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

Meta-analysis of the quantitative assessment of lower extremity motor function in elderly individuals based on objective detection

OBJECTIVE

To avoid deviation caused by the traditional scale method, the present study explored the accuracy, advantages, and disadvantages of different objective detection methods in evaluating lower extremity motor function in elderly individuals.

METHODS

Studies on lower extremity motor function assessment in elderly individuals published in the PubMed, Web of Science, Cochrane Library and EMBASE databases in the past five years were searched. The methodological quality of the included trials was assessed using RevMan 5.4.1 and Stata, followed by statistical analyses.

RESULTS

In total, 19 randomized controlled trials with a total of 2626 participants, were included. The results of the meta-analysis showed that inertial measurement units (IMUs), motion sensors, 3D motion capture systems, and observational gait analysis had statistical significance in evaluating the changes in step velocity and step length of lower extremity movement in elderly individuals (P < 0.00001), which can be used as a standardized basis for the assessment of motor function in elderly individuals. Subgroup analysis showed that there was significant heterogeneity in the assessment of step velocity [SMD=-0.98, 95%CI(-1.23, -0.72), I2 = 91.3%, P < 0.00001] and step length [SMD=-1.40, 95%CI(-1.77, -1.02), I2 = 86.4%, P < 0.00001] in elderly individuals. However, the sensors (I2 = 9%, I2 = 0%) and 3D motion capture systems (I2 = 0%) showed low heterogeneity in terms of step velocity and step length. The sensitivity analysis and publication bias test demonstrated that the results were stable and reliable.

CONCLUSION

observational gait analysis, motion sensors, 3D motion capture systems, and IMUs, as evaluation means, play a certain role in evaluating the characteristic parameters of step velocity and step length in lower extremity motor function of elderly individuals, which has good accuracy and clinical value in preventing motor injury. However, the high heterogeneity of observational gait analysis and IMUs suggested that different evaluation methods use different calculation formulas and indicators, resulting in the failure to obtain standardized indicators in clinical applications. Thus, multimodal quantitative evaluation should be integrated.

Position paper on how technology for human motion analysis and relevant clinical applications have evolved over the past decades: Striking a balance between accuracy and convenience

BACKGROUND

Over the past decades, tremendous technological advances have emerged in human motion analysis (HMA).

RESEARCH QUESTION

How has technology for analysing human motion evolved over the past decades, and what clinical applications has it enabled?

METHODS

The literature on HMA has been extensively reviewed, focusing on three main approaches: Fully-Instrumented Gait Analysis (FGA), Wearable Sensor Analysis (WSA), and Deep-Learning Video Analysis (DVA), considering both technical and clinical aspects.

RESULTS

FGA techniques relying on data collected using stereophotogrammetric systems, force plates, and electromyographic sensors have been dramatically improved providing highly accurate estimates of the biomechanics of motion. WSA techniques have been developed with the advances in data collection at home and in community settings. DVA techniques have emerged through artificial intelligence, which has marked the last decade. Some authors have considered WSA and DVA techniques as alternatives to "traditional" HMA techniques. They have suggested that WSA and DVA techniques are destined to replace FGA.

SIGNIFICANCE

We argue that FGA, WSA, and DVA complement each other and hence should be accounted as "synergistic" in the context of modern HMA and its clinical applications. We point out that DVA techniques are especially attractive as screening techniques, WSA methods enable data collection in the home and community for extensive periods of time, and FGA does maintain superior accuracy and should be the preferred technique when a complete and highly accurate biomechanical data is required. Accordingly, we envision that future clinical applications of HMA would favour screening patients using DVA in the outpatient setting. If deemed clinically appropriate, then WSA would be used to collect data in the home and community to derive relevant information. If accurate kinetic data is needed, then patients should be referred to specialized centres where an FGA system is available, together with medical imaging and thorough clinical assessments.

AI in evaluating ambulation of stroke patients: severity classification with video and functional ambulation category scale

BACKGROUND

The evaluation of gait function and severity classification of stroke patients are important to determine the rehabilitation goal and the level of exercise. Physicians often qualitatively evaluate patients' walking ability through visual gait analysis using naked eye, video images, or standardized assessment tools. Gait evaluation through observation relies on the doctor's empirical judgment, potentially introducing subjective opinions. Therefore, conducting research to establish a basis for more objective judgment is crucial.

OBJECTIVE

To verify a deep learning model that classifies gait image data of stroke patients according to Functional Ambulation Category (FAC) scale.

METHODS

Gait vision data from 203 stroke patients and 182 healthy individuals recruited from six medical institutions were collected to train a deep learning model for classifying gait severity in stroke patients. The recorded videos were processed using OpenPose. The dataset was randomly split into 80% for training and 20% for testing.

RESULTS

The deep learning model attained a training accuracy of 0.981 and test accuracy of 0.903. Area Under the Curve(AUC) values of 0.93, 0.95, and 0.96 for discriminating among the mild, moderate, and severe stroke groups, respectively.

CONCLUSION

This confirms the potential of utilizing human posture estimation based on vision data not only to develop gait parameter models but also to develop models to classify severity according to the FAC criteria used by physicians. To develop an AI-based severity classification model, a large amount and variety of data is necessary and data collected in non-standardized real environments, not in laboratories, can also be used meaningfully.

Application of the participatory design in the testing of a baropodometric insole prototype for weight-bearing asymmetry after a stroke: A qualitative study

Introduction

Currently studies indicate the need to incorporate the user`s perspective in the testing of new assistive technologies. The objective of this paper is to test a baropodometric insole prototype for monitoring and treatment weight-bearing asymmetry, according to the Participatory Design.

Methods

We used a qualitative case study approach during the testing phase of the baropodometric insole prototype. The focus group approach addressed topics related to the experience and accessibility of the potential user in conjunction with professionals, researchers, and physiotherapy students. Facilitators, barriers, and requirements for the device were collected through audio recordings of the discussions during and after prototype testing.

Results

Key steps in the prototype testing process were divided into (1) Test of the prototype according to the Participatory Design, divided into Who, When, How, and Why the potential user was involved in the study; and (2) Facilitators, barriers and requirements to improve the prototype.

Conclusions

The baropodometric insole prototype can be seen as a promising device for monitoring and treating weight-bearing asymmetry.

Can We Target Close Therapeutic Goals in the Gait Re-Education Algorithm for Stroke Patients at the Beginning of the Rehabilitation Process?

(1) Background: The study aimed to determine the most important activities of the knee joints related to gait re-education in patients in the subacute period after a stroke. We focused on the tests that a physiotherapist could perform in daily clinical practice. (2) Methods: Twenty-nine stroke patients (SG) and 29 healthy volunteers (CG) were included in the study. The patients underwent the 5-meter walk test (5mWT) and the Timed Up and Go test (TUG). Tests such as step up, step down, squat, step forward, and joint position sense test (JPS) were also performed, and the subjects were assessed using wireless motion sensors. (3) Results: We observed significant differences in the time needed to complete the 5mWT and TUG tests between groups. The results obtained in the JPS show a significant difference between the paretic and the non-paretic limbs compared to the CG group. A significantly smaller range of knee joint flexion (ROM) was observed in the paretic limb compared to the non-paretic and control limbs in the step down test and between the paretic and non-paretic limbs in the step forward test. (4) Conclusions: The described functional tests are useful in assessing a stroke patient's motor skills and can be performed in daily clinical practice.

Gait Impairment Analysis Using Silhouette Sinogram Signals and Assisted Knowledge Learning

The analysis of body motion is a valuable tool in the assessment and diagnosis of gait impairments, particularly those related to neurological disorders. In this study, we propose a novel automated system leveraging artificial intelligence for efficiently analyzing gait impairment from video-recorded images. The proposed methodology encompasses three key aspects. First, we generate a novel one-dimensional representation of each silhouette image, termed a silhouette sinogram, by computing the distance and angle between the centroid and each detected boundary points. This process enables us to effectively utilize relative variations in motion at different angles to detect gait patterns. Second, a one-dimensional convolutional neural network (1D CNN) model is developed and trained by incorporating the consecutive silhouette sinogram signals of silhouette frames to capture spatiotemporal information via assisted knowledge learning. This process allows the network to capture a broader context and temporal dependencies within the gait cycle, enabling a more accurate diagnosis of gait abnormalities. This study conducts training and an evaluation utilizing the publicly accessible INIT GAIT database. Finally, two evaluation schemes are employed: one leveraging individual silhouette frames and the other operating at the subject level, utilizing a majority voting technique. The outcomes of the proposed method showed superior enhancements in gait impairment recognition, with overall F1-scores of 100%, 90.62%, and 77.32% when evaluated based on sinogram signals, and 100%, 100%, and 83.33% when evaluated based on the subject level, for cases involving two, four, and six gait abnormalities, respectively. In conclusion, by comparing the observed locomotor function to a conventional gait pattern often seen in healthy individuals, the recommended approach allows for a quantitative and non-invasive evaluation of locomotion.

