarly in both groups (p<0.05). The information processing tasks impacted average step length, gait speed, and step length variability (p<0.05). Group differences were found in step length metrics: the ASD group had decreased average step length during the simple reaction time information-processing task and neurotypical participants did not (p=0.039); the ASD group had increased variability on carpet compared to hard floor and the neurotypical group had no change in variability due to floor (p=0.015). SignificanceThese results suggest that attentional set-shifting and somatosensory inputs may play an important role in ASD-related gait alterations. Step length metrics appear to be sensitive to group differences between ASD and neurotypical adults during sensory challenging conditions.

GAITBOOST: Boosting gait recognition performance with BAT-extreme learning machines algorithm

Gait analysis is a widely used technique for passive human identification and tracking, with potential applications in security and surveillance systems. However, existing gait recognition methods face challenges in handling changing angles and uncertain features. In this paper, we propose a novel gait recognition approach that leverages real-time spatio-temporal gait features, including step length, gait cycle, height, cadence, swing ratio, and foot length. We apply the Extreme Learning Machines (ELM) algorithm for classification, which has been shown to be effective in various applications due to its fast-learning speed and good generalization performance. To further enhance the recognition rate, we introduce an evolutionary BAT-optimized ELM algorithm that addresses the instability issue in ELM. The proposed BAT-ELM algorithm can optimize the hidden nodes and weights of ELM, which leads to improved efficiency in recognizing gait from multiple view angles ranging from 0° to 180°. Our comprehensive analysis of the proposed approach indicates that it outperforms other reported algorithms in terms of recognition rate and efficiency. Our work demonstrates the effectiveness of combining real-time spatio-temporal gait features with the BAT-ELM algorithm for gait recognition. The proposed approach has potential applications in various fields, including security and surveillance systems, healthcare, and robotics. Our findings highlight the importance of leveraging evolutionary algorithms to optimize machine learning models and achieve better performance in complex recognition tasks.

Using In-Shoe Inertial Measurement Unit Sensors to Understand Daily-Life Gait Characteristics in Patients With Distal Radius Fractures During 6 Months of Recovery: Cross-Sectional Study.

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.

Tapping into the human spinal locomotor centres with transspinal stimulation

Human locomotion is controlled by spinal neuronal networks of similar properties, function, and organization to those described in animals. Transspinal stimulation affects the spinal locomotor networks and is used to improve standing and walking ability in paralyzed people. However, the function of locomotor centers during transspinal stimulation at different frequencies and intensities is not known. Here, we document the 3D joint kinematics and spatiotemporal gait characteristics during transspinal stimulation at 15, 30, and 50 Hz at sub-threshold and supra-threshold stimulation intensities. We document the temporal structure of gait patterns, dynamic stability of joint movements over stride-to-stride fluctuations, and limb coordination during walking at a self-selected speed in healthy subjects. We found that transspinal stimulation (1) affects the kinematics of the hip, knee, and ankle joints, (2) promotes a more stable coordination at the left ankle, (3) affects interlimb coordination of the thighs, and (4) intralimb coordination between thigh and foot, (5) promotes greater dynamic stability of the hips, (6) increases the persistence of fluctuations in step length variability, and lastly (7) affects mechanical walking stability. These results support that transspinal stimulation is an important neuromodulatory strategy that directly affects gait symmetry and dynamic stability. The conservation of main effects at different frequencies and intensities calls for systematic investigation of stimulation protocols for clinical applications.

