Ambient floor vibration sensing advances the accessibility of functional gait assessments for children with muscular dystrophies.

Muscular dystrophies (MD) are a group of genetic neuromuscular disorders that cause progressive weakness and loss of muscles over time, influencing 1 in 3500-5000 children worldwide. New and exciting treatment options have led to a critical need for a clinical post-marketing surveillance tool to confirm the efficacy and safety of these treatments after individuals receive them in a commercial setting. For MDs, functional gait assessment is a common approach to evaluate the efficacy of the treatments because muscle weakness is reflected in individuals' walking patterns. However, there is little incentive for the family to continue to travel for such assessments due to the lack of access to specialty centers. While various existing sensing devices, such as cameras, force plates, and wearables can assess gait at home, they are limited by privacy concerns, area of coverage, and discomfort in carrying devices, which is not practical for long-term, continuous monitoring in daily settings. In this study, we introduce a novel functional gait assessment system using ambient floor vibrations, which is non-invasive and scalable, requiring only low-cost and sparsely deployed geophone sensors attached to the floor surface, suitable for in-home usage. Our system captures floor vibrations generated by footsteps from patients while they walk around and analyzes such vibrations to extract essential gait health information. To enhance interpretability and reliability under various sensing scenarios, we translate the signal patterns of floor vibration to pathological gait patterns related to MD, and develop a hierarchical learning algorithm that aggregates insights from individual footsteps to estimate a person's overall gait performance. When evaluated through real-world experiments with 36 subjects (including 15 patients with MD), our floor vibration sensing system achieves a 94.8% accuracy in predicting functional gait stages for patients with MD. Our approach enables accurate, accessible, and scalable functional gait assessment, bringing MD progressive tracking into real life.

Gait classification for growing children with Duchenne muscular dystrophy.

Classifying gait patterns into homogeneous groups could enhance communication among healthcare providers, clinical decision making and clinical trial designs in boys with Duchenne muscular dystrophy (DMD). Sutherland's classification has been developed 40 years ago. Ever since, the state-of-the-art medical care has improved and boys with DMD are now longer ambulatory. Therefore, the gait classification requires an update. The overall aim was to develop an up-to-date, valid DMD gait classification. A total of 137 three-dimensional gait analysis sessions were collected in 30 boys with DMD, aged 4.6-17 years. Three classes were distinguished, which only partly aligned with increasing severity of gait deviations. Apart from the mildly affected pattern, two more severely affected gait patterns were found, namely the tiptoeing pattern and the flexion pattern with distinct anterior pelvic tilt and posterior trunk leaning, which showed most severe deviations at the ankle or at the proximal segments/joints, respectively. The agreement between Sutherland's and the current classification was low, suggesting that gait pathology with the current state-of-the-art medical care has changed. However, overlap between classes, especially between the two more affected classes, highlights the complexity of the continuous gait changes. Therefore, caution is required when classifying individual boys with DMD into classes.

Are Gait Patterns during In-Lab Running Representative of Gait Patterns during Real-World Training? An Experimental Study.

Biomechanical assessments of running typically take place inside motion capture laboratories. However, it is unclear whether data from these in-lab gait assessments are representative of gait during real-world running. This study sought to test how well real-world gait patterns are represented by in-lab gait data in two cohorts of runners equipped with consumer-grade wearable sensors measuring speed, step length, vertical oscillation, stance time, and leg stiffness. Cohort 1 (N = 49) completed an in-lab treadmill run plus five real-world runs of self-selected distances on self-selected courses. Cohort 2 (N = 19) completed a 2.4 km outdoor run on a known course plus five real-world runs of self-selected distances on self-selected courses. The degree to which in-lab gait reflected real-world gait was quantified using univariate overlap and multivariate depth overlap statistics, both for all real-world running and for real-world running on flat, straight segments only. When comparing in-lab and real-world data from the same subject, univariate overlap ranged from 65.7% (leg stiffness) to 95.2% (speed). When considering all gait metrics together, only 32.5% of real-world data were well-represented by in-lab data from the same subject. Pooling in-lab gait data across multiple subjects led to greater distributional overlap between in-lab and real-world data (depth overlap 89.3-90.3%) due to the broader variability in gait seen across (as opposed to within) subjects. Stratifying real-world running to only include flat, straight segments did not meaningfully increase the overlap between in-lab and real-world running (changes of <1%). Individual gait patterns during real-world running, as characterized by consumer-grade wearable sensors, are not well-represented by the same runner's in-lab data. Researchers and clinicians should consider "borrowing" information from a pool of many runners to predict individual gait behavior when using biomechanical data to make clinical or sports performance decisions.