Dual Tasking Affects the Outcomes of Instrumented Timed up and Go, Sit-to-Stand, Balance, and 10-Meter Walk Tests in Stroke Survivors

Stroke can impair mobility, with deficits more pronounced while simultaneously performing multiple activities. In this study, common clinical tests were instrumented with wearable motion sensors to study motor-cognitive interference effects in stroke survivors (SS). A total of 21 SS and 20 healthy controls performed the Timed Up and Go (TUG), Sit-to-Stand (STS), balance, and 10-Meter Walk (10MWT) tests under single and dual-task (counting backward) conditions. Calculated measures included total time and gait measures for TUG, STS, and 10MWT. Balance tests for both open and closed eyes conditions were assessed using sway, measured using the linear acceleration of the thorax, pelvis, and thighs. SS exhibited poorer performance with slower TUG (16.15 s vs. 13.34 s, single-task p < 0.001), greater sway in the eyes open balance test (0.1 m/s2 vs. 0.08 m/s2, p = 0.035), and slower 10MWT (12.94 s vs. 10.98 s p = 0.01) compared to the controls. Dual tasking increased the TUG time (~14%, p < 0.001), balance thorax sway (~64%, p < 0.001), and 10MWT time (~17%, p < 0.001) in the SS group. Interaction effects were minimal, suggesting similar dual-task costs. The findings demonstrate exaggerated mobility deficits in SS during dual-task clinical testing. Dual-task assessments may be more effective in revealing impairments. Integrating cognitive challenges into evaluation can optimize the identification of fall risks and personalize interventions targeting identified cognitive-motor limitations post stroke.

The Artificial Intelligence Revolution in Stroke Care: A Decade of Scientific Evidence in Review

BACKGROUND

The emergence of artificial intelligence (AI) has significantly influenced the diagnostic evaluation of stroke and has revolutionized acute stroke care delivery. The scientific evidence evaluating the role of AI, especially in areas of stroke treatment and rehabilitation is limited but continues to accumulate. We performed a systemic review of current scientific evidence evaluating the use of AI in stroke evaluation and care and examined the publication trends during the past decade.

METHODS

A systematic search of electronic databases was conducted to identify all studies published from 2012 to 2022 that incorporated AI in any aspect of stroke care. Studies not directly relevant to stroke care in the context of AI and duplicate studies were excluded. The level of evidence and publication trends were examined.

RESULTS

A total of 623 studies were examined, including 101 reviews (16.2%), 9 meta-analyses (1.4%), 140 original articles on AI methodology (22.5%), 2 case reports (0.3%), 2 case series (0.3%), 31 case-control studies (5%), 277 cohort studies (44.5%), 16 cross-sectional studies (2.6%), and 45 experimental studies (7.2%). The highest published area of AI in stroke was diagnosis (44.1%) and the lowest was rehabilitation (12%). A 10-year trend analysis revealed a significant increase in AI literature in stroke care.

CONCLUSIONS

Most research on AI is in the diagnostic area of stroke care, with a recent noteworthy trend of increased research focus on stroke treatment and rehabilitation.

Systematic review of automatic post-stroke gait classification systems

BACKGROUND

Gait classification is a clinically helpful task performed after a stroke in order to guide rehabilitation therapy. Gait disorders are commonly identified using observational gait analysis in clinical settings, but this approach is limited due to low reliability and accuracy. Data-driven gait classification can quantify gait deviations and categorise gait patterns automatically possibly improving reliability and accuracy; however, the development and clinical utility of current data driven systems has not been reviewed previously.

RESEARCH QUESTION

The purpose of this systematic review is to evaluate the literature surrounding the methodology used to develop automatic gait classification systems, and their potential effectiveness in the clinical management of stroke-affected gait.

METHOD

The database search included PubMed, IEEE Xplore, and Scopus. Twenty-one studies were identified through inclusion and exclusion criteria from 407 available studies published between 2015 and 2022. Development methodology, classification performance, and clinical utility information were extracted for review.

RESULTS AND SIGNIFICANCE

Most of gait classification systems reported a classification accuracy between 80%-100%. However, collated studies presented methodological errors in machine learning (ML) model development. Further, many studies neglected model components such as clinical utility (e.g., predictions don't assist clinicians or therapists in making decisions, interpretability, and generalisability). We provided recommendations to guide development of future post-stroke automatic gait classification systems to better assist clinicians and therapists. Future automatic gait classification systems should emphasise the clinical significance and adopt a standardised development methodology of ML model.

Rehabilitative effects of electrical stimulation on gait performance in stroke patients: A systematic review with meta-analysis

BACKGROUND

Electrical stimulation techniques are widely utilized for rehabilitation management in individuals with stroke patients.

OBJECTIVES

This review aims to summarize the rehabilitative effects of electrical stimulation therapy on gait performance in stroke patients.

METHODS

This review included randomized controlled trials (RCT) investigating the therapeutic effects of electrical stimulation in stroke patients throughout five databases. This review qualitatively synthesized 20 studies and quantitatively analyzed 11 RCTs.

RESULTS

Functional electrical stimulation (FES) was the most commonly used electrical stimulation type to improve postural stability and gait performance in stroke patients. The clinical measurement tools commonly used in the three studies to assess the therapeutic effects of FES were Berg balance scale (BBS), 10-meter walk test (10MWT), 6-minute walk test (6mWT), and gait velocity. The BBS score and gait velocity had positive effects in the FES group compared with the control group, but the 10MWT and 6mWT showed the same effects between the two groups. The heterogeneity of BBS scores was also high.

CONCLUSION

The results of this review suggest that electrical stimulation shows little evidence of postural stability and gait performance in stroke patients, although some electrical stimulations showed positive effects on postural stability and gait performance.

Validation of an algorithm to assess regular and irregular gait using inertial sensors in healthy and stroke individuals

Background

Studies using inertial measurement units (IMUs) for gait assessment have shown promising results regarding accuracy of gait event detection and spatiotemporal parameters. However, performance of such algorithms is challenged in irregular walking patterns, such as in individuals with gait deficits. Based on the literature, we developed an algorithm to detect initial contact (IC) and terminal contact (TC) and calculate spatiotemporal gait parameters. We evaluated the validity of this algorithm for regular and irregular gait patterns against a 3D optical motion capture system (OMCS).

Methods

Twenty healthy participants (aged 59 ± 12 years) and 10 people in the chronic phase after stroke (aged 61 ± 11 years) were equipped with 4 IMUs: on both feet, sternum and lower back (MTw Awinda, Xsens) and 26 reflective makers. Participants walked on an instrumented treadmill for 2 minutes (i) with their preferred stride lengths and (ii) once with irregular stride lengths (±20% deviation) induced by light projected stepping stones. Accuracy of the algorithm was evaluated on stride-by-stride agreement of IC, TC, stride time, length and velocity with OMCS. Bland-Altman-like plots were made for the spatiotemporal parameters, while differences in detection of IC and TC time instances were shown in histogram plots. Performance of the algorithm was compared between regular and irregular gait with a linear mixed model. This was done by comparing the performance in healthy participants in the regular vs irregular walking condition, and by comparing the agreement in healthy participants with stroke participants in the regular walking condition.

