Gait Measures Research Articles

Validation of fNIRS measurement of executive demand during walking with and without dual-task in younger and older adults and people with Parkinson’s disease

BackgroundWalking with a concurrent cognitive task (dual-task walking) can pose a challenge to some populations due to aging or neurodegenerative disease. These tasks require cognitive resources involving the prefrontal cortex and can be studied using functional near-infrared spectroscopy (fNIRS). An important step in understanding fNIRS measures during such walking tasks is validating that measures reflect the demands of the tasks and not confounding sources or movement artifacts. AimThis study aimed to investigate the validity of fNIRS measures of prefrontal cortex activity as an indicator of executive demand during usual walking (single-task) and dual-task walking against clinical and objective measures of motor behavior in young adults, older adults, and people with Parkinson’s disease (PD), by evaluating several validation hypotheses. MethodsIn total, 133 participants were recruited from younger adults (18–50 years, n = 42), older adults (≥60 years, n = 49) and people with PD (≥60 years, n = 42). Activity in the prefrontal cortex during walking with and without an auditory Stroop task was measured with fNIRS. A combined hemoglobin measure (correlation-based signal improvement, CBSI) was calculated for use in a region of interest analysis in the dorsolateral prefrontal cortex (dlPFC). Pre-registered hypotheses regarding convergent validity, discriminant validity and known group validity were tested. An exploratory analysis of different hemoglobin measures was also performed. ResultsIncreases in dlPFC activity were found from single- to dual-task walking in the younger adults group and from rest to single-task walking in the older adults and PD groups. In line with hypotheses, a positive relationship was found between between dlPFC activity during dual-task walking and dual-task cost in the younger adults group, as well as a positive relationship to step time variability during single-task walking and a negative relationship to walking speed during single-task walking in the PD group. However, several clinical and gait measures lacked a relationship with dlPFC activity. ConclusionThe fNIRS results point towards the CBSI measure of dlPFC activity being a valid measure of executive demand during both single and dual-task walking. Some relationships between clinical and gait measures and brain activity during walking need further investigation.

Validation of Linear and Nonlinear Gait Variability Measures Derived From a Smartphone System Compared to a Gold-Standard Footswitch System During Overground Walking.

Smartphones, with embedded accelerometers, may be a viable method to monitor gait variability in the free-living environment. However, measurements estimated using smartphones must first be compared to known quantities to ensure validity. This study assessed the validity and reliability of smartphone-derived gait measures compared to a gold-standard footswitch system during overground walking. Seventeen adults completed three 8-minute overground walking trials during 3 separate visits. The stride time series was calculated as the time difference between consecutive right heel contact events within the footswitch and smartphone-accelerometry signals. Linear (average stride time, stride time standard deviation, and stride time coefficient of variation) and nonlinear (fractal scaling index, approximate entropy, and sample entropy) measures were calculated for each stride time series. Bland-Altman plots with 95% limits of agreement assessed agreement between systems. Intraclass correlation coefficients assessed reliability across visits. Bland-Altman plots revealed acceptable limits of agreement for all measures. Intraclass correlation coefficients revealed good-to-excellent reliability for both systems, except for fractal scaling index, which was moderate. The smartphone system is a valid method and performs similarly to gold-standard research equipment. These findings suggest the development and implementation of an inexpensive, easy-to-use, and ubiquitous telehealth instrument that may replace traditional laboratory equipment for use in the free-living environment.

A Potential-Real-Time Thigh Orientation Prediction Method Based on Two Shanks-Mounted IMUs and Its Clinical Application