Gait Pattern Analysis: Integration of a Highly Sensitive Flexible Pressure Sensor on a Wireless Instrumented Insole.

Gait phase monitoring wearable sensors play a crucial role in assessing both health and athletic performance, offering valuable insights into an individual's gait pattern. In this study, we introduced a simple and cost-effective capacitive gait sensor manufacturing approach, utilizing a micropatterned polydimethylsiloxane dielectric layer placed between screen-printed silver electrodes. The sensor demonstrated inherent stretchability and durability, even when the electrode was bent at a 45-degree angle, it maintained an electrode resistance of approximately 3 Ω. This feature is particularly advantageous for gait monitoring applications. Furthermore, the fabricated flexible capacitive pressure sensor exhibited higher sensitivity and linearity at both low and high pressure and displayed very good stability. Notably, the sensors demonstrated rapid response and recovery times for both under low and high pressure. To further explore the capabilities of these new sensors, they were successfully tested as insole-type pressure sensors for real-time gait signal monitoring. The sensors displayed a well-balanced combination of sensitivity and response time, making them well-suited for gait analysis. Beyond gait analysis, the proposed sensor holds the potential for a wide range of applications within biomedical, sports, and commercial systems where soft and conformable sensors are preferred.

The Analytical Validity of Stride Detection and Gait Parameters Reconstruction Using the Ankle-Mounted Inertial Measurement Unit Syde®.

The increasing use of inertial measurement units (IMU) in biomedical sciences brings new possibilities for clinical research. The aim of this paper is to demonstrate the accuracy of the IMU-based wearable Syde® device, which allows day-long and remote continuous gait recording in comparison to a reference motion capture system. Twelve healthy subjects (age: 23.17 ± 2.04, height: 174.17 ± 6.46 cm) participated in a controlled environment data collection and performed a series of gait tasks with both systems attached to each ankle. A total of 2820 strides were analyzed. The results show a median absolute stride length error of 1.86 cm between the IMU-based wearable device reconstruction and the motion capture ground truth, with the 75th percentile at 3.24 cm. The median absolute stride horizontal velocity error was 1.56 cm/s, with the 75th percentile at 2.63 cm/s. With a measurement error to the reference system of less than 3 cm, we conclude that there is a valid physical recovery of stride length and horizontal velocity from data collected with the IMU-based wearable Syde® device.

Ubiquitous Gait Analysis through Footstep-Induced Floor Vibrations.

Quantitative analysis of human gait is critical for the early discovery, progressive tracking, and rehabilitation of neurological and musculoskeletal disorders, such as Parkinson's disease, stroke, and cerebral palsy. Gait analysis typically involves estimating gait characteristics, such as spatiotemporal gait parameters and gait health indicators (e.g., step time, length, symmetry, and balance). Traditional methods of gait analysis involve the use of cameras, wearables, and force plates but are limited in operational requirements when applied in daily life, such as direct line-of-sight, carrying devices, and dense deployment. This paper introduces a novel approach for gait analysis by passively sensing floor vibrations generated by human footsteps using vibration sensors mounted on the floor surface. Our approach is low-cost, non-intrusive, and perceived as privacy-friendly, making it suitable for continuous gait health monitoring in daily life. Our algorithm estimates various gait parameters that are used as standard metrics in medical practices, including temporal parameters (step time, stride time, stance time, swing time, double-support time, and single-support time), spatial parameters (step length, width, angle, and stride length), and extracts gait health indicators (cadence/walking speed, left-right symmetry, gait balance, and initial contact types). The main challenge we addressed in this paper is the effect of different floor types on the resultant vibrations. We develop floor-adaptive algorithms to extract features that are generalizable to various practical settings, including homes, hospitals, and eldercare facilities. We evaluate our approach through real-world walking experiments with 20 adults with 12,231 labeled gait cycles across concrete and wooden floors. Our results show 90.5% (RMSE 0.08s), 71.3% (RMSE 0.38m), and 92.3% (RMSPE 7.7%) accuracy in estimating temporal, spatial parameters, and gait health indicators, respectively.