Results

For each condition at least 1,500 strides were included for analysis. Compared to OMCS, IMU-based IC detection in both groups and condition was on average 9-17 (SD ranging from 7 to 35) ms, while IMU-based TC was on average 15-24 (SD ranging from 12 to 35) ms earlier. When comparing regular and irregular gait in healthy participants, the difference between methods was 2.5 ms higher for IC, 3.4 ms lower for TC, 0.3 cm lower for stride length, and 0.4 cm/s higher for stride velocity in the irregular walking condition. No difference was found on stride time. When comparing the differences between methods between healthy and stroke participants, the difference between methods was 7.6 ms lower for IC, 3.8 cm lower for stride length, and 3.4 cm/s lower for stride velocity in stroke participants. No differences were found on differences between methods on TC detection and stride time between stroke and healthy participants.

Conclusions

Small irrelevant differences were found on gait event detection and spatiotemporal parameters due to irregular walking by imposing irregular stride lengths or pathological (stroke) gait. Furthermore, IMUs seem equally good compared to OMCS to assess gait variability based on stride time, but less accurate based on stride length.

Single-Belt Versus Split-Belt: Intelligent Treadmill Control via Microphase Gait Capture for Poststroke Rehabilitation

Stroke is the leading long-term disability and causes a significant financial burden associated with rehabilitation. In poststroke rehabilitation, individuals with hemiparesis have a specialized demand for coordinated movement between the paretic and the nonparetic legs. The split-belt treadmill can effectively facilitate the paretic leg by slowing down the belt speed for that leg while the patient is walking on a split-belt treadmill. Although studies have found that split-belt treadmills can produce better gait recovery outcomes than traditional single-belt treadmills, the high cost of split-belt treadmills is a significant barrier to stroke rehabilitation in clinics. In this article, we design an AI-based system for the single-belt treadmill to make it act like a split-belt by adjusting the belt speed instantaneously according to the patient's microgait phases. This system only requires a low-cost RGB camera to capture human gait patterns. A novel microgait classification pipeline model is used to detect gait phases in real time. The pipeline is based on self-supervised learning that can calibrate the anchor video with the real-time video. We then use a ResNet-LSTM module to handle temporal information and increase accuracy. A real-time filtering algorithm is used to smoothen the treadmill control. We have tested the developed system with 34 healthy individuals and four stroke patients. The results show that our system is able to detect the gait microphase accurately and requires less human annotation in training, compared to the ResNet50 classifier. Our system "Splicer" is boosted by AI modules and performs comparably as a split-belt system, in terms of timely varying left/right foot speed, creating a hemiparetic gait in healthy individuals, and promoting paretic side symmetry in force exertion for stroke patients. This innovative design can potentially provide cost-effective rehabilitation treatment for hemiparetic patients.

Minimal detectable change in inertial measurement unit-based trunk acceleration indices during gait in inpatients with subacute stroke

Gait analysis using inertial measurement units (IMU) provides a multifaceted assessment of gait characteristics, but minimal detectable changes (MDC), the true change beyond measurement error, during gait in patients hospitalized with subacute stroke has not been clarified. This study aimed to determine the MDC in IMU-based trunk acceleration indices during gait in patients hospitalized with subacute stroke. Nineteen patients with subacute stroke (mean ± SD, 75.4 ± 10.9 years; 13 males) who could understand instructions, had a pre-morbid modified Rankin Scale < 3 and could walk straight for 16 m under supervision were included. As trunk acceleration indices, Stride regularity, harmonic ratio (HR), and normalized root mean square (RMS) during gait were calculated on three axes: mediolateral (ML), vertical (VT), and anterior-posterior (AP). MDC was calculated from two measurements taken on the same day according to the following formula: MDC = standard error of measurement × 1.96 × 2. The MDCs for each trunk acceleration index were, in order of ML, VT, and AP: 0.175, 0.179, and 0.149 for stride regularity; 0.666, 0.741, and 0.864 for HR; 4.511, 2.288, and 2.680 for normalized RMS. This finding helps determine the effectiveness of rehabilitation interventions in the gait assessment of patients with stroke.

Ecological validity of a deep learning algorithm to detect gait events from real-life walking bouts in mobility-limiting diseases

Introduction

The clinical assessment of mobility, and walking specifically, is still mainly based on functional tests that lack ecological validity. Thanks to inertial measurement units (IMUs), gait analysis is shifting to unsupervised monitoring in naturalistic and unconstrained settings. However, the extraction of clinically relevant gait parameters from IMU data often depends on heuristics-based algorithms that rely on empirically determined thresholds. These were mainly validated on small cohorts in supervised settings.

Methods

Here, a deep learning (DL) algorithm was developed and validated for gait event detection in a heterogeneous population of different mobility-limiting disease cohorts and a cohort of healthy adults. Participants wore pressure insoles and IMUs on both feet for 2.5 h in their habitual environment. The raw accelerometer and gyroscope data from both feet were used as input to a deep convolutional neural network, while reference timings for gait events were based on the combined IMU and pressure insoles data.

Results and discussion

The results showed a high-detection performance for initial contacts (ICs) (recall: 98%, precision: 96%) and final contacts (FCs) (recall: 99%, precision: 94%) and a maximum median time error of -0.02 s for ICs and 0.03 s for FCs. Subsequently derived temporal gait parameters were in good agreement with a pressure insoles-based reference with a maximum mean difference of 0.07, -0.07, and <0.01 s for stance, swing, and stride time, respectively. Thus, the DL algorithm is considered successful in detecting gait events in ecologically valid environments across different mobility-limiting diseases.

Overground Robotic Exoskeleton Training for Patients With Stroke on Walking-Related Outcomes: A Systematic Review and Meta-analysis of Randomized Controlled Trials

OBJECTIVE

This review aims to evaluate the effectiveness of solely overground robotic exoskeleton (RE) training or overground RE training with conventional rehabilitation in improving walking ability, speed, and endurance among patients with stroke.

DATA SOURCES

Nine databases, 5 trial registries, gray literature, specified journals, and reference lists from inception until December 27, 2021.

STUDY SELECTION

Randomized controlled trials adopting overground robotic exoskeleton training for patients with any phases of stroke on walking-related outcomes were included.

DATA EXTRACTION

Two independent reviewers extracted items and performed risk of bias using the Cochrane Risk of Bias tool 1 and certainty of evidence using the Grades of Recommendation Assessment, Development, and Evaluation.

DATA SYNTHESIS

Twenty trials involving 758 participants across 11 countries were included in this review. The overall effect of overground robotic exoskeletons on walking ability at postintervention (d=0.21; 95% confidence interval [CI], 0.01, 0.42; Z=2.02; P=.04) and follow-up (d=0.37; 95% CI, 0.03, 0.71; Z=2.12; P=.03) and walking speed at postintervention (d=0.23; 95% CI, 0.01, 0.46; Z=2.01; P=.04) showed significant improvement compared with conventional rehabilitation. Subgroup analyses suggested that RE training should combine with conventional rehabilitation. A preferable gait training regime is <4 times per week over ≥6 weeks for ≤30 minutes per session among patients with chronic stroke and ambulatory status of independent walkers before training. Meta-regression did not identify any effect of the covariates on the treatment effect. The majority of randomized controlled trials had small sample sizes, and the certainty of the evidence was very low.

CONCLUSION

Overground RE training may have a beneficial effect on walking ability and walking speed to complement conventional rehabilitation. Further large-scale and long-term, high-quality trials are recommended to enhance the quality of overground RE training and confirm its sustainability.

A comparison of three-dimensional kinematics between markerless and marker-based motion capture in overground gait

Vision-based methods using RGB inputs for human pose estimation have grown in recent years but have undergone limited testing in clinical and biomechanics research areas like gait analysis. The purpose of the present study was to compare lower extremity kinematics during overground gait between a traditional marker-based approach and a commercial multi-view markerless system in a sample of subjects including young adults, older adults, and adults diagnosed with Parkinson's disease. A convenience sample of 35 adults between the age of 18-85 years were included in this study, yielding a total of 114 trials and 228 gait cycles that were compared between systems. A total of 30 time normalized waveforms, including three-dimensional joint centers, segment angles, and joint angles were compared between systems using root mean-squared error (RMSE), range of motion difference (ΔROM), Pearson correlation coefficients (r), and interclass correlation coefficients (ICC). RMSEs for joint center positions were less than 28 mm in all joints with correlations indicating good to excellent agreement. RMSEs for segment and joint angles were in range of previous results, with highest agreement between systems in the sagittal plane. ΔROM differences were within reference values that characterize clinical groups like Parkinson's disease, stroke, or knee osteoarthritis. Further improvements in pelvis tracking, markerless keypoint model definitions, and standardization of comparison study protocols are needed. Nevertheless, markerless solutions seem promising toward unrestricted motion analysis in biomechanics research and clinical settings.