The detection and evaluation of gait kinematics is vital for patient diagnosis and rehabilitation. Aiming at limitations of commonly used optical capture and wearable sensing systems in clinical applications, this paper proposes a thigh attitude angle prediction method based on the hip error tolerance from the kinematic data of two shank-mounted IMUs (Inertial Measurement Unit). The novelties of the proposed method are summarized as follows: i) It develops a parallel approach to regress variation of hip error tolerance for different subjects. This parallel approach, by simultaneously deconstructing the shank kinematics data via different regression algorithm including support vector machine, boosting tree, and stepwise linearity regression, is able to well accommodate the characteristics of both health subjects and patients. ii) It develops some evaluation indices based on gait symmetry, consistency, and activity to fully evaluate the human lower limbs motion performance in gait by only two IMUs. The effectiveness of the proposed method is verified by the experimental results among 8 healthy subjects and 16 cerebral infarction patients. For the healthy subjects, the estimated error of thigh prediction compared with Xsens and Vicon are <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.3 \pm {0.3^ \circ }$</tex-math> </inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.5 \pm {0.7^ \circ }$</tex-math> </inline-formula> , respectively. For the patients, the estimated error compared with Xsens-measured angle is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.8 \pm {1.7^ \circ }$</tex-math> </inline-formula> . Its broad significance in actual intelligent healthcare and robotics-assisted rehabilitation is three-fold: First, it is a recursive real-time method as it only needs data from the previous gait cycle to predict the thigh angle. Second, its accuracy meets the actual clinical needs. Third, it is a low-cost method that only needs two IMUs and has high potentials of clinical applications. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Gait is of great significance to quantify the degree of movement disorders in clinical practice, and the existing equipment is rarely able to meet the needs of full dimension, low cost, and low place constraints in clinical practice. This paper presents an innovative gait kinematic prediction method, which only uses two IMUs attached to the shanks to predict the orientation of thigh. Compared with related works, the proposed methods have achieved high-precision, real-time and low-cost acquisition. This paper is inspired by the problems of large number, high motion interference, and high cost of wearable gait measurement devices in clinical practice. The proposed method have potential to be integrated into exoskeletons and medical walking AIDS, which could greatly improve the comfort and control precision of the wearable system.

Gait Adaptation to Asymmetric Hip Stiffness Applied by a Robotic Exoskeleton.

Wearable exoskeletons show significant potential for improving gait impairments, such as interlimb asymmetry. However, a more profound understanding of whether exoskeletons are capable of eliciting neural adaptation is needed. This study aimed to characterize how individuals adapt to bilateral asymmetric joint stiffness applied by a hip exoskeleton, similar to split-belt treadmill training. Thirteen unimpaired individuals performed a walking trial on the treadmill while wearing the exoskeleton. The right side of the exoskeleton acted as a positive stiffness torsional spring, pulling the thigh towards the neutral standing position, while the left acted as a negative stiffness spring pulling the thigh away from the neutral standing position. The results showed that this intervention applied by a hip exoskeleton elicited adaptation in spatiotemporal and kinetic gait measures similar to split-belt treadmill training. These results demonstrate the potential of the proposed intervention for retraining symmetric gait.

Exploring unsupervised feature extraction of IMU-based gait data in stroke rehabilitation using a variational autoencoder.

Variational AutoEncoders (VAE) might be utilized to extract relevant information from an IMU-based gait measurement by reducing the sensor data to a low-dimensional representation. The present study explored whether VAEs can reduce IMU-based gait data of people after stroke into a few latent features with minimal reconstruction error. Additionally, we evaluated the psychometric properties of the latent features in comparison to gait speed, by assessing 1) their reliability; 2) the difference in scores between people after stroke and healthy controls; and 3) their responsiveness during rehabilitation. We collected test-retest and longitudinal two-minute walk-test data using an IMU from people after stroke in clinical rehabilitation, as well as from a healthy control group. IMU data were segmented into 5-second epochs, which were reduced to 12 latent-feature scores using a VAE. The between-day test-retest reliability of the latent features was assessed using ICC-scores. The differences between the healthy and the stroke group were examined using an independent t-test. Lastly, the responsiveness was determined as the number of individuals who significantly changed during rehabilitation. In total, 15,381 epochs from 107 people after stroke and 37 healthy controls were collected. The VAE achieved data reconstruction with minimal errors. Five latent features demonstrated good-to-excellent test-retest reliability. Seven latent features were significantly different between groups. We observed changes during rehabilitation for 21 and 20 individuals in latent-feature scores and gait speed, respectively. However, the direction of the change scores of the latent features was ambiguous. Only eleven individuals exhibited changes in both latent-feature scores and gait speed. VAEs can be used to effectively reduce IMU-based gait assessment to a concise set of latent features. Some latent features had a high test-retest reliability and differed significantly between healthy controls and people after stroke. Further research is needed to determine their clinical applicability.

Recommendations for Screening Measures in Undergraduate Freshman Female Dance Majors

ABSTRACT The purpose of this study was to assess physical performance and injury risk factors during an undergraduate dance program. The measures examined in this study were joint range of motion (JROM) and manual voluntary isokinetic contraction (MMT) of the hip and ankle, gait, and prevalence of pain and injury. The 30 female participants had been dancing for an average of 13 years. Fifty-two percent reported having had one or more injuries during their dance training years, and 85% reported experiencing pain in the legs or back. Asymmetry was found between the right and left legs during the JROM, MMT, and gait measures. Gait velocity, cadence, JROM, and MMT measures were at the lower end of the reported normal ranges. The results of this study provide evidence for recommending that undergraduate dance programs assess their students using a variety of measures to improve physical performance and reduce injury rates.

