Quantification Of Gait Research Articles

Feasibility and usefulness of video-based markerless two-dimensional automated gait analysis, in providing objective quantification of gait and complementing the evaluation of gait in children with cerebral palsy

BackgroundGait analysis aids in evaluation, classification, and follow-up of gait pattern over time in children with cerebral palsy (CP). The analysis of sagittal plane joint kinematics is of special interest to assess flexed knee gait and ankle joint deviations that commonly progress with age and indicate deterioration of gait. Although most children with CP are ambulatory, no objective quantification of gait is currently included in any of the known international follow-up programs. Is video-based 2-dimensional markerless (2D ML) gait analysis with automated processing a feasible and useful tool to quantify deviations, evaluate and classify gait, in children with CP?MethodsTwenty children with bilateral CP with Gross Motor Function Classification Scale (GMFCS) levels I–III, from five regions in Sweden, were included from the national CP registry. A single RGB-Depth video camera, sensitive to depth and contrast, was positioned laterally to a green walkway and background, with four light sources. A previously validated markerless method was employed to estimate sagittal plane hip, knee, ankle kinematics, foot orientation and spatio-temporal parameters including gait speed and step length.ResultsMean age was 10.4 (range 6.8–16.1) years. Eight children were classified as GMFCS level I, eight as II and four as III. Setup of the measurement system took 15 min, acquisition 5–15 min and processing 50 min per child. Using the 2D ML method kinematic deviations from normal could be determined and used to implement the classification of gait pattern, proposed by Rodda et al. 2001.Conclusion2D ML assessment is feasible, since it is accessible, easy to perform and well tolerated by the children. The 2D ML adds consistency and quantifies objectively important gait variables. It is both relevant and reasonable to include 2D ML gait assessment in the evaluation of children with CP.

A validation study demonstrating portable motion capture cameras accurately characterize gait metrics when compared to a pressure-sensitive walkway

Digital quantification of gait can be used to measure aging- and disease-related decline in mobility. Gait performance also predicts prognosis, disease progression, and response to therapies. Most gait analysis systems require large amounts of space, resources, and expertise to implement and are not widely accessible. Thus, there is a need for a portable system that accurately characterizes gait. Here, depth video from two portable cameras accurately reconstructed gait metrics comparable to those reported by a pressure-sensitive walkway. 392 research participants walked across a four-meter pressure-sensitive walkway while depth video was recorded. Gait speed, cadence, and step and stride durations and lengths strongly correlated (r > 0.9) between modalities, with root-mean-squared-errors (RMSE) of 0.04 m/s, 2.3 steps/min, 0.03 s, and 0.05–0.08 m for speed, cadence, step/stride duration, and step/stride length, respectively. Step, stance, and double support durations (gait cycle percentage) significantly correlated (r > 0.6) between modalities, with 5% RMSE for step and stance and 10% RMSE for double support. In an exploratory analysis, gait speed from both modalities significantly related to healthy, mild, moderate, or severe categorizations of Charleson Comorbidity Indices (ANOVA, Tukey’s HSD, p < 0.0125). These findings demonstrate the viability of using depth video to expand access to quantitative gait assessments.

The efficacy of 3D gait analysis to evaluate surgical (and rehabilitation) outcome after degenerative lumbar surgery

BackgroundLumbar degenerative conditions are a major cause of back pain and disability in individuals aged 45 and above. Gait analysis utilizes sensor technology to collect movement data, aiding in the evaluation of various gait aspects like spatiotemporal parameters, joint angles, neuromuscular activity, and joint forces. It is widely used in conditions such as cerebral palsy and knee osteoarthritis. This research aims to assess the effectiveness of 3D gait analysis in evaluating surgical outcomes and postoperative rehabilitation for lumbar degenerative disorders.MethodsA prospective self-controlled before-after study (n = 85) carried out at our Hospital (Sep 2018 - Dec 2021) utilized a 3D motion analysis system to analyze gait in patients with lumbar degenerative diseases. The study focused on the multifidus muscle, a crucial spinal muscle, during a minimally invasive lumbar interbody fusion surgery conducted by Shandong Weigao Pharmaceutical Co., Ltd. Pre- and postoperative assessments included time-distance parameters (gait speed, stride frequency, stride length, stance phase), hip flexion angle, and stride angle. Changes in 3D gait parameters post-surgery and during rehabilitation were examined. Pearson correlation coefficient was employed to assess relationships with the visual analog pain scale (VAS), Oswestry Disability Index (ODI), and Japanese Orthopedic Association (JOA) scores. Patient sagittal alignment was evaluated using “Surgimap” software from two types of lateral radiographs to obtain parameters like pelvic incidence (PI), pelvic tilt (PT), sacral slope (SS), lumbar lordosis (LL), intervertebral space height (DH), posterior height of the intervertebral space (PDH) at the operative segment, and anterior height of the intervertebral space (ADH).ResultsBy the 6th week post-operation, significant improvements were observed in the VAS score, JOA score, and ODI score of the patients compared to preoperative values (P < 0.05), along with notable enhancements in 3D gait quantification parameters (P < 0.05). Pearson correlation analysis revealed a significant positive correlation between improvements in 3D gait quantification parameters and VAS score, JOA score, and ODI value (all P < 0.001).Conclusion3D gait analysis is a valuable tool for evaluating the efficacy of surgery and rehabilitation training in patients.