Validity Verification of Human Pose-Tracking Algorithms for Gait Analysis Capability.

Two-dimensional (2D) clinical gait analysis systems are more affordable and portable than contemporary three-dimensional (3D) clinical models. Using the Vicon 3D motion capture system as the standard, we evaluated the internal statistics of the Imasen and open-source OpenPose gait measurement systems, both designed for 2D input, to validate their output based on the similarity of results and the legitimacy of their inner statistical processes. We measured time factors, distance factors, and joint angles of the hip and knee joints in the sagittal plane while varying speeds and gaits during level walking in three in-person walking experiments under normal, maximum-speed, and tandem scenarios. The intraclass correlation coefficients of the 2D models were greater than 0.769 for all gait parameters compared with those of Vicon, except for some knee joint angles. The relative agreement was excellent for the time-distance gait parameter and moderate-to-excellent for each gait motion contraction range, except for hip joint angles. The time-distance gait parameter was high for Cronbach's alpha coefficients of 0.899-0.993 but low for 0.298-0.971. Correlation coefficients were greater than 0.571 for time-distance gait parameters but lower for joint angle parameters, particularly hip joint angles. Our study elucidates areas in which to improve 2D models for their widespread clinical application.

Patient with knee osteoarthritis demonstrates improved knee adduction moment after knee joint distraction: a case report.

In this article we report a case of a 53-year-old patient diagnosed with end-stage osteoarthritis (OA) of the knee. The patient underwent treatment with knee joint distraction (KJD) with the aim to postpone total knee arthroplasty and prevent potential revision surgery. To assess the effect of KJD, a 3D gait analysis was performed preoperative and one year postoperative. In this patient, preoperative 3D gait analysis revealed an increased knee adduction moment (KAM) compared to healthy levels. Postoperative the KAM decreased, approaching healthy levels, suggesting potential improvements in disease status or in gait. Consequently, further investigation into the effectiveness of Knee Joint Distraction (KJD) as a treatment option for relatively young patients with knee OA is warranted. Gait analysis has emerged as an effective tool for assessing treatment outcomes of innovative treatment such as KJD at the individual level.

Accelerometer-based gait characteristics and their discrimination of gait independence in inpatients with subacute stroke.

BACKGROUND: Gait analysis using inertial measurement devices can identify multifaceted gait disorders after a stroke. Although the usefulness of gait assessment using inertial measurement devices has been reported, its accuracy in discriminating gait independence in patients hospitalized for subacute stroke has not yet been validated. RESEARCH QUESTION: Can trunk acceleration indices discriminate between dependent and independent walking in patients with subacute stroke? METHODS: Thirty-five patients with subacute stroke (mean ± standard deviation, 75.5 ± 9.8 years, 19 males), who were able to understand instructions, had a premorbid modified Rankin scale <3, and were able to walk 16 m straight ahead under supervision were included. The stride regularity, harmonic ratio, and normalized root mean square of trunk accelerations were measured in three directions (mediolateral, vertical, and anterioposterior) during comfortable walking. The Functional Ambulation Categories were used as the dependent variable to classify the patients into two groups (dependent and independent walking groups), and each trunk acceleration index was used as the independent variable to calculate the area under the curve using receiver operating characteristic curves. RESULTS: Twelve patients were in the dependent group and 23 were in the independent group. The normalized root mean square in both the mediolateral and vertical directions were excellent discriminators of walking independence, with an area under the curve greater than 0.8. The cutoff values (sensitivity/specificity) were 2.20 m2/s2 (0.783/0.833) and 2.82 m2/s2 (0.739/0.833), respectively. SIGNIFICANCE: The magnitude of vertical and lateral acceleration during gait in patients with subacute stroke, has excellent accuracy in discriminating between dependent and independent gaits. The results of this study will be useful for inexperienced clinicians working with stroke patients presenting with gait disturbances to accurately determine gait independence based on objective data.