ASSOCIATIONS BETWEEN GAIT MEASURES AND QUADRICEPS MUSCLE THICKNESS AS MEASURED BY POINT-OF-CARE ULTRASOUND

Abstract Gait parameters and sarcopenia predict falls risk which is one of the major causes of both mortality and morbidity among older adults. Our objective was to evaluate whether anterior thigh muscle measured by point-of-care ultrasound is significantly associated with standard gait measures. All subjects were referred from ambulatory geriatric medicine clinics at an academic center. Quadriceps muscle thickness was measured by a portable ultrasound device. Gait variables were measured by the patient walking for 6 minutes. The primary response variables were gait variables, and the predictor variables were age, biological sex, body mass index, and MT. Univariate and multivariate regression analyses were performed. A total of 150 participants were recruited from geriatric medicine clinics. Muscle thickness was measured in 149 participants and the mean (SD) was 1.91 (0.52) (median 1.82 cm, 0.96 to 3.68 cm). Among all the gait variables, average swing time (P = 0.010) and average stance time (P = 0.010) were correlated significantly with muscle thickness. Muscle thickness also showed a negative association with step time variability percent (P = 0.005). Muscle thickness (P = 0.046), age (P = 0.025), and gender (P = 0.047) had a statistically significant association with present step time variability. PoCUS showed a significant association with average swing time, average stance time, and step time variability all of which are predictive of future falls risk. Although more work needs to be done, PoCUS is a reliable and feasible muscle measurement method, which could lead to faster clinical decision-making and falls risk assessment.

The validity of smartphone-based spatiotemporal gait measurements during walking with and without head turns: Comparison with the GAITRite® system

Smartphone accelerometry has potential to provide clinicians with specialized gait analysis not available in most clinical settings. The Gait&Balance Application (G&B App) uses smartphone accelerometry to assess spatiotemporal gait parameters under two conditions: walking looking straight ahead and walking with horizontal head turns. This study investigated the validity of G&B App gait parameters compared with the GAITRite® pressure-sensitive walkway. Healthy young and older adults (age range 21–85 years) attended a single session where a smartphone was secured over the lumbosacral junction. Data were collected concurrently with the app and GAITRite® systems as participants completed the two walking conditions. Spatiotemporal gait parameters for 54 participants were determined from both systems and agreement evaluated with partial Pearson’s correlation coefficients and limits of agreement. The results demonstrated moderate to excellent validity for G&B App measures of step time (rp 0.97, 95 % CI [0.96, 0.98]), walking speed (rp 0.83 [0.78, 0.87]), and step length (rp 0.74, [0.66, 0.80]) when walking looking straight ahead, and results were comparable with head turns. The validity of walking speed and step length measures was influenced by sex and height. G&B App measures of step length variability, step time variability, step length asymmetry, and step time asymmetry had poor validity. The G&B App has potential to provide valid measures of unilateral and bilateral step time, unilateral and bilateral step length, and walking speed, under two walking conditions in healthy young and older adults. Further research should validate this tool in clinical conditions and optimise the algorithm for demographic characteristics.

Digital gait markers to potentially distinguish fragile X-associated tremor/ataxia syndrome, Parkinson's disease, and essential tremor.