Spatiotemporal gait parameter fluctuations in older adults affected by mild cognitive impairment: comparisons among three cognitive dual-task tests

BACKGROUNDS

Gait disorder is associated with cognitive functional impairment, and this disturbance is more pronouncedly when performing additional cognitive tasks. Our study aimed to characterize gait disorders in mild cognitive impairment (MCI) under three dual tasks and determine the association between gait performance and cognitive function.

METHODS

A total of 260 participants were enrolled in this cross-sectional study and divided into MCI and cognitively normal control. Spatiotemporal and kinematic gait parameters (31 items) in single task and three dual tasks (serial 100-7, naming animals and words recall) were measured using a wearable sensor. Baseline characteristics of the two groups were balanced using propensity score matching. Important gait features were filtered using random forest method and LASSO regression and further described using logistic analysis.

RESULTS

After matching, 106 participants with MCI and 106 normal controls were recruited. Top 5 gait features in random forest and 4 ~ 6 important features in LASSO regression were selected. Robust variables associating with cognitive function were temporal gait parameters. Participants with MCI exhibited decreased swing time and terminal swing, increased mid stance and variability of stride length compared with normal control. Subjects walked slower when performing an extra dual cognitive task. In the three dual tasks, words recall test exhibited more pronounced impact on gait regularity, velocity, and dual task cost than the other two cognitive tests.

CONCLUSION

Gait assessment under dual task conditions, particularly in words recall test, using portable sensors could be useful as a complementary strategy for early detection of MCI.

Conventional physical therapy combined with extracorporeal shock wave leads to positive effects on spasticity in stroke survivors: a prospective observational study

The study aimed to evaluate the effectiveness of radial extracorporeal shock wave therapy (rESWT) and conventional physical therapy (CPT) protocol on the gait pattern in stroke survivors through a new gait analysis technology. Fifteen (n=15) stroke survivors took part in this prospective, observational study and were assessed clinically and through an instrumented treadmill before and after rESWT and CPT. Spasticity grade 95% CI 0.93 (0.79 +/- 1.08), pain intensity 95% CI 1.60 (1.19 +/- 2.01), and clonus score decreased significantly 95% CI 1.13 (0.72 +/- 1.54). The sensorimotor function 95% CI -2.53 (-3.42 +/- 1.65), balance 95% CI -5.67 (-6.64 +/- - 4.69), and gait parameters were enhanced at the end of the program. Step length 95% CI -3.47 (-6.48 +/- 0.46) and step cycle were improved 95% CI -0.09 (-0.17 +/- -0.01), and hip 95% CI -3.90 (-6.92 +/- -0.88), knee 95% CI -2.08 (-3.84 +/- -0.32) and ankle flexion-extension 95% CI -2.08 (-6.64 +/- -4.69) were augmented. Adding the quantitative analysis to the clinical assessment, we gained easy access to track progress and obtained an individualized therapeutic approach for stroke survivors.

Immediate Effect of Ankle Exoskeleton on Spatiotemporal Parameters and Center of Pressure Trajectory After Stroke

Gait impairments is a common condition in post-stroke subjects. We recently presented a wearable ankle exoskeleton called G-Exos, which showed that the device assisted in the ankle's dorsiflexion and inversion/reversion movements. The aim of the current pilot study was to explore spatiotemporal gait parameters and center of pressure trajectories associated with the use of the G-Exos in stroke participants. Three post-stroke subjects (52-63 years, 2 female/1 male) walked 160-meter using the G-Exos on the affected limb, on a protocol divided into 4 blocks of 40-meters: (I) without the exoskeleton, (II) with systems hybrid system, (III) active only and (IV) passive only. The results showed that the use of the exoskeleton improved swing and stance phases on both limbs, reduced stride width on the paretic limb, increased stance COP distances, and made single support COP distances more similar between the paretic and non-paretic limb. This suggests that all G-Exos systems contributed to improving body weight bearing on the paretic limb and symmetry in the gait cycle.

Assistive Technology Involving Postural Control and Gait Performance for Adults with Stroke: A Systematic Review and Meta-Analysis

This study aimed to comprehensively summarize assistive technology devices for postural control and gait performance in stroke patients. In the study, we searched for randomized controlled trials (RCTs) published until 31 December 2022 in four electrical databases. The most frequently applied assistive technology devices involving postural stability and gait function for stroke patients were robot-assistive technology devices. Out of 1065 initially retrieved citations that met the inclusion criteria, 30 RCTs (12 studies for subacute patients and 18 studies for chronic patients) were included in this review based on eligibility criteria. The meta-analysis included ten RCTs (five studies for subacute patients and five for chronic patients) based on the inclusion criteria of the data analysis. After analyzing, the variables, only two parameters, the Berg balance scale (BBS) and the functional ambulation category (FAC), which had relevant data from at least three studies measuring postural control and gait function, were selected for the meta-analysis. The meta-analysis revealed significant differences in the experimental group compared to the control group for BBS in both subacute and chronic stroke patients and for the FAC in chronic stroke patients. Robot-assistive training was found to be superior to regular therapy in improving postural stability for subacute and chronic stroke patients but not gait function. This review suggests that robot-assistive technology devices should be considered in rehabilitative approaches for postural stability and gait function for subacute and chronic stroke patients.

Adaptive Control Method for Gait Detection and Classification Devices with Inertial Measurement Unit

Cueing and feedback training can be effective in maintaining or improving gait in individuals with Parkinson's disease. We previously designed a rehabilitation assist device that can detect and classify a user's gait at only the swing phase of the gait cycle, for the ease of data processing. In this study, we analyzed the impact of various factors in a gait detection algorithm on the gait detection and classification rate (GDCR). We collected acceleration and angular velocity data from 25 participants (1 male and 24 females with an average age of 62 ± 6 years) using our device and analyzed the data using statistical methods. Based on these results, we developed an adaptive GDCR control algorithm using several equations and functions. We tested the algorithm under various virtual exercise scenarios using two control methods, based on acceleration and angular velocity, and found that the acceleration threshold was more effective in controlling the GDCR (average Spearman correlation -0.9996, p < 0.001) than the gyroscopic threshold. Our adaptive control algorithm was more effective in maintaining the target GDCR than the other algorithms (p < 0.001) with an average error of 0.10, while other tested methods showed average errors of 0.16 and 0.28. This algorithm has good scalability and can be adapted for future gait detection and classification applications.

Convergent Validity of the Timed Walking Tests with Functional Ambulatory Category in Subacute Stroke

Determining the walking ability of post-stroke patients is crucial for the design of rehabilitation programs and the correct functional information to give to patients and their caregivers at their return home after a neurorehabilitation program. We aimed to assess the convergent validity of three different walking tests: the Functional Ambulation Category (FAC) test, the 10-m walking test (10MeWT) and the 6-minute walking test (6MWT). Eighty walking participants with stroke (34 F, age 64.54 ± 13.02 years) were classified according to the FAC score. Gait speed evaluation was performed with 10MeWT and 6MWT. The cut-off values for FAC and walking tests were calculated using a receiver-operating characteristic (ROC) curve. Area under the curve (AUC) and Youden's index were used to find the cut-off value. Statistical differences were found in all FAC subgroups with respect to walking speed on short and long distances, and in the Rivermead Mobility Index and Barthel Index. Mid-level precision (AUC > 0.7; p < 0.05) was detected in the walking speed with respect to FAC score (III vs. IV and IV vs. V). The confusion matrix and the accuracy analysis showed that the most sensitive test was the 10MeWT, with cut-off values of 0.59 m/s and 1.02 m/s. Walking speed cut-offs of 0.59 and 1.02 m/s were assessed with the 10MeWT and can be used in FAC classification in patients with subacute stroke between the subgroups able to walk with supervision and independently on uniform and non-uniform surfaces. Moreover, the overlapping walking speed registered with the two tests, the 10MeWT showed a better accuracy to drive FAC classification.