Abstract TP74: MR-001 Improves Stride Length and Cadence in People With Chronic Stroke Walking Impairment

Introduction: Chronic stroke walking impairments impact approximately 3.5 million Americans and can lead to decreased quality of life, increased fall risk, and increased comorbidities. Stroke results in changes to spatiotemporal gait characteristics, including cadence, stride length, and speed, which can negatively impact walking stability and increase falls. In a multicenter randomized controlled trial, MR-001 was shown to increase comfortable walking speed more than Active Control (walking without MR-001). The purpose of this investigation is to characterize the spatiotemporal gait changes underlying MR-001's improvement in walking speed. Methods: Participants (n = 34) received the MR-001 (InTandem™) intervention for 30 minutes, 3 times a week for 5 weeks. MR-001 autonomously delivers an individualized and progressive intervention based on the principles of rhythmic auditory stimulation. The device algorithm uses input from foot-worn sensors to change the auditory stimuli in the form of embedded cues in time-shifted music. This secondary analysis from a larger clinical trial included change in stride length and cadence while walking without auditory stimuli, calculated as the change from before to after the intervention, as well as the relative contribution of changes in stride length and cadence to changes in walking speed. Results: Participants showed a significant increase in both stride length [+8%, Δ 0.06 +/- 0.02 m, p &lt; 0.004] and cadence [+4%, Δ 3.04 +/- 1.32 steps/min, p &lt; 0.028]. The average increase in stride length was 2x the established minimal detectable change of 4%. Changes in stride length and cadence each independently predicted the change in speed [ R 2 = 0.99, F (2,31) = 1132.44, p &lt; 0.001], with the change in stride length being the stronger contributor [stride length: β = .78, p &lt; 0.001, cadence: β = .50, p &lt; 0.001]. Conclusions: Post-stroke reductions in stride length and cadence result in slower walking speed and negatively impact walking stability. In conclusion, results of this secondary analysis show that the previously reported increase in walking speed resulting from MR-001 is influenced by stride length more strongly than cadence for individuals with chronic stroke walking impairment.

Inflammatory Biomarkers and Gait Impairment in Older Adults: A Systematic Review.

Peripheral inflammation and gait speed alterations are common in several neurological disorders and in the aging process, but the association between the two is not well established. The aim of this systematic literary review is to determine whether proinflammatory markers are a positive predictor for gait impairments and their complications, such as falls in older adults, and may represent a risk factor for slow gait speed and its complications. The systematic review was performed in line with the Preferred Report Items for Systematic Review and Meta-Analyses (PRISMA). A protocol for literature searches was structured a priori and designed according to the International Perspective Register of Systemic Review (PROSPERO: CRD42023451108). Peer-reviewed original articles were identified by searching seven electronic databases: Excerpta Medica Database (EMBASE), SciVerse (ScienceDirect), Scopus, PubMed, Medline, Web of Science, and the Cochrane Library. The search strategy was formulated based on a combination of controlled descriptors and/or keywords related to the topic and a manual search was conducted of the reference lists from the initially selected studies to identify other eligible studies. The studies were thoroughly screened using the following inclusion criteria: older adults, spatiotemporal gait characteristics, and proinflammatory markers. A meta-analysis was not performed due to the heterogeneity of the studies, and the results were narratively synthesized. Due to the clinical and methodological heterogeneity, the studies were combined in a narrative synthesis, grouped by the type of biomarkers evaluated. A standardized data extraction form was used to collect the following methodological outcome variables from each of the included studies: author, year, population, age, sample size, spatiotemporal gait parameters such as gait velocity, and proinflammatory markers such as TNF-α, high sensitivity C-reactive (CRP) proteins, and IL-6. We included 21 out of 51 studies in our review, which examined the association between inflammatory biomarkers and gait impairment. This review highlights the role of TNF-α, CRP, and IL-6 in gait impairment. Biomarkers play an important role in the decision-making process, and IL-6 can be an effective biomarker in establishing the diagnosis of slow gait speed. Further longitudinal research is needed to establish the use of molecular biomarkers in monitoring gait impairment.

Remote Gait Analysis Using Ultra-Wideband Radar Technology Based on Joint Range-Doppler-Time Representation.