Fragile X-associated tremor/ataxia syndrome (FXTAS), a neurodegenerative disease that affects carriers of a 55-200 CGG repeat expansion in the fragile X messenger ribonucleoprotein 1 (FMR1) gene, may be given an incorrect initial diagnosis of Parkinson's disease (PD) or essential tremor (ET) due to overlapping motor symptoms. It is critical to characterize distinct phenotypes in FXTAS compared to PD and ET to improve diagnostic accuracy. Fast as possible (FP) speed and dual-task (DT) paradigms have the potential to distinguish differences in gait performance between the three movement disorders. Therefore, we sought to compare FXTAS, PD, and ET patients using quantitative measures of functional mobility and gait under self-selected (SS) speed, FP, and DT conditions. Participants with FXTAS (n = 22), PD (n = 23), ET (n = 20), and controls (n = 20) underwent gait testing with an inertial sensor system (APDM™). An instrumented Timed Up and Go test (i-TUG) was used to measure movement transitions, and a 2-min walk test (2MWT) was used to measure gait and turn variables under SS, FP, and DT conditions, and dual-task costs (DTC) were calculated. ANOVA and multinomial logistic regression analyses were performed. PD participants had reduced stride lengths compared to FXTAS and ET participants under SS and DT conditions, longer turn duration than ET participants during the FP task, and less arm symmetry than ET participants in SS gait. They also had greater DTC for stride length and velocity compared to FXTAS participants. On the i-TUG, PD participants had reduced sit-to-stand peak velocity compared to FXTAS and ET participants. Stride length and arm symmetry index during the DT 2MWT was able to distinguish FXTAS and ET from PD, such that participants with shorter stride lengths were more likely to have a diagnosis of PD and those with greater arm asymmetry were more likely to be diagnosed with PD. No gait or i-TUG parameters distinguished FXTAS from ET participants in the regression model. This is the first quantitative study demonstrating distinct gait and functional mobility profiles in FXTAS, PD, and ET which may assist in more accurate and timely diagnosis.

Gait and balance measures and tools for early detection of dementias: A systematic review

AbstractBackgroundBrain mechanisms supporting gait and balance are vulnerable to early neurodegenerative changes and identification of alterations in these behaviours has great potential for the detection of different dementia‐causing diseases. This systematic review aimed to identify the most promising gait and balance measures for early detection and technologies suitable for capturing these data and future deployment at scale in clinical practice.MethodWe performed literature searches following PRISMA guidelines across two databases (PubMed and Web of Science). Keywords were designed to capture literature on preclinical and prodromal stages of dementias and high‐risk populations that investigated gait and balance cross‐sectionally and longitudinally. We included studies written in English and published until August 2022. Exclusion criteria included case, intervention, and uncontrolled studies. Studies on established dementia were also excluded.ResultFrom 2086 titles, 182 were screened at full‐text stage, with 95 included in the systematic review. Most investigated gait and balance in association with preclinical or prodromal AD [n = 38], Parkinson’s [n = 18] or Lewy body [n = 17] dementias, with very few on FTD [n = 1] or vascular cognitive impairment [n = 2]. Twenty‐two studies utilised a term “dementia”. Fifty‐one unique metrics were used to characterise gait, with 12 balance metrics identified. The most common differentiative metric was gait speed (n = 44 studies), which declined across populations, and was often measured using a stopwatch. Twenty‐seven studies utilised pressure carpets, force platforms and body‐worn sensors, enabling quantification of finer‐grained metrics of gait and balance, e.g., variability, asymmetry, and movement jerkiness. These metrics were shown to be sensitive, especially under dual‐task conditions, and specific to different pre‐dementia states. Only three studies utilised smartphone‐based assessment, potentially the most scalable platform for at‐scale application.ConclusionGait and balance measures can detect early neurodegenerative diseases but measurement using scalable off‐the‐shelf technologies in real‐world settings is currently limited. These findings will guide the selection of gait‐measuring devices for the next generation EDoN (https://edon‐initiative.org) toolkit for early detection.

A multimodal digital biomarker of functional deficits in early‐stage Alzheimer’s disease: results of the RADAR‐AD study

AbstractBackgroundRemote monitoring technologies (RMTs), such as smartphone apps, smartwatches, and in‐home sensors, are rapidly changing the way functional and cognitive performance is measured in Alzheimer’s disease (AD) patients. Here, we present results from the European RADAR‐AD study on the use of multimodal data streams for the identification of functional deficits across all syndromic stages of AD.MethodFour study groups (Healthy controls (HC), preclinical AD (pre. AD), prodromal AD (pro. AD), and mild AD) were included in this cross‐sectional study. The RMT features (gait measures from Timed up and Go (TUG), Dual Task Effect (DTE) using physilog, acoustic features from Speech task in Mezurio, neurocognitive function using Altoida, managing finances with Banking app) with in‐clinic neuropsychological (NP) tests, activities of daily living with Amsterdam IADL and demographics (age, gender, education years) were analyzed for different combinations of the multimodal digital biomarker of disease stage in AD across different pairwise comparisons. The analysis includes data from 175 participants (HC = 67, Pre.AD = 26, Pro.AD = 50, Mild AD = 32) collected for 8 weeks. An extreme gradient boosting (XGBoost) machine learning model was trained to obtain a multimodal biomarker of AD disease stage with repeated cross‐validation (5‐fold with 4 repeats) (Figure 1). This investigation is part of the ongoing RADAR‐AD study.ResultThe multimodal combination of RMTs achieved a mean AUC of &gt; 0.60 in all pairwise comparisons with a mean AUC &gt; 0.65 for HC vs (Pre.AD, Pro.AD and Mild) (Figures 2 and 3). The addition of NP tests increases the performance considerably across all pairwise comparisons except HC vs Pre.AD.ConclusionOur results highlight the advantage of combining RMTs to identify functional deficits in the early stage of AD. In particular, in prodromal and mild AD patients, a combined signal shows much more strength compared to individual tests. This work has received support from the EU/EFPIA Innovative Medicines Initiative Joint Undertaking (grant No 806999). www.imi.europa.eu. This communication reflects the views of the RADAR‐AD consortium and neither IMI nor the European Union and EFPIA are liable for any use that may be made of the information contained herein.