Review of adaptive control for stroke lower limb exoskeleton rehabilitation robot based on motion intention recognition

Stroke is a significant cause of disability worldwide, and stroke survivors often experience severe motor impairments. Lower limb rehabilitation exoskeleton robots provide support and balance for stroke survivors and assist them in performing rehabilitation training tasks, which can effectively improve their quality of life during the later stages of stroke recovery. Lower limb rehabilitation exoskeleton robots have become a hot topic in rehabilitation therapy research. This review introduces traditional rehabilitation assessment methods, explores the possibility of lower limb exoskeleton robots combining sensors and electrophysiological signals to assess stroke survivors' rehabilitation objectively, summarizes standard human-robot coupling models of lower limb rehabilitation exoskeleton robots in recent years, and critically introduces adaptive control models based on motion intent recognition for lower limb exoskeleton robots. This provides new design ideas for the future combination of lower limb rehabilitation exoskeleton robots with rehabilitation assessment, motion assistance, rehabilitation treatment, and adaptive control, making the rehabilitation assessment process more objective and addressing the shortage of rehabilitation therapists to some extent. Finally, the article discusses the current limitations of adaptive control of lower limb rehabilitation exoskeleton robots for stroke survivors and proposes new research directions.

Gait Analysis in Neurorehabilitation: From Research to Clinical Practice

When brain damage occurs, gait and balance are often impaired. Evaluation of the gait cycle, therefore, has a pivotal role during the rehabilitation path of subjects who suffer from neurological disorders. Gait analysis can be performed through laboratory systems, non-wearable sensors (NWS), and/or wearable sensors (WS). Using these tools, physiotherapists and neurologists have more objective measures of motion function and can plan tailored and specific gait and balance training early to achieve better outcomes and improve patients' quality of life. However, most of these innovative tools are used for research purposes (especially the laboratory systems and NWS), although they deserve more attention in the rehabilitation field, considering their potential in improving clinical practice. In this narrative review, we aimed to summarize the most used gait analysis systems in neurological patients, shedding some light on their clinical value and implications for neurorehabilitation practice.

[Clinical value of instrumental gait analysis]

BACKGROUND

Instrumental gait analysis is becoming an established addition to conventional diagnostic methods for the clinical assessment of complex movement disorders. It can provide objective and high resolution motion data and contains information that is not observable with conventional clinical methods, such as muscle activation during gait.

UTILISATION

Instrumental gait analysis can add observer independent parameters to the treatment planning of individuals as well as provide insights into pathomechanisms with clinical research studies. Limiting factors for the use of gait analysis technology are currently the time and personnel expenditures for measurements and data processing, as well as the extensive amount of training time required for data interpretation. This article illustrates the clinical value of instrumental gait analysis and specifies its synergies with conventional diagnostic methods.

The contribution of walking speed versus recent stroke to temporospatial gait variability

BACKGROUND

Inconsistent results have been reported for temporospatial gait variability after stroke. Given the large differences in gait speed across stroke subjects and relative to healthy controls, it is not clear which changes in temporospatial gait variability can be ascribed to the walking speed during gait evaluation versus the consequences of stroke.

RESEARCH QUESTIONS

Does temporospatial gait variability differ between (1) stroke subjects grouped in clinically-relevant functional ambulation classes, (2) the paretic and non-paretic legs within each class, and (3) stroke and healthy subjects after controlling for gait speed?

METHODS

Stroke subjects were evaluated at their comfortable speed < 2 months post-onset and classified into the household (<40 cm/s, n = 38), limited-community (40-80 cm/s, n = 35), and full-community (>80 cm/s, n = 14) walkers. Coefficients of variation (CVs) for paretic and non-paretic stance, initial double-support, and single-support times, step length, step cadence, and step width were compared across the stroke ambulation classes and between the two legs. For the parameters with significantly different CVs between stroke subjects and 33 age-matched controls walking at very-slow and free speeds, a 1-way ANCOVA was used with the gait speed as a covariate.

RESULTS

For most step parameters, CVs were greater in slower stroke ambulation classes except for the smaller step width CV. The differences between the paretic and non-paretic legs emerged in slower walkers only. After controlling for the gait speed, CVs of stroke subjects no longer significantly differed from controls walking at very-slow speed. With controls walking at free speed, however, CVs for the paretic and non-paretic single-support times and the non-paretic step time remained significantly different.

SIGNIFICANCE

Gait is more variable at slower speeds both in stroke subjects and healthy controls. After accounting for the free gait speed, the increased variability of only a few temporal parameters may be attributed to a recent stroke.

Effectiveness of the Pelvic Clock and Static Bicycle Exercises on Wisconsin Gait Scale and Trunk Impairment Scale in Chronic Ambulatory Hemiplegic Patients: A Single Group Pre-Post Design

BACKGROUND

Most Hemiplegic patients achieve ambulatory function during the sub-acute stage of stroke. Though ambulatory, they still perform an unpleasant awkward gait with remarkable compensations requiring more energy expenditure. Fatigue arises at an early duration as a result of increased energy expenditure. The walking pattern becomes circumduction, featured by asymmetry with an extensor synergy of the lower limb. Each step is rotated away from the body then towards the body, forming a semicircle. This leads to changes in various parameters of gait (spatiotemporal, kinematic, and kinetic) in hemiparetic patients.

PURPOSE

Many studies reveal the effectiveness of various therapeutic techniques in managing hemiplegic circumduction gait. Pelvic clock exercises aid in improving pelvic rotation components and cause dissociation in impaired pelvic mobility due to spasticity. A static bicycle helps in enhancing proper control between the hamstrings and quadriceps. It also helps in improving knee flexion range. As the patient places the foot in the cycle's petals, it helps to enhance dorsiflexion and eversion functions as well. As the lower body is exercised, there could be relative changes in the upper body, i.e., the trunk. Thus, this study aimed to determine the changes in gait functions and trunk performance of chronic ambulatory hemiplegic patients in response to the above therapies for four weeks.

METHOD

Twenty-five subjects (post-stroke duration (2.8 ± 0.6) years) who could walk 10 m independently without assistance or support of aid participated in a pelvic clock and static bicycle exercise intervention. The session duration was 30 min a day, and therapy was delivered six days a week and continued for four weeks. The entire program was carried out in an outpatient neurorehabilitation center.

RESULTS

After the intervention with pelvic clock and static bicycle exercises, there was a remarkable change in gait and trunk functions in chronic hemiplegic patients.

CONCLUSION

The exercises comprising pelvic clock and static bicycle showed positive differences in gait and trunk functions in chronic stage hemiplegic patients. Later, randomized controlled studies involving larger sample sizes, advanced activation techniques, and increased intervention duration will explore in-depth information on their effectiveness and clinical significance.

Reliability and Validity of a Wearable Sensing System and Online Gait Analysis Report in Persons after Stroke

The restoration of gait and mobility after stroke is an important and challenging therapy goal due to the complexity of the potentially impaired functions. As a result, precise and clinically feasible assessment methods are required for personalized gait rehabilitation after stroke. The aim of this study is to investigate the reliability and validity of a sensor-based gait analysis system in stroke survivors with different severities of gait deficits. For this purpose, 28 chronic stroke survivors (9 women, ages: 62.04 ± 11.68 years) with mild to moderate walking impairments performed a set of ambulatory assessments (3× 10MWT, 1× 6MWT per session) twice while being equipped with a sensor suit. The derived gait reports provided information about speed, step length, step width, swing and stance phases, as well as joint angles of the hip, knee, and ankle, which we analyzed for test-retest reliability and hypothesis testing. Further, test-retest reliability resulted in a mean ICC of 0.78 (range: 0.46-0.88) for walking 10 m and a mean ICC of 0.90 (range: 0.63-0.99) for walking 6 min. Additionally, all gait parameters showed moderate-to-strong correlations with clinical scales reflecting lower limb function. These results support the applicability of this sensor-based gait analysis system for individuals with stroke-related walking impairments.

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

Background

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.

Research objective

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.

Methods

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.

Results and significance

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.