Innovative multidimensional gait evaluation using IMU in multiple sclerosis: introducing the semiogram.

Quantifying gait using inertial measurement units has gained increasing interest in recent years. Highly degraded gaits, especially in neurological impaired patients, challenge gait detection algorithms and require specific segmentation and analysis tools. Thus, the outcomes of these devices must be rigorously tested for both robustness and relevancy in order to recommend their routine use. In this study, we propose a multidimensional score to quantify and visualize gait, which can be used in neurological routine follow-up. We assessed the reliability and clinical coherence of this method in a group of severely disabled patients with progressive multiple sclerosis (pMS), who display highly degraded gait patterns, as well as in an age-matched healthy subjects (HS) group. Twenty-two participants with pMS and nineteen HS were included in this 18-month longitudinal follow-up study. During the follow-up period, all participants completed a 10-meter walk test with a U-turn and back, twice at M0, M6, M12, and M18. Average speed and seven clinical criteria (sturdiness, springiness, steadiness, stability, smoothness, synchronization, and symmetry) were evaluated using 17 gait parameters selected from the literature. The variation of these parameters from HS values was combined to generate a multidimensional visual tool, referred to as a semiogram. For both cohorts, all criteria showed moderate to very high test-retest reliability for intra-session measurements. Inter-session quantification was also moderate to highly reliable for all criteria except smoothness, which was not reliable for HS participants. All partial scores, except for the stability score, differed between the two populations. All partial scores were correlated with an objective but not subjective quantification of gait severity in the pMS population. A deficit in the pyramidal tract was associated with altered scores in all criteria, whereas deficits in cerebellar, sensitive, bulbar, and cognitive deficits were associated with decreased scores in only a subset of gait criteria. The proposed multidimensional gait quantification represents an innovative approach to monitoring gait disorders. It provides a reliable and informative biomarker for assessing the severity of gait impairments in individuals with pMS. Additionally, it holds the potential for discriminating between various underlying causes of gait alterations in pMS.

PMotion: an advanced markerless pose estimation approach based on novel deep learning framework used to reveal neurobehavior

Objective. The evaluation of animals’ motion behavior has played a vital role in neuromuscular biomedical research and clinical diagnostics, which reflects the changes caused by neuromodulation or neurodamage. Currently, the existing animal pose estimation methods are unreliable, unpractical, and inaccurate. Approach. Data augmentation (random scaling, random standard deviation Gaussian blur, random contrast, and random uniform color quantization) is adopted to augment image dataset. For the key points recognition, we present a novel efficient convolutional deep learning framework (PMotion), which combines modified ConvNext using multi-kernel feature fusion and self-defined stacked Hourglass block with SiLU activation function. Main results. PMotion is useful to predict the key points of dynamics of unmarked animal body joints in real time with high spatial precision. Gait quantification (step length, step height, and joint angle) was performed for the study of lateral lower limb movements with rats on a treadmill. Significance. The performance accuracy of PMotion on rat joint dataset was improved by 1.98, 1.46, and 0.55 pixels compared with deepposekit, deeplabcut, and stacked hourglass, respectively. This approach also may be applied for neurobehavioral studies of freely moving animals’ behavior in challenging environments (e.g. Drosophila melanogaster and openfield-Pranav) with a high accuracy.