Multi-segment foot kinematics during gait in children with spastic cerebral palsy.

BACKGROUND: Foot deformities (e.g. planovalgus and cavovarus) are very common in children with spastic cerebral palsy (CP), with the midfoot often being involved. Dynamic foot function can be assessed with 3D gait analysis including a multi-segment foot model. Incorporating a midfoot segment in such a model, allows quantification of separate Chopart and Lisfranc joint kinematics. Yet, midfoot kinematics have not previously been reported in CP. RESEARCH QUESTIONS: What is the difference in multi-segment kinematics including midfoot joints between common foot deformities in CP and typically-developing feet? METHODS: 103 feet of 57 children with spastic CP and related conditions were retrospectively included and compared with 15 typically-developing children. All children underwent clinical gait analysis with the Amsterdam Foot Model marker set. Multi-segment foot kinematics were calculated for three strides per foot and averaged. A k-means cluster analysis was performed to identify foot deformity groups that were present within CP data. The deformity type represented by each cluster was based on the foot posture index. Kinematic output of the clusters was compared to typically-developing data for a static standing trial and for the range of motion and kinematic waveforms during walking, using regular and SPM independent t-tests respectively. RESULTS: A neutral, planovalgus and varus cluster were identified. Neutral feet showed mostly similar kinematics as typically-developing data. Planovalgus feet showed increased ankle valgus and Chopart dorsiflexion, eversion and abduction. Varus feet showed increased ankle varus and Chopart inversion and adduction. SIGNIFICANCE: This study is the first to describe Chopart and Lisfranc joint kinematics in different foot deformities of children with CP. It shows that adding a midfoot segment can provide additional clinical and kinematic information. It highlights joint angles that are more distinctive between deformities, which could be helpful to optimize the use of multi-segment foot kinematics in the clinical decision making process.

Three-Dimensional Instrumented Gait Analysis for Children With Cerebral Palsy: An Evidence-Based Clinical Practice Guideline.

BACKGROUND: Children with cerebral palsy (CP) who walk have complex gait patterns and deviations often requiring physical therapy (PT)/medical/surgical interventions. Walking in children with CP can be assessed with 3-dimensional instrumented gait analysis (3D-IGA) providing kinematics (joint angles), kinetics (joint moments/powers), and muscle activity. PURPOSE: This clinical practice guideline provides PTs, physicians, and associated clinicians involved in the care of children with CP, with 7 action statements on when and how 3D-IGA can inform clinical assessments and potential interventions. It links the action statement grades with specific levels of evidence based on a critical appraisal of the literature. CONCLUSIONS: This clinical practice guideline addresses 3D-IGA's utility to inform surgical and non-surgical interventions, to identify gait deviations among segments/joints and planes and to evaluate the effectiveness of interventions. Best practice statements provide guidance for clinicians about the preferred characteristics of 3D-IGA laboratories including instrumentation, staffing, and reporting practices.Video Abstract: Supplemental digital content available at http://links.lww.com/PPT/A524.

Exploration and demonstration of explainable machine learning models in prosthetic rehabilitation-based gait analysis.