Sensitivity of NIH ToolBox Motor tests to detect amyloid positivity in cognitively normal older adults

AbstractBackgroundChanges in motor functioning and particularly lower motor functioning such as balance and gait have been shown to be an early predictor of dementia (Kueper et al 2017). Based on these results, it has been suggested that balance and gait measures should be a regular component of a clinical dementia risk evaluation. The NIH Toolbox (NIHTB) has developed motor‐based tasks along with its cognitive battery. The aim of this study was to evaluate the utility of several of the NIHTB‐Motor task performance to predict amyloid status in cognitively normal older adults.MethodData from 45 cognitively normal adults age 65+ who were part of a larger study examining changes in driving behavior as predictors of cognitive decline were included in this study. Participants were divided into two groups based on PiB PET imaging and amyloid positivity determined using a centiloid scale: values ≥20 were considered positive, values ≤10 were negative; values between 10 and 20 were considered indeterminate and not included (n = 9)). Twenty nine participants were amyloid negative (OCN‐) and 16 were positive (OCN+). Data from the standing balance, grip strength and 2‐minute walking endurance tasks were analyzed using independent sample t‐tests.ResultThere were no differences in age or education between the two groups. However, there was a significant correlation between age and standing balance uncorrected score. As a result, fully corrected scores were used for this analysis. There was a significant difference between the OCN‐ and OCN+ for the fully corrected standing balance standard score (t = 1.7, p&lt;0.04) such that the OCN+ group had lower balance scores compared to the OCN‐ group. No significance differences were found for grip strength or the 2‐minute walking endurance task.ConclusionThe Standing Balance task from the NIHTB may be a sensitive marker to identify individuals who are at higher risk of developing cognitive impairment and dementia. The NIHTB tasks were designed to be easy to administer in a relatively short amount of time with normative data, and these results suggest the Standard Balance task should be included as a routine measure of dementia risk.

Non‐imaging measures to predict variability in white matter changes relevant to VCID

AbstractBackgroundSmall vessel disease (SVD) is a common vascular contributor to cognitive impairment and dementia. In this work, we evaluated and compared non‐imaging measures that can predict variability in SVD related white matter (WM) damage in a population‐based sample.MethodWe identified 752 participants in the population‐based sample of Mayo Clinic Study of Aging (≥50 years of age), who had at least 2 diffusion tensor imaging (DTI) scans and white matter hyperintensity (WMH) assessments along with baseline demographics, systemic vascular health, lifestyle, motor impairment, behavioral symptoms, and plasma markers (Summarized in Figure 1). We computed the WM markers of SVD: fractional anisotropy of genu of the corpus callosum (Genu‐FA) representative of early WM changes, WMH representative of late WM changes, and Vascular WM score (combination of Genu‐FA and WMH). Using all available data, we fit linear mixed effect models and evaluated predictors of WM markers by considering each category of features independently (with demographics) as well as by combining all categories of featuresResultPlasma, gait, systemic vascular, and life‐style measures were significant predictors of all three WM markers of SVD (Figure 2). The predictors of early and late WM slightly differed such that there were no additional significant contributions from behavioral and plasma measures to Genu‐FA. In the final composite models shown in Table 1, higher gait speed, lower number of cardiovascular and metabolic conditions (CMC) and lower Unified Parkinson’s disease rating scale scores were associated with better Genu‐FA whereas higher CMC, Beck Anxiety Inventory scores (BAI), and glial acidic protein (GFAP) levels were associated with higher burden of WMH.ConclusionOur observations evaluated the non‐imaging markers of greater WM damage that are relevant to vascular contributions to cognitive impairment to dementia (VCID). We found that in addition to higher vascular risk, worse plasma GFAP, gait measures, and BAI scores were predictors of greater WM damage. Some non‐imaging markers were more associated with early WM changes and others with late WM changes.