Immersive Virtual Reality during Robot-Assisted Gait Training: Validation of a New Device in Stroke Rehabilitation

Background and objective: Duration of rehabilitation and active participation are crucial for gait rehabilitation in the early stage after stroke onset. Virtual reality (VR) is an innovative tool providing engaging and playful environments that could promote intrinsic motivation and higher active participation for non-ambulatory stroke patients when combined with robot-assisted gait training (RAGT). We have developed a new, fully immersive VR application for RAGT, which can be used with a head-mounted display and wearable sensors providing real-time gait motion in the virtual environment. The aim of this study was to validate the use of this new device and assess the onset of cybersickness in healthy participants before testing the device in stroke patients. Materials and Methods: Thirty-seven healthy participants were included and performed two sessions of RAGT using a fully immersive VR device. They physically walked with the Gait Trainer for 20 min in a virtual forest environment. The occurrence of cybersickness, sense of presence, and usability of the device were assessed with three questionnaires: the Simulator Sickness Questionnaire (SSQ), the Presence Questionnaire (PQ), and the System Usability Scale (SUS). Results: All of the participants completed both sessions. Most of the participants (78.4%) had no significant adverse effects (SSQ < 5). The sense of presence in the virtual environment was particularly high (106.42 ± 9.46). Participants reported good usability of the device (86.08 ± 7.54). Conclusions: This study demonstrated the usability of our fully immersive VR device for gait rehabilitation and did not lead to cybersickness. Future studies should evaluate the same parameters and the effectiveness of this device with non-ambulatory stroke patients.

A novel gait analysis system for detecting abnormal hemiparetic gait patterns during robot-assisted gait training: A criterion validity study among healthy adults

INTRODUCTION

Robot-assisted gait training has been reported to improve gait in individuals with hemiparetic stroke. Ideally, the gait training program should be customized based on individuals' gait characteristics and longitudinal changes. However, a gait robot that uses gait characteristics to provide individually tailored gait training has not been proposed. The new gait training robot, "Welwalk WW-2000," permits modification of various parameters, such as time and load of mechanical assistance for a patient's paralyzed leg. The robot is equipped with sensors and a markerless motion capture system to detect abnormal hemiparetic gait patterns during robot-assisted gait training. Thus, it can provide individually tailored gait training. This study aimed to investigate the criterion validity of the gait analysis system in the Welwalk WW-2000 in healthy adults.

MATERIALS AND METHODS

Twelve healthy participants simulated nine abnormal gait patterns that were often manifested in individuals with hemiparetic stroke while wearing the robot. Each participant was instructed to perform a total of 36 gait trials, with four levels of severity for each abnormal gait pattern. Fifteen strides for each gait trial were recorded using the markerless motion capture system in the Welwalk WW-2000 and a marker-based three-dimensional (3D) motion analysis system. The abnormal gait pattern index was then calculated for each stride from both systems. The correlation of the index values between the two methods was evaluated using Spearman's rank correlation coefficients for each gait pattern in each participant.

RESULTS

Using the participants' index values for each abnormal gait pattern obtained using the two motion analysis methods, the median Spearman's rank correlation coefficients ranged from 0.68 to 0.93, which corresponded to moderate to very high correlation.

CONCLUSION

The gait analysis system in the Welwalk WW-2000 for real-time detection of abnormal gait patterns during robot-assisted gait training was suggested to be a valid method for assessing gait characteristics in individuals with hemiparetic stroke.

CLINICAL TRIAL REGISTRATION

[https://jrct.niph.go.jp], identifier [jRCT 042190109].

Effects of kinesio taping therapy on gait and surface electromyography in stroke patients with hemiplegia

Background: The application of Kinesio Taping (KT) on the lower extremity of stroke patients can improve the quality of somatosensory information by activating lower extremity muscles involved in postural control. Gait analysis and surface electromyography (SEMG) are valuable in assessing the motor ability of the lower extremities.

Objective: This study aimed to investigate the effects of KT therapy on gait and SEMG in stroke patients with hemiplegia.

Methods: Twenty-one stroke patients were included in the study. KT was applied to the lower extremities of the hemiplegic side. Quantitative gait parameters were measured by a gait analysis system (IDEEA, by MiniSun, United States) and activation of the lower extremity muscles were evaluated by the SEMG (Trigno™ Wireless Systems, Delsys Inc., United States) before and after taping. Step length, stride length, pulling acceleration, swing power, ground impact, and energy expenditure were used to evaluate when patients walk as usual. SEMG signals were collected from the anterior bilateral tibialis (TA) and the lateral gastrocnemius (LG). The root mean square (RMS) value was used to assess muscle activity. SEMG signals were examined before and after KT treatment in three different locomotor conditions of the patients: walking at a natural speed, walking with a weight of 5 kg, dual-tasking walking (walking + calculation task) while carrying a weight of 5 kg. The calculation task was to ask the patients to calculate the result of subtracting 7 from 100 and continuing to subtract 7 from the resulting numbers. Comparisons between two normally distributed samples (before and after KT treatment) were evaluated using the two-tailed, paired Student’s t-test.

Results: Stride length (0.89 ± 0.19 vs. 0.96 ± 0.23; p = 0.029), pulling acceleration (0.40 ± 0.21 vs. 1.11 ± 0.74; p = 0.005), and swing power (0.42 ± 0.24 vs. 1.14 ± 0.72; p = 0.004) improved in the hemiplegia side after KT treatment. The RMS value of TA SEMG signals in the limbs on the hemiplegia side decreased after KT treatment during dual-tasking walking carrying a weight of 5 kg (3.65 ± 1.31 vs. 2.93 ± 0.95; p = 0.030).

Conclusion: KT treatment is effective in altering gait and SEMG characteristics in stroke patients with hemiplegia.

Biomechanics beyond the lab: Remote technology for osteoarthritis patient data-A scoping review

The objective of this project is to produce a review of available and validated technologies suitable for gathering biomechanical and functional research data in patients with osteoarthritis (OA), outside of a traditionally fixed laboratory setting. A scoping review was conducted using defined search terms across three databases (Scopus, Ovid MEDLINE, and PEDro), and additional sources of information from grey literature were added. One author carried out an initial title and abstract review, and two authors independently completed full-text screenings. Out of the total 5,164 articles screened, 75 were included based on inclusion criteria covering a range of technologies in articles published from 2015. These were subsequently categorised by technology type, parameters measured, level of remoteness, and a separate table of commercially available systems. The results concluded that from the growing number of available and emerging technologies, there is a well-established range in use and further in development. Of particular note are the wide-ranging available inertial measurement unit systems and the breadth of technology available to record basic gait spatiotemporal measures with highly beneficial and informative functional outputs. With the majority of technologies categorised as suitable for part-remote use, the number of technologies that are usable and fully remote is rare and they usually employ smartphone software to enable this. With many systems being developed for camera-based technology, such technology is likely to increase in usability and availability as computational models are being developed with increased sensitivities to recognise patterns of movement, enabling data collection in the wider environment and reducing costs and creating a better understanding of OA patient biomechanical and functional movement data.

Accuracy of gait and posture classification using movement sensors in individuals with mobility impairment after stroke

Background: Stroke leads to motor impairment which reduces physical activity, negatively affects social participation, and increases the risk of secondary cardiovascular events. Continuous monitoring of physical activity with motion sensors is promising to allow the prescription of tailored treatments in a timely manner. Accurate classification of gait activities and body posture is necessary to extract actionable information for outcome measures from unstructured motion data. We here develop and validate a solution for various sensor configurations specifically for a stroke population.

Methods: Video and movement sensor data (locations: wrists, ankles, and chest) were collected from fourteen stroke survivors with motor impairment who performed real-life activities in their home environment. Video data were labeled for five classes of gait and body postures and three classes of transitions that served as ground truth. We trained support vector machine (SVM), logistic regression (LR), and k-nearest neighbor (kNN) models to identify gait bouts only or gait and posture. Model performance was assessed by the nested leave-one-subject-out protocol and compared across five different sensor placement configurations.

Results: Our method achieved very good performance when predicting real-life gait versus non-gait (Gait classification) with an accuracy between 85% and 93% across sensor configurations, using SVM and LR modeling. On the much more challenging task of discriminating between the body postures lying, sitting, and standing as well as walking, and stair ascent/descent (Gait and postures classification), our method achieves accuracies between 80% and 86% with at least one ankle and wrist sensor attached unilaterally. The Gait and postures classification performance between SVM and LR was equivalent but superior to kNN.