Automated, non-invasive Varroa mite detection by vibrational measurements of gait combined with machine learning

Little is known about mite gait, but it has been suggested that there could be greater variation in locomotory styles for arachnids than insects. The Varroa destructor mite is a devastating ectoparasite of the honeybee. We aim to automatically detect Varroa-specific signals in long-term vibrational recordings of honeybee hives and additionally provide the first quantification and characterisation of Varroa gait through the analysis of its unique vibrational trace. These vibrations are used as part of a novel approach to achieve remote, non-invasive Varroa monitoring in honeybee colonies, requiring discrimination between mite and honeybee signals. We measure the vibrations occurring in samples of freshly collected capped brood-comb, and through combined critical listening and video recordings we build a training database for discrimination and classification purposes. In searching for a suitable vibrational feature, we demonstrate the outstanding value of two-dimensional-Fourier-transforms in invertebrate vibration analysis. Discrimination was less reliable when testing datasets comprising of Varroa within capped brood-cells, where Varroa induced signals are weaker than those produced on the cell surface. We here advance knowledge of Varroa vibration and locomotion, whilst expanding upon the remote detection strategies available for its control.

Augmented Virtual Reality in Vestibular Assessment: A Dynamic Gait Application

Introduction: Dizziness is a common complaint affecting up to 23% of the world population. Diagnosis is of utmost importance and routinely involves several tests to be performed in specialized centers. The advent of a new generation of technical devices would make envision their use for a valid objective vestibular assessment. Microsoft HoloLens 2 (HL2) mixed reality headset has the potential to be a valuable wearable technology that provides interactive digital stimuli and inertial measurement units (IMUs) to objectively quantify the movements of the user in response to various exercises. The aim of this study was to validate the integration of HoloLens with traditional methods used to analyze the vestibular function in order to obtain precise diagnostic values. Methods: Twenty-six healthy adults completed the Dynamic Gait Index tests both with a traditional evaluation and while wearing HL2 headset, thus allowing to collect kinematic data of the patients’ head and eyes. The subjects had to perform 8 different tasks, and the scores were independently assigned by two otolaryngology specialists. Results: The maximum of the mean position of the walking axis of the subjects was found in the second task (−0.14 ± 0.23 m), while the maximum value of the standard deviation of the walking axis was found in the fifth task (−0.12 ± 0.27 m). Overall, positive results were obtained in regard to the validity of the HL2 use to analyze kinematic features. Conclusion: The accurate quantification of gait, movement along the walking axis, and deviation from the normality using HL2 provide an initial evidence for its useful adoption as a valuable tool in gait and mobility assessment.

The impact of instrumented gait analysis on decision-making in the interprofessional management of cerebral palsy: A scoping review.

Management of gait-related problems in children and young people with Cerebral Palsy (CYPwCP) is complex and requires an interprofessional approach. Irrespective of underlying mechanisms, instrumented gait analysis (IGA) can provide quantification of gait to support clinical decision-making for CYPwCP when planning treatment interventions. This scoping review aimed to determine the impact of instrumented gait analysis (IGA) on treatment decision-making for CYPwCP, paying particular attention to interprofessional decision-making. PubMed, EMBASE, Web of Science and Scopus databases were searched from inception to October 2019 for studies including CYPwCP age<25 years. The PRISMA ScR protocol was followed, and Quality was assessed with the Downs and Black (D&B) scale. Influences on decision-making were coded according to the International Classification of Functioning, Disability and Health for Children and Youth framework (ICF-CY). Seventeen studies (1144 patients, 2.8-23 years) of varying quality (mean D&B=17.2, range=11-26) were included. Studies considered IGA influence at three decision-making stages 'Clinical Planning', 'Treatment Performed' and 'Follow up'. Child and Family, and Clinician and Service-centred factors had a high impact on engagement with IGA recommendations. IGA guided recommendations can differ from initial clinical plans, and often lead to modification of the treatment ultimately performed. The effect on individual patients' outcomes when treatment recommendations based on instrumented gait analysis are followed is not yet clear and warrants further research. The differences in clinicians' engagement with IGA recommendations occur due to an array of Child and Family, and Clinician and Service-centred factors. Overall, IGA leads to less surgical recommendations, and has the potential to influence conservative gait-related management in CYPwCP.

Present and future of gait assessment in clinical practice: Towards the application of novel trends and technologies.