In recent years, radar technology has been extensively utilized in contactless human behavior monitoring systems. The unique capabilities of ultra-wideband (UWB) radars compared to conventional radar technologies, due to time-of-flight measurements, present new untapped opportunities for in-depth monitoring of human movement during overground locomotion. This study aims to investigate the deployability of UWB radars in accurately capturing the gait patterns of healthy individuals with no known walking impairments. A novel algorithm was developed that can extract ten clinical spatiotemporal gait features using the Doppler information captured from three monostatic UWB radar sensors during a 6-meter walking task. Key gait events are detected from lower-extremity movements based on the joint range-Doppler-time representation of recorded radar data. The estimated gait parameters were validated against a gold-standard optical motion tracking system using 12 healthy volunteers. On average, nine gait parameters can be consistently estimated with 90-98% accuracy, while capturing 94.5% of participants' gait variability and 90.8% of inter-limb symmetry. Correlation and Bland-Altman analysis revealed a strong correlation between radar-based parameters and the ground-truth values, with average discrepancies consistently close to 0. Results prove that radar sensing can provide accurate biomarkers to supplement clinical human gait assessment, with quality similar to gold standard assessment. Radars can potentially allow a transition from expensive and cumbersome lab-based gait analysis tools toward a completely unobtrusive and affordable solution for in-home deployment, enabling continuous long-term monitoring of individuals for research and healthcare applications.

A Double-Hurdle Quantification Model for Freezing of Gait of Parkinson's Patients.

Freezing of gait (FOG) leads to an increased risk of falls and limited mobility in individuals with Parkinson's disease (PD). However, existing research ignores the fine-grained quantitative assessment of FOG severity. This paper provides a double-hurdle model that uses typical spatiotemporal gait features to quantify the FOG severity in patients with PD. Moreover, a novel multi-output random forest algorithm is used as one hurdle of the double-hurdle model, further enhancing the model's performance. We conduct six experiments on a public PD gait database. Results demonstrate that the designed random forest algorithm in the double-hurdle model-hyperparameter independence framework achieves outstanding performances with the highest correlation coefficient (CC) of 0.972 and the lowest root mean square error (RMSE) of 2.488. Furthermore, we study the effect of drug state on the gait patterns of PD patients with or without FOG. Results show that "OFF" state amplifies the visibility of FOG symptoms in PD patients. Therefore, this study holds significant implications for the management and treatment of PD.

Assessment of spatiotemporal characteristics of gait, trough the Phyphox® app: a case series

BackgroundSpatiotemporal characteristics from human locomotion can provide effective clinical metrics to assess motor control and brain function. This case report aims to assess the temporal structure of variability in stride-to-stride time and calculated the intrinsic fractal frame that is hidden below the repetitive structure of physiological gait through the “Phyphox” app. This is an innovative study from the perspective of analyzing gait variables through a mobile app. Case presentationFive older adults Caucasian (3 women; age = 73 ± 10,5 years; body mass = 62,2 ± 15,1 kg; height = 1,56 ± 0,1 m; 2 men; age = 75,5 ± 7,8 years; body mass = 86,3 ± 18,0 kg; height = 1,77 ± 0,1 m) participated in this study. Five participants were asked to walk with a natural cadence, two of the participants presented a value greater than 80 step’s/minute (81.14 ± 0.01; 86.67 ± 0.02); and the others had values between 55 and 65 step’s/minute (55.20 ± 0.02; 55.78 ± 0.05; 61.02 ± 0.05). Regarding the coefficient of variation, only one participant presented 10.08%. For the total number of steps, three of the participants had values greater than 1000 steps. The variability of these stride-to-stride time has been quantified through detrended fluctuation analysis; one participant presented a value above 1.ConclusionsThis study provides evidence that a smartphone might provide a valid measure to assess the spatiotemporal characteristics of gait.

Comparison of gait and sagittal plane arm swing between individuals with adolescent idiopathic scoliosis and healthy individuals.