Conclusion: This work presents a comparison of performance when classifying Gait and body postures in post-stroke individuals with different sensor configurations, which provide options for subsequent outcome evaluation. We achieved accurate classification of gait and postures performed in a real-life setting by individuals with a wide range of motor impairments due to stroke. This validated classifier will hopefully prove a useful resource to researchers and clinicians in the increasingly important field of digital health in the form of remote movement monitoring using motion sensors.

Masticatory function of stroke patients: A systematic review with meta-analysis

PURPOSE

The present systematic review and meta-analysis aimed to summarise the current information on the masticatory function of stroke patients.

METHODS

Four electronic databases (Medline, Embase, CINAHL and Web of Science) were searched for relevant observational studies and clinical trials (up to Jun 2021) on the masticatory function of stroke patients. Two reviewers independently performed study selection and quality assessments (using JBI Critical Appraisal Checklist). Meta-analysis was conducted for the comparison of bite force and masticatory performance using standardised mean difference (SMD). Of the 3837 records identified, nine studies, corresponding to 11 papers and 302 participants, were included in the analysis.

RESULTS

The maximum bite force of stroke patients was significantly lower than that of the healthy individuals (SMD -0.52, 95% CI: -0.95 to -0.08, P = .02). There was no significant difference between the ipsi-lesional and the contra-lesional sides of the same stroke patient (SMD 0.13, 95% CI: -0.14 to 0.39, P = .34). Stroke patients had lower masticatory performance than healthy people (SMD -0.97, 95% CI: 0.57 to 1.37, P < .00001), and the contra-lesional side was worse than the ipsi-lesional side. Electromyographic analysis indicated that muscle activation of stroke patients was poorer than the healthy individuals, and stroke patients seem to exhibit dysfunction in the recruiting and firing of motor units.

CONCLUSIONS

Stroke patients have lower maximum bite force and masticatory performance than healthy people, with masticatory performance being the most affected.

Update on an Observational, Clinically Useful Gait Coordination Measure: The Gait Assessment and Intervention Tool (G.A.I.T.)

With discoveries of brain and spinal cord mechanisms that control gait, and disrupt gait coordination after disease or injury, and that respond to motor training for those with neurological disease or injury, there is greater ability to construct more efficacious gait coordination training paradigms. Therefore, it is critical in these contemporary times, to use the most precise, sensitive, homogeneous (i.e., domain-specific), and comprehensive measures available to assess gait coordination, dyscoordination, and changes in response to treatment. Gait coordination is defined as the simultaneous performance of the spatial and temporal components of gait. While kinematic gait measures are considered the gold standard, the equipment and analysis cost and time preclude their use in most clinics. At the same time, observational gait coordination scales can be considered. Two independent groups identified the Gait Assessment and Intervention Tool (G.A.I.T.) as the most suitable scale for both research and clinical practice, compared to other observational gait scales, since it has been proven to be valid, reliable, sensitive to change, homogeneous, and comprehensive. The G.A.I.T. has shown strong reliability, validity, and sensitive precision for those with stroke or multiple sclerosis (MS). The G.A.I.T. has been translated into four languages (English, Spanish, Taiwanese, and Portuguese (translation is complete, but not yet published)), and is in use in at least 10 countries. As a contribution to the field, and in view of the evidence for continued usefulness and international use for the G.A.I.T. measure, we have provided this update, as well as an open access copy of the measure for use in clinical practice and research, as well as directions for administering the G.A.I.T.

A Systematic Review of Fall Risk Factors in Stroke Survivors: Towards Improved Assessment Platforms and Protocols

Background/Purpose: To prevent falling, a common incident with debilitating health consequences among stroke survivors, it is important to identify significant fall risk factors (FRFs) towards developing and implementing predictive and preventive strategies and guidelines. This review provides a systematic approach for identifying the relevant FRFs and shedding light on future directions of research.

Methods: A systematic search was conducted in 5 popular research databases. Studies investigating the FRFs in the stroke community were evaluated to identify the commonality and trend of FRFs in the relevant literature.

Results: twenty-seven relevant articles were reviewed and analyzed spanning the years 1995–2020. The results confirmed that the most common FRFs were age (21/27, i.e., considered in 21 out of 27 studies), gender (21/27), motion-related measures (19/27), motor function/impairment (17/27), balance-related measures (16/27), and cognitive impairment (11/27). Among these factors, motion-related measures had the highest rate of significance (i.e., 84% or 16/19). Due to the high commonality of balance/motion-related measures, we further analyzed these factors. We identified a trend reflecting that subjective tools are increasingly being replaced by simple objective measures (e.g., 10-m walk), and most recently by quantitative measures based on detailed motion analysis.

Conclusion: There remains a gap for a standardized systematic approach for selecting relevant FRFs in stroke fall risk literature. This study provides an evidence-based methodology to identify the relevant risk factors, as well as their commonalities and trends. Three significant areas for future research on post stroke fall risk assessment have been identified: 1) further exploration the efficacy of quantitative detailed motion analysis; 2) implementation of inertial measurement units as a cost-effective and accessible tool in clinics and beyond; and 3) investigation of the capability of cognitive-motor dual-task paradigms and their association with FRFs.

Synchronized Cyclograms to Assess Inter-Limb Symmetry during Gait in Post-Stroke Patients

The aim of this study was to assess the inter-limb symmetry during gait in post-stroke patients using the synchronized cyclograms technique. In total, 41 individuals with stroke (21 left and 20 right hemiplegic patients; age: 57.9 ± 12.8 years; time stroke event 4.6 ± 1.8 years) and 48 age-, sex-, and height-matched individuals (control group: CG; age: 54.4 ± 12.5 years) were assessed via 3D gait analysis. Raw kinematic data were processed to compute spatio-temporal parameters (speed, stride length, cadence, stance, swing, and double support phases duration) and angle–angle diagrams (synchronized cyclograms), which were characterized in terms of area, orientation, and trend symmetry indices. The results reveal that all spatio-temporal parameters are characterized by abnormal values, with reduced speed, stride length, cadence, and swing phase duration and increased stance and double support phases duration. With respect to inter-limb symmetry, higher values were found in post-stroke individuals for all the considered parameters as patients generally exhibited a cyclogram characterized by larger areas, higher orientation, and trend symmetry parameters with respect to CG. The described alterations of gait asymmetry are important from a clinical point of view as the achievement of symmetry in gait represents a crucial objective in the rehabilitation of hemiplegic people.

A Deep Learning Approach for Gait Event Detection from a Single Shank-Worn IMU: Validation in Healthy and Neurological Cohorts

Many algorithms use 3D accelerometer and/or gyroscope data from inertial measurement unit (IMU) sensors to detect gait events (i.e., initial and final foot contact). However, these algorithms often require knowledge about sensor orientation and use empirically derived thresholds. As alignment cannot always be controlled for in ambulatory assessments, methods are needed that require little knowledge on sensor location and orientation, e.g., a convolutional neural network-based deep learning model. Therefore, 157 participants from healthy and neurologically diseased cohorts walked 5 m distances at slow, preferred, and fast walking speed, while data were collected from IMUs on the left and right ankle and shank. Gait events were detected and stride parameters were extracted using a deep learning model and an optoelectronic motion capture (OMC) system for reference. The deep learning model consisted of convolutional layers using dilated convolutions, followed by two independent fully connected layers to predict whether a time step corresponded to the event of initial contact (IC) or final contact (FC), respectively. Results showed a high detection rate for both initial and final contacts across sensor locations (recall ≥92%, precision ≥97%). Time agreement was excellent as witnessed from the median time error (0.005 s) and corresponding inter-quartile range (0.020 s). The extracted stride-specific parameters were in good agreement with parameters derived from the OMC system (maximum mean difference 0.003 s and corresponding maximum limits of agreement (−0.049 s, 0.051 s) for a 95% confidence level). Thus, the deep learning approach was considered a valid approach for detecting gait events and extracting stride-specific parameters with little knowledge on exact IMU location and orientation in conditions with and without walking pathologies due to neurological diseases.

Pathological gait clustering in post-stroke patients using motion capture data

BACKGROUND

Analyzing the complex gait patterns of post-stroke patients with lower limb paralysis is essential for rehabilitation.