Despite being available for more than three decades, quantitative gait analysis remains largely associated with research institutions and not well leveraged in clinical settings. This is mostly due to the high cost/cumbersome equipment and complex protocols and data management/analysis associated with traditional gait labs, as well as the diverse training/experience and preference of clinical teams. Observational gait and qualitative scales continue to be predominantly used in clinics despite evidence of less efficacy of quantifying gait. This study provides a scoping review of the status of clinical gait assessment, including shedding light on common gait pathologies, clinical parameters, indices, and scales. We also highlight novel state-of-the-art gait characterization and analysis approaches and the integration of commercially available wearable tools and technology and AI-driven computational platforms. A comprehensive literature search was conducted within PubMed, Web of Science, Medline, and ScienceDirect for all articles published until December 2021 using a set of keywords, including normal and pathological gait, gait parameters, gait assessment, gait analysis, wearable systems, inertial measurement units, accelerometer, gyroscope, magnetometer, insole sensors, electromyography sensors. Original articles that met the selection criteria were included. Clinical gait analysis remains highly observational and is hence subjective and largely influenced by the observer's background and experience. Quantitative Instrumented gait analysis (IGA) has the capability of providing clinicians with accurate and reliable gait data for diagnosis and monitoring but is limited in clinical applicability mainly due to logistics. Rapidly emerging smart wearable technology, multi-modality, and sensor fusion approaches, as well as AI-driven computational platforms are increasingly commanding greater attention in gait assessment. These tools promise a paradigm shift in the quantification of gait in the clinic and beyond. On the other hand, standardization of clinical protocols and ensuring their feasibility to map the complex features of human gait and represent them meaningfully remain critical challenges.

Gait Analysis with Wearables Is a Potential Progression Marker in Parkinson's Disease.

Gait disturbance is a prototypical feature of Parkinson’s disease (PD), and the quantification of gait using wearable sensors is promising. This study aimed to identify gait impairment in the early and progressive stages of PD according to the Hoehn and Yahr (H–Y) scale. A total of 138 PD patients and 56 healthy controls (HCs) were included in our research. We collected gait parameters using the JiBuEn gait-analysis system. For spatiotemporal gait parameters and kinematic gait parameters, we observed significant differences in stride length (SL), gait velocity, the variability of SL, heel strike angle, and the range of motion (ROM) of the ankle, knee, and hip joints between HCs and PD patients in H–Y Ⅰ-Ⅱ. The changes worsened with the progression of PD. The differences in the asymmetry index of the SL and ROM of the hip were found between HCs and patients in H–Y Ⅳ. Additionally, these gait parameters were significantly associated with Unified Parkinson’s Disease Rating Scale and Parkinson’s Disease Questionnaire-39. This study demonstrated that gait impairment occurs in the early stage of PD and deteriorates with the progression of the disease. The gait parameters mentioned above may help to detect PD earlier and assess the progression of PD.

An Algorithm for Accurate Marker-Based Gait Event Detection in Healthy and Pathological Populations During Complex Motor Tasks.

There is growing interest in the quantification of gait as part of complex motor tasks. This requires gait events (GEs) to be detected under conditions different from straight walking. This study aimed to propose and validate a new marker-based GE detection method, which is also suitable for curvilinear walking and step negotiation. The method was first tested against existing algorithms using data from healthy young adults (YA, n = 20) and then assessed in data from 10 individuals from the following five cohorts: older adults, chronic obstructive pulmonary disease, multiple sclerosis, Parkinson’s disease, and proximal femur fracture. The propagation of the errors associated with GE detection on the calculation of stride length, duration, speed, and stance/swing durations was investigated. All participants performed a variety of motor tasks including curvilinear walking and step negotiation, while reference GEs were identified using a validated methodology exploiting pressure insole signals. Sensitivity, positive predictive values (PPV), F1-score, bias, precision, and accuracy were calculated. Absolute agreement [intraclass correlation coefficient ()] between marker-based and pressure insole stride parameters was also tested. In the YA cohort, the proposed method outperformed the existing ones, with sensitivity, PPV, and F1 scores ≥ 99% for both GEs and conditions, with a virtually null bias (<10 ms). Overall, temporal inaccuracies minimally impacted stride duration, length, and speed (median absolute errors ≤1%). Similar algorithm performances were obtained for all the other five cohorts in GE detection and propagation to the stride parameters, where an excellent absolute agreement with the pressure insoles was also found (). In conclusion, the proposed method accurately detects GE from marker data under different walking conditions and for a variety of gait impairments.