Arm swing and energy consumption play an important role in the realization of an effective gait. However, research on arm swing and energy consumption during gait in individuals with adolescent idiopathic scoliosis (AIS) is limited. The aim of this study was to investigate the spatiotemporal characteristics of gait, arm swing angles in the sagittal plane, energy consumption in individuals with AIS, to compare them with their healthy peers in this regard. 26 diagnosed with AIS and 21 healthy were included in this study. Evaluation measures were based on the Cobb angle, axial trunk rotation, trunk symmetry, sagittal curve measurements, spatiotemporal characteristics of gait with the GAITRite electronic walkway, sagittal plane arm swing with two video-camera recordings, and energy consumption. There were a decrease in right- sided sagittal arm swing, an increase in energy consumption, in left-side step time and right-side double support time in the scoliosis group compared to the control group. The other spatiotemporal characteristics of the gait were similar in both groups. The evaluation of arm swing, energy consumption, and gait of individuals with AIS may contribute to the development of rehabilitation programs by better identifying the deficiencies of individuals with AIS.

Modulation of limb mechanics in alligators moving across varying grades.

Graded substrates require legged animals to modulate their limb mechanics to meet locomotor demands. Previous work has elucidated strategies used by cursorial animals with upright limb posture, but it remains unclear how sprawling species such as alligators transition between grades. We measured individual limb forces and 3D kinematics as alligators walked steadily across level, 15deg incline and 15deg decline conditions. We compared our results with the literature to determine how limb posture alters strategies for managing the energetic variation that accompanies shifts in grade. We found that juvenile alligators maintain spatiotemporal characteristics of gait and locomotor speed while selectively modulating craniocaudal impulses (relative to level) when transitioning between grades. Alligators seem to accomplish this using a variety of kinematic strategies, but consistently sprawl both limb pairs outside of the parasagittal plane during decline walking. This latter result suggests alligators and other sprawling species may use movements outside of the parasagittal plane as an axis of variation to modulate limb mechanics when transitioning between graded substrates. We conclude that limb mechanics during graded locomotion are fairly predictable across quadrupedal species, regardless of body plan and limb posture, with hindlimbs playing a more propulsive role and forelimbs functioning to dissipate energy. Future work will elucidate how shifts in muscle properties or function underlie such shifts in limb kinematics.

Spatiotemporal gait characteristics during single- and dual-task walking are associated with the burden of cerebral small vessel disease.

Gait impairment is a common symptom among individuals with cerebral small vessel disease (CSVD). However, performance differences between single-task walking (STW) and dual-task walking (DTW) among individuals with CSVD remain unclear. Therefore, we aimed to examine differences in gait characteristics during STW and DTW as well as the association between gait performance and neuroimaging markers. We enrolled 126 older individuals with CSVD. The speed, cadence, stride length, stride time, and their dual-task cost (DTC) or variability were measured under the STW, motor-cognitive DTW (cognitive DTW), and motor-motor DTW (motor DTW) conditions. We examined neuroimaging features such as white matter hyperintensities (WMHs), lacunes, microbleeds, and total burden. Further, we analysed the association of neuroimaging markers with gait performance, including gait variability and DTC. Almost all spatiotemporal characteristics, as well as their DTCs or variabilities, showed significant among-group differences according to disease severity in the cognitive DTW condition; however, relatively lesser differences were observed in the STW and motor DTW conditions. The total CSVD burden score was moderately correlated with all the spatial parameters, as well as their DTCs or variabilities, in the cognitive DTW condition. Moreover, WMHs showed a correlation with speed, stride time, and cadence, as well as their DTCs, in the cognitive DTW condition. Furthermore, lacunes showed a moderate correlation with speed, stride length, and the DTC of speed, whilst microbleeds were only related to the DTC of stride length in the cognitive DTW condition. Neuroimaging biomarkers were not correlated with spatiotemporal parameters in STW and motor DTW conditions after Bonferroni correction. Moreover, the correlation coefficient between the total CSVD burden score and gait parameters was greater than those of other biomarkers. Parameters in the cognitive DTW condition are more appropriate than those in the motor DTW condition for the evaluation of gait abnormalities in patients with CSVD. Moreover, the total CSVD burden score might have better predictive utility than any single neuroimaging marker. Patients with CSVD, especially those with moderate-to-severe disease, should concentrate more on their gait patterns and reduce the load of secondary cognitive tasks whilst walking in daily life.