Quantitative gait analysis is important for understanding the non-typical walking patterns associated with mobility impairments. Conventional linear statistical methods and machine learning (ML) models are commonly used to assess gait performance and related changes in the gait parameters. Nonetheless, explainable machine learning provides an alternative technique for distinguishing the significant and influential gait changes stemming from a given intervention. The goal of this work was to demonstrate the use of explainable ML models in gait analysis for prosthetic rehabilitation in both population- and sample-based interpretability analyses. Models were developed to classify amputee gait with two types of prosthetic knee joints. Sagittal plane gait patterns of 21 individuals with unilateral transfemoral amputations were video-recorded and 19 spatiotemporal and kinematic gait parameters were extracted and included in the models. Four ML models-logistic regression, support vector machine, random forest, and LightGBM-were assessed and tested for accuracy and precision. The Shapley Additive exPlanations (SHAP) framework was applied to examine global and local interpretability. Random Forest yielded the highest classification accuracy (98.3%). The SHAP framework quantified the level of influence of each gait parameter in the models where knee flexion-related parameters were found the most influential factors in yielding the outcomes of the models. The sample-based explainable ML provided additional insights over the population-based analyses, including an understanding of the effect of the knee type on the walking style of a specific sample, and whether or not it agreed with global interpretations. It was concluded that explainable ML models can be powerful tools for the assessment of gait-related clinical interventions, revealing important parameters that may be overlooked using conventional statistical methods.

Effects on gait kinematics, pedobarography, functional and subjective results after isolated chopart injury.

BACKGROUND: This study analysed changes in gait and pedobarography and subjective and functional outcomes after isolated Chopart joint injury. METHODS: The results of 14 patients were reviewed. Kinematic 3D gait analysis, comparative bilateral electromyography (EMG) and pedobarography were performed. RESULTS: On the injured side, the 3D gait analysis showed a significantly increased internal rotation and decreased external rotation of the hip and significantly decreased adduction and decreased range of motion (ROM) for the ankle. On the healthy side, the pedobarography revealed a significantly increased mean force in the forefoot, an increased peak maximum force and an increased maximum pressure in the metatarsal. When standing, significantly more weight was placed on the healthy side. The EMG measurements showed no significant differences between the healthy and injured legs. CONCLUSIONS: After isolated Chopart injuries, significant changes in gait and pedobarography can be seen over the long term.

Gait differences between COPD and healthy controls: systematic review and meta-analysis.

BACKGROUND: Despite the importance of gait as a determinant of falls, disability and mortality in older people, understanding of gait impairment in COPD is limited. This study aimed to identify differences in gait characteristics during supervised walking tests between people with COPD and healthy controls. METHODS: We searched 11 electronic databases, supplemented by Google Scholar searches and manual collation of references, in November 2019 and updated the search in July 2021. Record screening and information extraction were performed independently by one reviewer and checked for accuracy by a second. Meta-analyses were performed in studies not considered at a high risk of bias. RESULTS: Searches yielded 21 085 unique records, of which 25 were included in the systematic review (including 1015 people with COPD and 2229 healthy controls). Gait speed was assessed in 17 studies (usual speed: 12; fast speed: three; both speeds: two), step length in nine, step duration in seven, cadence in six, and step width in five. Five studies were considered at a high risk of bias. Low-quality evidence indicated that people with COPD walk more slowly than healthy controls at their usual speed (mean difference (MD) -19 cm·s-1, 95% CI -28 to -11 cm·s-1) and at a fast speed (MD -30 cm·s-1, 95% CI -47 to -13 cm·s-1). Alterations in other gait characteristics were not statistically significant. CONCLUSION: Low-quality evidence shows that people with COPD walk more slowly than healthy controls, which could contribute to an increased falls risk. The evidence for alterations in spatial and temporal components of gait was inconclusive. Gait impairment appears to be an important but understudied area in COPD.

Demonstrating the utility of Instrumented Gait Analysis in the treatment of children with cerebral palsy.