Markerless Gait Classification Employing 3D IR-UWB Physiological Motion Sensing

Human gait refers to the propulsion achieved by the effort of human limbs, a reflex progression resulting from the rhythmic reciprocal bursts of flexor and extensor activity. Several quantitative models are followed by health professionals to diagnose gait abnormality. Marker-based gait quantification is considered a gold standard by the research and health communities. It reconstructs motion in 3D and provides parameters to measure gait. But, it is an expensive and intrusive technique, limited to soft tissue artefact, prone to incorrect marker positioning, and skin sensitivity problems. Hence, markerless, swiftly deployable, non-intrusive, camera-less prototypes would be a game changing possibility, and an example is proposed here. This paper illustrates a 3D gait motion analyser employing impulse radio ultra-wide band (IR-UWB) wireless technology. The prototype can measure 3D motion and determine quantitative parameters considering anatomical reference planes. Knee angles have been calculated from the gait by applying vector algebra. Simultaneously, the model has been corroborated with the popular markerless camera based 3D motion capturing system, the Kinect sensor. Bland and Altman (B&A) statistics has been applied to the proposed prototype and Kinect sensor results to verify the measurement agreement. Finally, the proposed prototype has been incorporated with popular supervised machine learning such as, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -nearest neighbour ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$kNN$ </tex-math></inline-formula> ), support vector machine (SVM) and the deep learning technique deep neural multilayer perceptron (DMLP) network to automatically recognize gait abnormalities, with promising results presented.

The Microsoft HoloLens 2 Provides Accurate Measures of Gait, Turning, and Functional Mobility in Healthy Adults.

Augmented-reality (AR) headsets, such as the Microsoft HoloLens 2 (HL2), have the potential to be the next generation of wearable technology as they provide interactive digital stimuli in the context of ecologically-valid daily activities while containing inertial measurement units (IMUs) to objectively quantify the movements of the user. A necessary precursor to the widespread utilization of the HL2 in the fields of movement science and rehabilitation is the rigorous validation of its capacity to generate biomechanical outcomes comparable to gold standard outcomes. This project sought to determine equivalency of kinematic outcomes characterizing lower-extremity function derived from the HL2 and three-dimensional (3D) motion capture systems (MoCap). Sixty-six healthy adults completed two lower-extremity tasks while kinematic data were collected from the HL2 and MoCap: (1) continuous walking and (2) timed up-and-go (TUG). For all the continuous walking metrics (cumulative distance, time, number of steps, step and stride length, and velocity), equivalence testing indicated that the HL2 and MoCap were statistically equivalent (error ≤ 5%). The TUG metrics, including turn duration and turn velocity, were also statistically equivalent between the two systems. The accurate quantification of gait and turning using a wearable such as the HL2 provides initial evidence for its use as a platform for the development and delivery of gait and mobility assessments, including the in-person and remote delivery of highly salient digital movement assessments and rehabilitation protocols.

Extraction of gait parameters from marker-free video recordings of Timed Up-and-Go tests: Validity, inter- and intra-rater reliability.

We study dual-task performance with marker-free video recordings of Timed Up-and-Go tests (TUG) and TUG combined with a cognitive/verbal task (TUG dual-task, TUGdt). Can gait parameters be accurately estimated from video-recorded TUG tests by a new semi-automatic method aided by a technique for human 2D pose estimation based on deep learning? Thirty persons aged 60-85 years participated in the study, conducted in a laboratory environment. Data were collected by two synchronous video-cameras and a marker-based optoelectronic motion capture system as gold standard, to evaluate the gait parameters step length (SL), step width (SW), step duration (SD), single-stance duration (SSD) and double-stance duration (DSD). For reliability evaluations, data processing aided by a deep neural network model, involved three raters who conducted three repetitions of identifying anatomical keypoints in recordings of one randomly selected step from each of the participants. Validity was analysed using 95 % confidence intervals (CI) and p-values for method differences and Bland-Altman plots with limits of agreement. Inter- and intra-rater reliability were calculated as intraclass correlation coefficients (ICC) and standard errors of measurement. Smallest detectable change was calculated for inter-rater reliability. Mean ddifferences between video and the motion capture system data for SW, DSD, and SSD were significant (p < 0.001). However, mean differences for all parameters were small (-6.4%-13.0% of motion capture system) indicating good validity. Concerning reliability, almost all 95 % CI of the ICC estimates exceeded 0.90, indicating excellent reliability. Only inter-rater reliability for SW (95 % CI = 0.892;0.973) and one rater's intra-rater reliability for SSD (95 % CI = 0.793;0.951) were lower, but still showed good to excellent reliability. The presented method for extraction of gait parameters from video appears suitable for valid and reliable quantification of gait. This opens up for analyses that may contribute to the knowledge of cognitive-motor interference in dual-task testing.