Gender Recognition Based on Gradual and Ensemble Learning from Multi-View Gait Energy Images and Poses.

Image-based gender classification is very useful in many applications, such as intelligent surveillance, micromarketing, etc. One common approach is to adopt a machine learning algorithm to recognize the gender class of the captured subject based on spatio-temporal gait features extracted from the image. The image input can be generated from the video of the walking cycle, e.g., gait energy image (GEI). Recognition accuracy depends on the similarity of intra-class GEIs, as well as the dissimilarity of inter-class GEIs. However, we observe that, at some viewing angles, the GEIs of both gender classes are very similar. Moreover, the GEI does not exhibit a clear appearance of posture. We postulate that distinctive postures of the walking cycle can provide additional and valuable information for gender classification. This paper proposes a gender classification framework that exploits multiple inputs of the GEI and the characteristic poses of the walking cycle. The proposed framework is a cascade network that is capable of gradually learning the gait features from images acquired in multiple views. The cascade network contains a feature extractor and gender classifier. The multi-stream feature extractor network is trained to extract features from the multiple input images. Features are then fed to the classifier network, which is trained with ensemble learning. We evaluate and compare the performance of our proposed framework with state-of-the-art gait-based gender classification methods on benchmark datasets. The proposed framework outperforms other methods that only utilize a single input of the GEI or pose.

Can Gait Characteristics Be Represented by Physical Activity Measured with Wrist-Worn Accelerometers?

Wearable accelerometers allow for continuous monitoring of function and behaviors in the participant's naturalistic environment. Devices are typically worn in different body locations depending on the concept of interest and endpoint under investigation. The lumbar and wrist are commonly used locations: devices placed at the lumbar region enable the derivation of spatio-temporal characteristics of gait, while wrist-worn devices provide measurements of overall physical activity (PA). Deploying multiple devices in clinical trial settings leads to higher patient burden negatively impacting compliance and data quality and increases the operational complexity of the trial. In this work, we evaluated the joint information shared by features derived from the lumbar and wrist devices to assess whether gait characteristics can be adequately represented by PA measured with wrist-worn devices. Data collected at the Pfizer Innovation Research (PfIRe) Lab were used as a real data example, which had around 7 days of continuous at-home data from wrist- and lumbar-worn devices (GENEActiv) obtained from a group of healthy participants. The relationship between wrist- and lumbar-derived features was estimated using multiple statistical methods, including penalized regression, principal component regression, partial least square regression, and joint and individual variation explained (JIVE). By considering multilevel models, both between- and within-subject effects were taken into account. This work demonstrated that selected gait features, which are typically measured with lumbar-worn devices, can be represented by PA features measured with wrist-worn devices, which provides preliminary evidence to reduce the number of devices needed in clinical trials and to increase patients' comfort. Moreover, the statistical methods used in this work provided an analytic framework to compare repeated measures collected from multiple data modalities.

Relationship between gait parameters and cognitive indexes in adult aging.