BACKGROUND: Instrumented gait analysis (IGA) has been around for a long time but has never been shown to be useful for improving patient outcomes. In this study we demonstrate the potential utility of IGA by showing that machine learning models are better able to estimate treatment outcomes when they include both IGA and clinical (CLI) features compared to when they include CLI features alone. DESIGN: We carried out a retrospective analysis of data from ambulatory children diagnosed with cerebral palsy who were seen at least twice at our gait analysis center. Individuals underwent a variety of treatments (including no treatment) between sequential gait analyses. We fit Bayesian Additive Regression Tree (BART) models that estimated outcomes for mean stance foot progression to demonstrate the approach. We built two models: one using CLI features only, and one using CLI and IGA features. We then compared the models' performance in detail. We performed similar, but less detailed, analyses for a number of other outcomes. All results were based on independent test data from a 70%/30% training/testing split. RESULTS: The IGA model was more accurate than the CLI model for mean stance-phase foot progression outcomes (RMSEIGA = 11∘, RMSECLI = 13∘) and explained more than 1.5 × as much of the variance (R2IGA = .45, R2CLI = .28). The IGA model outperformed the CLI model for every level of treatment complexity, as measured by number of simultaneous surgeries. The IGA model also exhibited superior performance for estimating outcomes of mean stance-phase knee flexion, mean stance-phase ankle dorsiflexion, maximum swing-phase knee flexion, gait deviation index (GDI), and dimensionless speed. INTERPRETATION: The results show that IGA has the potential to be useful in the treatment planning process for ambulatory children diagnosed with cerebral palsy. We propose that the results of machine learning outcome estimators-including estimates of uncertainty-become the primary IGA tool utilized in the clinical process, complementing the standard medical practice of conducting a through patient history and physical exam, eliciting patient goals, reviewing relevant imaging data, and so on.

The impact of anxiety on gait impairments in Parkinson's disease: insights from sensor-based gait analysis.

BACKGROUND: Sensor-based gait analysis provides a robust quantitative tool for assessing gait impairments and their associated factors in Parkinson's disease (PD). Anxiety is observed to interfere with gait clinically, but this has been poorly investigated. Our purpose is to utilize gait analysis to uncover the effect of anxiety on gait in patients with PD. METHODS: We enrolled 38 and 106 PD patients with and without anxiety, respectively. Gait parameters were quantitively examined and compared between two groups both in single-task (ST) and dual-task (DT) walking tests. Multiple linear regression was applied to evaluate whether anxiety independently contributed to gait impairments. RESULTS: During ST, PD patients with anxiety presented significantly shorter stride length, lower gait velocity, longer stride time and stance time, longer stance phase, smaller toe-off (TO) and heel-strike (HS) angles than those without anxiety. While under DT status, the differences were diminished. Multiple linear regression analysis demonstrated that anxiety was an independent factor to a serials of gait parameters, particularly ST-TO (B = -2.599, (-4.82, -0.38)), ST-HS (B = -2.532, (-4.71, -0.35)), ST-TO-CV (B = 4.627, (1.71, 7.64)), ST-HS-CV(B = 4.597, (1.66, 7.53)), ST stance phase (B = 1.4, (0.22, 2.58)), and DT stance phase (B = 1.749, (0.56, 2.94)). CONCLUSION: Our study discovered that anxiety has a significant impact on gait impairments in PD patients, especially exacerbating shuffling steps and prolonging stance phase. These findings highlight the importance of addressing anxiety in PD precision therapy to achieve better treatment outcomes.

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

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

BlazePose-Seq2Seq: Leveraging Regular RGB Cameras for Robust Gait Assessment.

Evaluation of human gait through smartphone-based pose estimation algorithms provides an attractive alternative to costly lab-bound instrumented assessment and offers a paradigm shift with real time gait capture for clinical assessment. Systems based on smart phones, such as OpenPose and BlazePose have demonstrated potential for virtual motion assessment but still lack the accuracy and repeatability standards required for clinical viability. Seq2seq architecture offers an alternative solution to conventional deep learning techniques for predicting joint kinematics during gait. This study introduces a novel enhancement to the low-powered BlazePose algorithm by incorporating a Seq2seq autoencoder deep learning model. To ensure data accuracy and reliability, synchronized motion capture involving an RGB camera and ten Vicon cameras were employed across three distinct self-selected walking speeds. This investigation presents a groundbreaking avenue for remote gait assessment, harnessing the potential of Seq2seq architectures inspired by natural language processing (NLP) to enhance pose estimation accuracy. When comparing BlazePose alone to the combination of BlazePose and 1D convolution Long Short-term Memory Network (1D-LSTM), Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM), the average mean absolute errors decreased from 13.4° to 5.3° for fast gait, from 16.3° to 7.5° for normal gait, and from 15.5° to 7.5° for slow gait at the left ankle joint angle respectively. The strategic utilization of synchronized data and rigorous testing methodologies further bolsters the robustness and credibility of these findings.