Quantification of Gait During a Standardized IADL Task

Abstract Date Presented 04/200/21 Current methods to assess functional performance often rely on subjective reporting and are commonly not generalizable. A cross-sectional comparison of standard straight-line gait under a cognitive load and a functional task that included cognitive and visual components was performed using instrumented gait mats. Preliminary results suggest traditional linear dual-task walking may not accurately depict functional gait, highlighting the need for ecologically valid assessments of function. Primary Author and Speaker: Maxwell Toepfer Additional Authors and Speakers: Natalie Robek, Megan Dougherty, and Anna Murray Contributing Authors: Lindy Weaver, Amy Darragh

Assessment Methods of Post-stroke Gait: A Scoping Review of Technology-Driven Approaches to Gait Characterization and Analysis.

Background: Gait dysfunction or impairment is considered one of the most common and devastating physiological consequences of stroke, and achieving optimal gait is a key goal for stroke victims with gait disability along with their clinical teams. Many researchers have explored post stroke gait, including assessment tools and techniques, key gait parameters and significance on functional recovery, as well as data mining, modeling and analyses methods.Research Question: This study aimed to review and summarize research efforts applicable to quantification and analyses of post-stroke gait with focus on recent technology-driven gait characterization and analysis approaches, including the integration of smart low cost wearables and Artificial Intelligence (AI), as well as feasibility and potential value in clinical settings.Methods: A comprehensive literature search was conducted within Google Scholar, PubMed, and ScienceDirect using a set of keywords, including lower extremity, walking, post-stroke, and kinematics. Original articles that met the selection criteria were included.Results and Significance: This scoping review aimed to shed light on tools and technologies employed in post stroke gait assessment toward bridging the existing gap between the research and clinical communities. Conventional qualitative gait analysis, typically used in clinics is mainly based on observational gait and is hence subjective and largely impacted by the observer's experience. Quantitative gait analysis, however, provides measured parameters, with good accuracy and repeatability for the diagnosis and comparative assessment throughout rehabilitation. Rapidly emerging smart wearable technology and AI, including Machine Learning, Support Vector Machine, and Neural Network approaches, are increasingly commanding greater attention in gait research. Although their use in clinical settings are not yet well leveraged, these tools promise a paradigm shift in stroke gait quantification, as they provide means for acquiring, storing and analyzing multifactorial complex gait data, while capturing its non-linear dynamic variability and offering the invaluable benefits of predictive analytics.

Implementation of Machine Learning Classification Regarding Hemiplegic Gait Using an Assortment of Machine Learning Algorithms with Quantification from Conformal Wearable and Wireless Inertial Sensor System

The quantification of gait is uniquely facilitated through the conformal wearable and wireless inertial sensor system, which consists of a profile comparable to a bandage. These attributes advance the ability to quantify hemiplegic gait in consideration of the hemiplegic affected leg and unaffected leg. The recorded inertial sensor data, which is inclusive of the gyroscope signal, can be readily transmitted by wireless means to a secure Cloud. Incorporating Python to automate the post-processing of the gyroscope signal data can enable the development of a feature set suitable for a machine learning platform, such as the Waikato Environment for Knowledge Analysis (WEKA). An assortment of machine learning algorithms, such as the multilayer perceptron neural network, J48 decision tree, random forest, K-nearest neighbors, logistic regression, and na&#239ve Bayes, were evaluated in terms of classification accuracy and time to develop the machine learning model. The K-nearest neighbors achieved optimal performance based on classification accuracy achieved for differentiating between the hemiplegic affected leg and unaffected leg for gait and the time to establish the machine learning model. The achievements of this research endeavor demonstrate the utility of amalgamating the conformal wearable and wireless inertial sensor with machine learning algorithms for distinguishing the hemiplegic affected leg and unaffected leg during gait.