This study aimed to identify the most effective summary cognitive index predicted from spatio-temporal gait features (STGF) extracted from gait patterns. The study involved 125 participants, including 40 young (mean age: 27.65 years, 50% women), and 85 older adults (mean age: 73.25 years, 62.35% women). The group of older adults included both healthy adults and those with Mild Cognitive Impairment (MCI). Participant´s performance in various cognitive domains was evaluated using 12 cognitive measures from five neuropsychological tests. Four summary cognitive indexes were calculated for each case: 1) the z-score of Mini-Mental State Examination (MMSE) from a population norm (MMSE z-score); 2) the sum of the absolute z-scores of the patients' neuropsychological measures from a population norm (ZSum); 3) the first principal component scores obtained from the individual cognitive variables z-scores (PCCog); and 4) the Mahalanobis distance between the vector that represents the subject's cognitive state (defined by the 12 cognitive variables) and the vector corresponding to a population norm (MDCog). The gait patterns were recorded using a body-fixed Inertial Measurement Unit while participants executed four walking tasks (normal, fast, easy- and hard-dual tasks). Sixteen STGF for each walking task, and the dual-task costs for the dual tasks (when a subject performs an attention-demanding task and walks at the same time) were computed. After applied Principal Component Analysis to gait measures (96 features), a robust regression was used to predict each cognitive index and individual cognitive variable. The adjusted proportion of variance (adjusted-R2) coefficients were reported, and confidence intervals were estimated using the bootstrap procedure. The mean values of adjusted-R2 for the summary cognitive indexes were as follows: 0.0248 for MMSE z-score, 0.0080 for ZSum, 0.0033 for PCCog, and 0.4445 for MDCog. The mean adjusted-R2 values for the z-scores of individual cognitive variables ranged between 0.0009 and 0.0693. Multiple linear regression was only statistically significant for MDCog, with the highest estimated adjusted-R2 value. The association between individual cognitive variables and most of the summary cognitive indexes with gait parameters was weak. However, the MDCog index showed a stronger and significant association with the STGF, exhibiting the highest value of the proportion of the variance that can be explained by the predictor variables. These findings suggest that the MDCog index may be a useful tool in studying the relationship between gait patterns and cognition.

286 Gait speed—a useful addition to dementia risk prediction in midlife? Results from ENBIND

Abstract Background Midlife Type 2 Diabetes (T2DM) is associated with a two-fold increased risk of dementia in later life. The utility of spatiotemporal gait characteristics to identify those with midlife T2DM at greatest risk of later cognitive decline has not been assessed. This may aid in selecting-out those at greatest risk for preventative brain-health interventions. Methods ENBIND is a longitudinal study of cognitively-healthy individuals with midlife T2DM (free from microvascular/macrovascular complications) and healthy controls. At baseline, gait speed was measured across three tasks (usual gait speed, maximal gait speed and cognitive dual-task—whilst reciting alternate letters of the alphabet). Domain-specific neuropsychological performance was assessed using the Montreal Cognitive Assessment (MoCA) and a custom CANTAB battery with identical assessments repeated at a 4-year interval. Linear regression was used to analyse results with adjustment for important clinical confounders. Results 30 individuals (aged 55.5 ± 9 years; 50% female) were followed up, of whom three-quarters (n = 23, 76.7%) had midlife T2DM. Individuals with midlife T2DM did not experience greater cognitive decline over 4 years. Slower ‘usual’ gait speed (but not dual-task or maximal speed) at baseline was associated with greater likelihood of incident errors on the MoCA at 4 years, which persisted on controlling for T2DM status, age, sex and baseline cognition (B: -0.22 [95% Confidence Interval: −0.41, −0.03, p = 0.03]). Slower maximal (B: −3.07, −6.06, −0.08, p = 0.04) and cognitive dual-task (B:-1.98, −3.61, −0.17, p = 0.03) speed were both associated with significantly poorer delayed memory performance after 4 years. Conclusion In middle-aged individuals with known dementia risk factors, gait speed may be a useful adjunct to identify those at greatest risk of longitudinal cognitive decline. Whilst usual gait speed was associated with a decline in global cognition, fast and dual task gait speed were associated with longitudinal decline in delayed memory performance.

A Comparative Analysis of Symmetry Indices for Spatiotemporal Gait Features in Early Parkinson's Disease.