Effects of Habituation on Spatiotemporal Gait Measures in Younger Adults

Abstract Virtual reality (VR) has potential benefits for training and rehabilitation (Lei et al., 2019; Rutkowski et al., 2020). In some instances, understanding how performance in VR relates to performance in the real world is a necessary step in building effective environments. This study was designed to determine whether gait in VR changes over time (adaptation), and whether it approaches natural environment performance over time (habituation). Nineteen young adults walked over a gait mat in a real hallway for five minutes and a virtual hallway for three 10-minute blocks. Results indicated that gait was significantly different in the natural environment compared to the first five minutes of immersion (p &amp;lt; 0.05). Group averages also demonstrated that after 10–15 minutes of immersion, there was no statistical difference between the virtual and natural environments across any of the gait measures (p &amp;gt; 0.05). However, individual differences in gait behavior were also evident. While some participants matched performance between virtual and natural environments by the end of the 30-minute session, others did not. These findings provide evidence that individuals adapt differently to motor tasks in VR and suggest that when gait is required in VR, the environment may need to be tailored to the individual to achieve desired outcomes.

Palmitoylethanolamide Modulates Gait By Targeting Purkinje Cells in the Brain of Sickle Mice

Chronic pain is a major comorbidity of sickle cell disease (SCD). Transgenic humanized (BERK) sickle mice recapitulate many clinical features of SCD, including chronic pain. Motor impairments concomitant with increased pyknotic cerebellar Purkinje cells occur in BERK sickle mice. Neurological, neuropathic, and immunological factors, including Purkinje cell damage and chronic inflammation, may contribute to peripheral and central mechanisms that drive gait dysfunction. Pathological changes in Purkinje cells of sickle mice include a significantly greater number of pyknotic cells with smudged nuclei, condensed chromatin, and lack of well-formed nucleoli, &amp; may contribute to alterations in gait as Purkinje cells regulate sensory-motor functions. Subjects with SCD often use cannabinoids for pain relief, but the intoxicating effects and social stigma associated with their use represent barriers to utilizing this treatment. Palmitoylethanolamide (PEA) is a non-intoxicating endocannabinoid-like lipid mediator naturally produced throughout the body that may act via indirect cannabinoid receptor activation. In clinical studies associated with movement and pain, including chronic arthritic and multiple sclerosis, PEA has demonstrated a reduction of motor dysfunction and pain. Therefore, we examined the effect of PEA on hyperalgesia and gait in humanized HbSS-BERK sickle (HbSS) mice. We utilized ~6-month-old female, HbSS mice, expressing &amp;gt;99% human α- and ßS- sickle hemoglobin and complete knockout of murine α- and β-globins. Mice were treated with PEA (ip, 20 mg/kg/d) or vehicle (7.5% DMSO, 7.5% Tween 20, in sterile saline) for 2 weeks (2-wks). Gait measurements were gathered for each mouse at baseline (BL) and 2-wks post treatment during normal walking in MouseWalker's transparent corridor with a floor panel mounted with LED lights to produce a detectable touch sensor. Physical contact of paw pads with the surface during natural walking disrupts the light path, causing frustrated total internal reflection detected by a high-speed camera and analyzed using artificial intelligence-based algorithms for gait parameters. Mechanical hyperalgesia was analyzed as paw withdrawal frequency (PWF) in response to 10 applications of 1.0 g von Frey monofilaments. Complete blood counts and brain histopathology on hematoxylin- and eosin-stained sections were evaluated after 2-wks post-treatment. We observed that PEA ameliorates features of dysfunctional gait in HbSS mice. PEA-treated mice show increased limb swing speed (~30%, P&amp;lt;0.01) and shortened swing duration (20%, P&amp;lt;0.05) without affecting walking speed (P &amp;gt;0.05) Vs. vehicle-treated mice. Also, PEA significantly reduced body instability (~75%, P&amp;lt;0.05) and improved stance duration (~65%, P&amp;lt;0.05) Vs. BL. This suggests improved limb coordination and reduced sensitivity to mechanical forces during walking. Complementary to gait changes, PEA significantly decreased mechanical hyperalgesia, indicated by lower PWF, 1-hour following initial treatment and following 2-wks post-treatment (~55%, P&amp;lt; 0.001 and ~65%, P&amp;lt; 0.001, respectively) Vs. BL. PEA decreased the number of circulating white blood cells (P&amp;lt; 0.05), with the greatest reduction in circulating lymphocytes (P&amp;lt; 0.1). PEA had no adverse effects on organ weight Vs. vehicle treatment. PEA-treated mice showed a significant reduction in pyknotic cerebellar Purkinje cells (~23.9% p&amp;lt;.006) in the brain Vs. vehicle. Thus, Purkinje cell pathology may contribute to impaired gait, and PEA may improve motor coordination by reducing Purkinje cell damage. We provide the first evidence that PEA administration in a humanized sickle mouse model improves features of gait, restores Purkinje cell morphology, and reduces inflammation. Our data demonstrate that PEA may have a disease-modifying effect and restoration of sensory-motor function in SCD. Impairment in gait may also be due to avascular necrosis. Therefore gait measurements may provide a non-invasive analysis of movement associated pathology and pain as well as effectiveness of targeted interventions.