Gait quantification and visualization for digital healthcare

Gait abnormalities are common in clinical practice and there is a global imperative to improve technologies that facilitate their detection, evaluation, monitoring and management. Real time evaluation using digital technology supports the development of digital healthcare. Currently gait assessment relies on visual observation of structured clinical tests such as the “Timed Get up and Go Test.” Gold standard methods such as “Qualisys Motion Capture System” require sophisticated equipment in gait laboratories. These are not widely available due to expense, analysis time and requirement of trained technicians. Developing low cost, portable, easy to use digital technology is important to enable sophisticated assessment of gait at home or in clinics. Common measures for quantification of gait include symmetry angle, ratio and index. These measurements may be difficult to interpret by users as stand-alone values. To facilitate the evaluation and interpretation of locomotive information, a tool to visualize gait in real-time is proposed. The proposed tool consists of five approaches (1: Real-time dial visualization, 2: Visualization of individual leg time variation, 3: Visualization of both legs asymmetry, 4: Boxplot visualization, and 5: Evaluation considering all features). Results show that wearable Inertial Measurement Unit (IMU) can be used for extraction of objective gait features. This system opens possibilities for home-based assessment of gait without the requirement and expense of an elaborate laboratory setup and supports the development of digital healthcare.

Wearable inertial sensors provide reliable biomarkers of disease severity in multiple sclerosis: A systematic review and meta-analysis.

Gait impairment is a hallmark of multiple sclerosis (MS). InertiaLocoGraphy, the quantification of gait with inertial measurement units (IMUs), has been found useful to detect early changes in gait in MS. Still, the potential use of IMUs as a reliable biomarker of disease severity in MS remains unknown. This systematic review and meta-analysis of observational studies aimed to describe IMU protocols used to assess gait in MS patients and calculate the effect sizes of IMU features associated with disease severity scale measures. We searched MEDLINE, Cochrane Central, EMBASE and grey literature to identify articles published before May 2, 2018 that measured gait in MS patients by using IMUs and correlated IMU parameters with disease severity scale measures. We excluded from the meta-analysis articles that did not provide enough data to evaluate the association between IMU parameters and disease severity scale measures. The study was registered with the International Prospective Register of Systematic Reviews on May 2, 2018 (Registration: CRD42018092651) and the protocol was published in Systematic Reviews on January 8, 2019. We included 36 articles in the systematic review and pooled 12 for the meta-analysis. The risk of bias was moderate, with only 2 articles (none included in the meta-analysis) showing a bias score<50%. Among protocols tested, 2 were predominant (the Timed Up and Go test and 6-min walk test). Speed, step length and step time with IMUs were significantly correlated with the Expanded Disability Status Scale (EDSS) score, and speed and step length were significantly correlated with the Multiple Sclerosis Walking Scale-12 score. IMU measurement has the potential to increase the sensitivity of clinical and performance tests to identify evolution in gait alteration in MS. Kinematic parameters easily accessible with IMUs, such as speed, step length and step duration, can help follow up disease severity in MS individuals with low to medium EDSS score (1.0-4.5).

Paw-Print Analysis of Contrast-Enhanced Recordings (PrAnCER): A Low-Cost, Open-Access Automated Gait Analysis System for Assessing Motor Deficits.

Gait analysis is used to quantify changes in motor function in many rodent models of disease. Despite the importance of assessing gait and motor function in many areas of research, the available commercial options have several limitations such as high cost and lack of accessible, open code. To address these issues, we developed PrAnCER, Paw-Print Analysis of Contrast-Enhanced Recordings, for automated quantification of gait. The contrast-enhanced recordings are produced by using a translucent floor that obscures objects not in contact with the surface, effectively isolating the rat's paw prints as it walks. Using these videos, our simple software program reliably measures a variety of spatiotemporal gait parameters. To demonstrate that PrAnCER can accurately detect changes in motor function, we employed a haloperidol model of Parkinson's disease (PD). We tested rats at two doses of haloperidol: high dose (0.30 mg/kg) and low dose (0.15 mg/kg). Haloperidol significantly increased stance duration and hind paw contact area in the low dose condition, as might be expected in a PD model. In the high dose condition, we found a similar increase in contact area but also an unexpected increase in stride length. With further research, we found that this increased stride length is consistent with the bracing-escape phenomenon commonly observed at higher doses of haloperidol. Thus, PrAnCER was able to detect both expected and unexpected changes in rodent gait patterns. Additionally, we confirmed that PrAnCER is consistent and accurate when compared with manual scoring of gait parameters.