This study compared the five most commonly used equations for calculating gait symmetry in discrete variables among Parkinson's disease patients. Twelve patients (five women and seven men) performed ten consecutive gait trials on a 10 m walkway. Gait data were collected using eight optoelectronic cameras (100 fr/s). The analysis focused on various spatiotemporal parameters, including cadence, step time, stride time, single support, double support, walking speed, step length, stride length, step width, and foot angle. Five symmetry indices were calculated for each trial rather than averaging the ten recorded trials. The variability in and reliability of each symmetry equation were assessed using the coefficient of variation (CV) and intraclass correlation coefficient (ICC), respectively. Additionally, Bland-Altman plots were produced to visualize the agreement between each pair of methods for each spatiotemporal parameter. The results revealed that the symmetry ratio method exhibited lower variability and higher reliability compared with the other four indices across all spatiotemporal gait parameters. However, it was found that the reliability of a single trial was generally poor, regardless of the symmetry calculation formula used. Therefore, we recommend basing measurements of gait asymmetry in Parkinson's disease on multiple trials.

Effect of feeling the phantom sensation during gait on spatiotemporal gait characteristics in individuals with transtibial amputation

Effect of feeling the phantom sensation during gait on spatiotemporal gait characteristics in individuals with transtibial amputation

Quantification of spinal ataxia in dogs with thoracolumbar spinal cord injury.

The clinical sign of ataxia is related to several neurological diseases and is seen in conjunction with paresis in dogs with spinal cord injury (SCI). Endeavours to objectify canine spinal ataxia in SCI remain limited. The aim of this clinical study was to determine and quantify differences between gait characteristics of ataxic dogs with thoracolumbar myelopathy and healthy control dogs using a computer-and treadmill-based gait analysis system. Five dogs with spinal ataxia and six healthy dogs underwent video-and computer-assisted gait analysis while walking on a four-ground reaction force plate treadmill system (maximum speed of 0.7 m/s). Spatio-temporal and kinetic gait characteristics regarding the dogs' locomotion were analysed with a focus on the individual coefficient of variation (CV), as a potential measure for quantification of the level of ataxia. Ataxic dogs with thoracolumbar SCI showed no effect on symmetry indices but higher variability in spatio-temporal and kinetic gait parameters mainly in the pelvic, but also in the thoracic limbs. Double support phase of the individual limb was prolonged in SCI dogs at the cost of the single support and swing phase. Reduced peaks of ground reaction forces (GRF) could potentially be explained by reduction of muscle strength, as a strategy of avoiding falling by taking enthusiastic steps, or by alteration of the rhythmogenic spinal circuits between the pelvic and thoracic limb pattern generators.

Early Development of Locomotion in the Term Piglet Model: Does Size Matter?

Intrauterine undernutrition in humans typically results in low birth weight ([small for gestational age] SGA) and delayed postnatal neuromotor maturation. Since SGA and intrauterine growth retardation are also common in domestic pigs, piglets are premised as models to study delayed motor development. Applied to the locomotor paradigm, however, questions emerge: (i) how to map the developmental time scale of the precocial model onto the altricial target species and (ii) how to distinguish size from maturation effects? Gait data were collected at self-selected voluntary walking speed during early development (0-96 hours postpartum; pp) for SGA- and normal ([appropriate for gestational age] AGA) piglets. Dimensionless spatiotemporal gait characteristics (according to dynamic similarity) become invariant already after 4 hours pp, suggesting rapid postnatal neuromotor maturation. Moreover, dimensionless gait data are largely identical for SGA- and AGA-siblings, indicating that primarily size effects explain absolute locomotor differences. This is further supported by (i) normalized force-generating capacity of limb muscles, (ii) joint kinematics (<10 hours pp), and (iii) normalized ground reaction forces (<5 days pp) being indifferent between SGA- and AGA- piglets. Furthermore, predictive modeling based on limb joint kinematics is unable to discern the majority of SGA- from AGA-piglets (<10 hours pp). All this leads to the conclusion that, although smaller than the AGA piglets in absolute terms, SGA-piglets mature (neuromechanically speaking) just like, and equally fast as their AGA littermates. Yet, it remains a fact that early SGA piglets are reported to be less mobile, less vital, and less competitive than their AGA siblings (even often die before day 3 pp). This conspicuous difference likely results from the energy level (blood glucose and glycogen) and its mobilization being considerably different between the piglet categories during early development.