Interpretable classification for multivariate gait analysis of cerebral palsy

BackgroundThe Gross Motor Function Classification System (GMFCS) is a widely used tool for assessing the mobility of people with Cerebral Palsy (CP). It classifies patients into different levels based on their gross motor function and its level is typically determined through visual evaluation by a trained expert. Although gait analysis is commonly used in CP research, the functional aspects of gait patterns has yet to be fully exploited. By utilizing the gait patterns to predict GMFCS, we can gain a more comprehensive understanding of how CP affects mobility and develop more effective interventions for CP patients.ResultIn this study, we propose a multivariate functional classification method to examine the relationship between kinematic gait measures and GMFCS levels in both normal individuals and CP patients with varying GMFCS levels. A sparse linear functional discrimination framework is utilized to achieve an interpretable prediction model. The method is generalized to handle multivariate functional data and multi-class classification. Our method offers competitive or improved prediction accuracy compared to state-of-the-art functional classification approaches and provides interpretable discriminant functions that can characterize the kinesiological progression of gait corresponding to higher GMFCS levels.ConclusionWe generalize the sparse functional linear discrimination framework to achieve interpretable classification of GMFCS levels using kinematic gait measures. The findings of this research will aid clinicians in diagnosing CP and assigning appropriate GMFCS levels in a more consistent, systematic, and scientifically supported manner.

Monitoring Gait and Physical Activity of Elderly Frail Individuals in Free-Living Environment: A Feasibility Study

Introduction: Frailty is conventionally diagnosed using clinical tests and self-reported assessments. However, digital health technologies (DHTs), such as wearable accelerometers, can capture physical activity and gait during daily life, enabling more objective assessments. In this study, we assess the feasibility of deploying DHTs in community-dwelling older individuals, and investigate the relationship between digital measurements of physical activity and gait in naturalistic environments and participants’ frailty status, as measured by conventional assessments. Methods: Fried Frailty Score (FFS) was used to classify fifty healthy individuals as non-frail (FFS = 0, n/female = 21/11, mean ± SD age: 71.10 ± 3.59 years), pre-frail (FFS = 1–2, n/female = 23/9, age: 73.74 ± 5.52 years), or frail (FFS = 3+, n/female = 6/6, age: 70.70 ± 6.53 years). Participants wore wrist-worn and lumbar-worn GENEActiv accelerometers (Activinsights Ltd., Kimbolton, UK) during three in-laboratory visits, and at-home for 2 weeks, to measure physical activity and gait. After this period, they completed a comfort and usability questionnaire. Compliant days at-home were defined as follows: those with ≥18 h of wear time, for the wrist-worn accelerometer, and those with ≥1 detected walking bout, for the lumbar-worn accelerometer. For each at-home measurement, a group analysis was performed using a linear regression model followed by ANOVA, to investigate the effect of frailty on physical activity and gait. Correlation between at-home digital measurements and conventional in-laboratory assessments was also investigated. Results: Participants were highly compliant in wearing the accelerometers, as 94% indicated willingness to wear the wrist device, and 66% the lumbar device, for at least 1 week. Time spent in sedentary activity and time spent in moderate activity as measured from the wrist device, as well as average gait speed and its 95th percentile from the lumbar device were significantly different between frailty groups. Moderate correlations between digital measurements and self-reported physical activity were found. Conclusions: This work highlights the feasibility of deploying DHTs in studies involving older individuals. The potential of digital measurements in distinguishing frailty phenotypes, while unobtrusively collecting unbiased data, thus minimizing participants’ travels to sites, will be further assessed in a follow-up study.