RESEARCH QUESTION

Is it feasible to use the full joint-level kinematic features extracted from the motion capture data of patients directly to identify the optimal gait types that ensure high classification performance?

METHODS

In this study, kinematic features were extracted from 111 gait cycle data on joint angles, and angular velocities of 36 post-stroke patients were collected eight times over six months using a motion capture system. Simultaneous clustering and classification were applied to determine the optimal gait types for reliable classification performance.

RESULTS

In the given dataset, six optimal gait groups were identified, and the clustering and classification performances were denoted by a silhouette coefficient of 0.1447 and F₁ score of 1.0000, respectively.

SIGNIFICANCE

There is no distinct clinical classification of post-stroke hemiplegic gaits. However, in contrast to previous studies, more optimal gait types with a high classification performance fully utilizing the kinematic features were identified in this study.

Methodological Considerations in Assessing Interlimb Coordination on Poststroke Gait: A Scoping Review of Biomechanical Approaches and Outcomes

Objective: To identify and summarize biomechanical assessment approaches in interlimb coordination on poststroke gait. Introduction: Interlimb coordination involves complex neurophysiological mechanisms that can be expressed through the biomechanical output. The deepening of this concept would have a significant contribution in gait rehabilitation in patients with an asymmetric neurological impairment as poststroke adults. Inclusion criteria: Poststroke adults (>19 years old), with assessment of interlimb coordination during gait, in an open context, according to the Population, Concept, Context framework. Methods: A literature search was performed in PubMed, Web of Science™, Scopus, and gray literature in Google Scholar™, according to the PRISMA-ScR recommendations. Studies written in Portuguese or English language and published between database inception and 14 November 2021 were included. Qualitative studies, conference proceedings, letters, and editorials were excluded. The main conceptual categories were “author/year”, “study design”, “participant’s characteristics”, “walking conditions”, “instruments” and “outcomes”. Results: The search identified 827 potentially relevant studies, with a remaining seven fulfilling the established criteria. Interlimb coordination was assessed during walking in treadmill (n = 3), overground (n = 3) and both (n = 1). The instruments used monitored electromyography (n = 2), kinetics (n = 2), and kinematics (n = 4) to assess spatiotemporal parameters (n = 4), joint kinematics (n = 2), anteroposterior ground reaction forces (n = 2), and electromyography root mean square (n = 2) outcomes. These outcomes were mostly used to analyze symmetry indices or ratios, to calculate propulsive impulse and external mechanical power produced on the CoM, as well as antagonist coactivation. Conclusions: Assessment of interlimb coordination during gait is important for consideration of natural auto-selected overground walking, using kinematic, kinetic, and EMG instruments. These allow for the collection of the main biomechanical outcomes that could contribute to improve better knowledge of interlimb coordination assessment in poststroke patients.

Assessment of Agreement Between a New Application to Compute the Wisconsin Gait Score and 3-Dimensional Gait Analysis, and Reliability of the Application in Stroke Patients

Currently, there are no computerized tools enabling objective interpretation of observational gait assessment based on Wisconsin Gait Scale (WGS), which is a reliable and well-tested tool. The solution envisaged by us may provide a practical tool for assessing gait deviations in patients with hemiparesis after stroke. The present study assessed agreement between a new application software for computerized WGS and 3-dimensional gait analysis (3DGA), and reliability of the application. The study involved 33 individuals with hemiparesis after stroke. The software was developed based on a model designed taking into account components of the WGS and incorporating auxiliary lines passing through the relevant anthropometric points on the patient’s body, as well as measurements of angular values, distances and duration of the specific gait phases, which make it possible to substantiate assessment based on this scale. Series of videos were made to record gait of the qualified patients. After the gait evaluation was carried out using the app, the data were retrieved from the software. The gait assessment was performed separately by three independent examiners who reviewed the video recording using the new app twice (two weeks apart). Additionally, 3DGA was carried out for all the subjects, and the results of the app-aided assessment were compared to those acquired using 3DGA. The findings show statistically significant correlations (p &lt; 0.05) between majority of the WGS items measured using the new app, and the relevant spatiotemporal and kinematic parameters identified by 3DGA. Agreement between the scores reported by the three examiners was high in both measurements, as reflected by Cronbach’s alpha exceeding 0.8. The findings reflect very good intra-observer reliability (as reflected by kappa coefficients from 0.847 to 1) and inter-observer reliability (as reflected by kappa coefficients from 0.634 to 1) of the new application software for computerized WGS. The opportunities offered by the observational gait scale objectified through our new software for computerized WGS result from the fact that the tool provides a useful low-cost and time-effective feedback to monitor ongoing treatments or formulate hypotheses.

Reliability of IMU-Based Gait Assessment in Clinical Stroke Rehabilitation

Background: Gait is often impaired in people after stroke, restricting personal independence and affecting quality of life. During stroke rehabilitation, walking capacity is conventionally assessed by measuring walking distance and speed. Gait features, such as asymmetry and variability, are not routinely determined, but may provide more specific insights into the patient’s walking capacity. Inertial measurement units offer a feasible and promising tool to determine these gait features. Objective: We examined the test–retest reliability of inertial measurement units-based gait features measured in a two-minute walking assessment in people after stroke and while in clinical rehabilitation. Method: Thirty-one people after stroke performed two assessments with a test–retest interval of 24 h. Each assessment consisted of a two-minute walking test on a 14-m walking path. Participants were equipped with three inertial measurement units, placed at both feet and at the low back. In total, 166 gait features were calculated for each assessment, consisting of spatio-temporal (56), frequency (26), complexity (63), and asymmetry (14) features. The reliability was determined using the intraclass correlation coefficient. Additionally, the minimal detectable change and the relative minimal detectable change were computed. Results: Overall, 107 gait features had good–excellent reliability, consisting of 50 spatio-temporal, 8 frequency, 36 complexity, and 13 symmetry features. The relative minimal detectable change of these features ranged between 0.5 and 1.5 standard deviations. Conclusion: Gait can reliably be assessed in people after stroke in clinical stroke rehabilitation using three inertial measurement units.

Machine Learning-Based Classification of Dependence in Ambulation in Stroke Patients Using Smartphone Video Data

The goal of this study was to develop a framework to classify dependence in ambulation by employing a deep model in a 3D convolutional neural network (3D-CNN) using video data recorded by a smartphone during inpatient rehabilitation therapy in stroke patients. Among 2311 video clips, 1218 walk action cases were collected from 206 stroke patients receiving inpatient rehabilitation therapy (63.24 ± 14.36 years old). As ground truth, the dependence in ambulation was assessed and labeled using the functional ambulatory categories (FACs) and Berg balance scale (BBS). The dependent ambulation was defined as a FAC score less than 4 or a BBS score less than 45. We extracted patient-centered video and patient-centered pose of the target from the tracked target’s posture keypoint location information. Then, the extracted patient-centered video was input in the 3D-CNN, and the extracted patient-centered pose was used to measure swing time asymmetry. Finally, we evaluated the classification of dependence in ambulation using video data via fivefold cross-validation. When training the 3D-CNN based on FACs and BBS, the model performed with 86.3% accuracy, 87.4% precision, 94.0% recall, and 90.5% F1 score. When the 3D-CNN based on FACs and BBS was combined with swing time asymmetry, the model exhibited improved performance (88.7% accuracy, 89.1% precision, 95.7% recall, and 92.2% F1 score). The proposed framework for dependence in ambulation can be useful, as it alerts clinicians or caregivers when stroke patients with dependent ambulatory move alone without assistance. In addition, monitoring dependence in ambulation can facilitate the design of individualized rehabilitation strategies for stroke patients with impaired mobility and balance function.

Early Individualized Approach for a Patient with Spasticity of Stroke Origin

The aim of this case report is to present an early individualized rehabilitative plan for a post-stroke patient with limb spasticity given that stroke is a leading cause for disability that involves prolonged hospital stay and neurorehabilitation strategies. The rehabilitation plan consisted of conventional physical therapy and radial extracorporeal shock wave therapy (rESWT), and the results were evaluated through clinical assessment together with an innovative gait analysis system. Two rESWT sessions and conventional physical therapy program decreased spasticity grade and pain intensity, and improved ankle range of motion, balance and gait.