Analysis of gait pattern related to high cerebral small vessel disease burden using quantitative gait data from wearable sensors.

BACKGROUND AND OBJECTIVES: Sensor-based wearable devices help to obtain a wide range of quantitative gait parameters, which provides sufficient data to investigate disease-specific gait patterns. Although cerebral small vessel disease (CSVD) plays a significant role in gait impairment, the specific gait pattern associated with a high burden of CSVD remains to be explored. METHODS: We analyzed the gait pattern related to high CSVD burden from 720 participants (aged 55-65 years, 42.5 % male) free of neurological disease in the Taizhou Imaging Study. All participants underwent detailed quantitative gait assessments (obtained from an insole-like wearable gait tracking device) and brain magnetic resonance imaging examinations. Thirty-three gait parameters were summarized into five gait domains. Sparse sliced inverse regression was developed to extract the gait pattern related to high CSVD burden. RESULTS: The specific gait pattern derived from several gait domains (i.e., angles, phases, variability, and spatio-temporal) was significantly associated with the CSVD burden (OR=1.250, 95 % CI: 1.011-1.546). The gait pattern indicates that people with a high CSVD burden were prone to have smaller gait angles, more stance time, more double support time, larger gait variability, and slower gait velocity. Furthermore, people with this gait pattern had a 25 % higher risk of a high CSVD burden. CONCLUSIONS: We established a more stable and disease-specific quantitative gait pattern related to high CSVD burden, which is prone to facilitate the identification of individuals with high CSVD burden among the community residents or the general population.

Motion acquisition of gait characteristics one week after total hip arthroplasty: a factor analysis.

INTRODUCTION: Clinical gait analysis can be used to evaluate the recovery process of patients undergoing total hip arthroplasty (THA). The postoperative walking patterns of these patients can be significantly influenced by the choice of surgical approach, as each procedure alters distinct anatomical structures. The aim of this study is twofold. The first objective is to develop a gait model to describe the change in ambulation one week after THA. The secondary goal is to describe the differences associated with the surgical approach. MATERIALS AND METHODS: Thirty-six patients undergoing THA with lateral (n = 9), anterior (n = 15), and posterior (n = 12) approaches were included in the study. Walking before and 7 days after surgery was recorded using a markerless motion capture system. Exploratory Factor Analysis (EFA), a data reduction technique, condensed 21 spatiotemporal gait parameters to a smaller set of dominant variables. The EFA-derived gait domains were utilized to study post-surgical gait variations and to compare the post-surgical gait among the three groups. RESULTS: Four distinct gait domains were identified. The most pronounced variation one week after surgery is in the Rhythm (gait cycle time: + 32.9 % ), followed by Postural control (step width: + 27.0 % ), Phases (stance time: + 11.0 % ), and Pace (stride length: -  9.3 % ). In postsurgical walking, Phases is statistically significantly different in patients operated with the posterior approach compared to lateral (p-value = 0.017) and anterior (p-value = 0.002) approaches. Furthermore, stance time in the posterior approach group is significantly lower than in healthy individuals (p-value < 0.001). CONCLUSIONS: This study identified a four-component gait model specific to THA patients. The results showed that patients after THA have longer stride time but shorter stride length, wider base of support, and longer stance time, although the posterior group had a statistically significant shorter stance time than the others. The findings of this research have the potential to simplify the reporting of gait outcomes, reduce redundancy, and inform targeted interventions in regards to specific gait domains.