PD‐Gait: Contactless and privacy‐preserving gait measurement of Parkinson's disease patients using acoustic signals

AbstractIn this article, we propose a mobile edge computing (MEC)‐related system named PD‐Gait, which can measure gait parameters of Parkinson's disease patients in a contactless and privacy‐preserving manner. We utilize inaudible acoustic signals and band‐pass filters to achieve privacy data protection in the physical layer. The proposed framework can be easily deployed in the mobile end of MEC, and hence release the edge server in cybersecurity attacks fighting. The gait parameters include stride cycle time length and moving speed, and hence providing an objective basis for the doctors' judgment. PD‐Gait utilizes acoustic signals in bands from 16 to 23 kHz to achieve device‐free sensing, which would release both doctors and patients from the tedious wearing process and psychological burden caused by traditional wearable devices. To achieve robust measurement, we propose a novel acoustic ranging method to avoid “broken tones” and “uneven peak distribution” in the received data. The corresponding ranging accuracy is 0.1 m. We also propose auto‐focus micro‐Doppler features to extract robust stride cycle time length, and can achieve an accuracy of 0.052 s. We deployed PD‐Gait in a brain hospital and collected data from 8 patients. The total walked distance is over 330 m. From the overall trend, our results are highly correlated with the doctor's judgment.

Investigating the agreement between force platform and plantar pressure insole data in barefoot and skating-specific footwear conditions across four different movement patterns

Conducting applied sport research in real-world settings is challenged by the lack of portable instrumentation capable of producing valid and reliable data that is collected without interfering with the athletes’ movement and is meaningful to sport performance. The purpose of this study was to compare and contrast data collected simultaneously from a force platform and a plantar pressure insole to provide support for the use of the XSENSOR® plantar pressure insoles in a skating-performance application. Data was collected in two conditions. The barefoot condition consisted of the insole on a force platform in isolation and the in-skate condition consisted of the insole inside a speed skate boot on a force platform. A single-participant design was conducted whereby an injury-free female completed multiple trials in both conditions. A P6000 Force Platform (BTS Bioengineering Corp., MI, Italy) was used to collect force data and an X4 Foot and Gait Measurement Systems plantar pressure insole (XSENSOR® Technology Corporation, AB, Canada) was used to collect pressure data in the two conditions across four movement patterns; static stance (SS), anterior-posterior sway (AP), medial-lateral sway (ML), and lateral jump (LJ). Intraclass correlation coefficients (ICC) and Bland-Altman plots revealed excellent agreement between the cumulative centre of pressure path length (mm) measured from the force platform and insole in the barefoot condition and moderate to excellent agreement between impulse (Ns) measured from the force platform and insole, in both conditions. These outcomes provide researchers and practitioners with empirical support and confidence to employ the XSENSOR® plantar pressure insoles outside of a laboratory setting in a real-world skating environment to collect and analyse in-skate kinetic data during on ice performance.

A pilot observational study of gait changes over time before and after an unplanned hospital visit in long-term care residents with dementia

BackgroundOlder adults with dementia living in long-term care (LTC) have high rates of hospitalization. Two common causes of unplanned hospital visits for LTC residents are deterioration in health status and falls. Early detection of health deterioration or increasing falls risk may present an opportunity to intervene and prevent hospitalization. There is some evidence that impairments in older adults’ gait, such as reduced gait speed, increased variability, and poor balance may be associated with hospitalization. However, it is not clear whether changes in gait are observable and measurable before an unplanned hospital visit and whether these changes persist after the acute medical issue has been resolved. The objective of this study was to examine gait changes before and after an unplanned acute care hospital visit in people with dementia.MethodsWe performed a secondary analysis of quantitative gait measures extracted from videos of natural gait captured over time on a dementia care unit and collected information about unplanned hospitalization from health records.ResultsGait changes in study participants before hospital visits were characterized by decreasing stability and step length, and increasing step variability, although these changes were also observed in participants without hospital visits. In an age and sex-adjusted mixed effects model, gait speed and step length declined more quickly in those with a hospital visit compared to those without.ConclusionsThese results provide preliminary evidence that clinically meaningful longitudinal gait changes may be captured by repeated non-invasive gait monitoring, although a larger study is needed to identify changes specific to future medical events.