Analysis of Temporospatial Gait Parameters (original) (raw)
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Computational gait analysis using fuzzy logic for everyday clinical purposes – preliminary findings
Bio-Algorithms and Med-Systems, 2017
Background: Proper, early, and exact identification of gait impairments and their causes is regarded as a prerequisite for specific therapy and a useful control tool to assess efficacy of rehabilitation. There is a need for simple tools allowing for quickly detecting general tendencies. Objective: The aim of this paper is to present the outcomes of traditional and fuzzy-based analysis of the outcomes of post-stroke gait reeducation using the NeuroDevelopmental Treatment-Bobath (NDT-Bobath) method. Materials and methods: The research was conducted among 40 adult people: 20 of them after ischemic stroke constituted the study group, and 20 healthy people constituted the reference group. Study group members were treated through 2 weeks (10 therapeutic sessions) using the NDT-Bobath method. Spatio-temporal gait parameters were assessed before and after therapy and compared using novel fuzzy-based assessment tool. Results: Achieved results of rehabilitation, observed as changes of gait pa...
Prediction of gait velocity in ambulatory stroke patients during rehabilitation
Archives of Physical Medicine and Rehabilitation, 1999
Objective: To quantify prediction of gait velocity in ambulatory stroke patients during rehabilitation. Design: Single group (n = 42) at the beginning of rehabilitation (Test 1) and 8 weeks later (Test 2). Setting: Inpatient rehabilitation. Patients: Unilateral first stroke; informed consent; able to walk 10 meters. Measures: Independent variables: Gait velocity at Test 1, age, time from stroke to Test 1, side of lesion, neglect. Dependent variables: Gait velocity at Test 2, gait velocity change. Results: The correlation between initial gait velocity and gait velocity outcome at Test 2 was of moderate strength (13 = .62, p < .05). However, even at its lowest, the standard error of prediction for an individual patient was 9.4m/min, with 95% confidence intervals extending over a range of 36.8mlmin. Age was a weak predictor of gait velocity at Test 2 (1-? =-.lO, p < .05). Gait velocity change was poorly predicted. The only significant correlations were initial gait velocity (u2 = .lO, p <: .05) and age (i = .lO,p < .05). Conclusion: While the prediction of gait velocity at Test 2 was of moderate strength on a group basis, the error surrounding predicted values of gait velocity for a single patient was relatively high, indicating that this simple approach was imprecise on an individual basis. The prediction of gait velocity change was poor. A wide range of change scores was possible for patients, irrespective of their gait velocity score on admission to rehabilitation. 0 1999 by the American Congress of Rehabilitation Medicine and the American Academy of Physical Medicine and Rehabilitation T HE ABILITY TO WALK has been rated as one of the most important goals of rehabilitation by stroke patients.l A noteworthy feature of gait recovery after stroke is the wide range of individual differences between patients in achieved level of gait performance and in change in gait performance in From the School of Phvsiotheraov (Dr. Goldie). School of Psvcholo&al Sciences (Drs. Matyas, Kinsella), and Schdd of Human kosciences
Gait parameters following stroke: A practical assessment
The Journal of Rehabilitation Research and Development
Mechanical methods of quantifying gait are more sensitive to change than is direct clinical inspection. To assess gait parameters and patterns of patients with stroke, and the temporal changes of these parameters, a foot-switch gait analyzer was used to test 49 ambulatory patients with stroke and 24 controls. Patients walked significantly slower than controls, with decreased cadence, increased gait cycle, and increased time in double limb support. Patients' hemiplegic limbs spent more time in swing and stance when compared to controls; their unaffected limbs spent significantly more time in stance and single limb support compared to controls. Patients' hemiplegic side, when compared with the unaffected side, spent less time in stance and more time in swing. A flatfoot pattern was typically noted on the affected side. General gait parameters improved over time, with the largest changes occurring in the first 12 months. However, the percentage of time spent in double and singl...
Quantitative kinematics of gait patterns during the recovery period after stroke
Journal of Stroke and Cerebrovascular Diseases, 1999
Background and Purpose: The purpose of this study was to assess the potential of a new quantitative kinematic analysis for the documentation and evaluation of recovery of gait function after neurological injury. Methods: We assessed the kinematics of gait function in 16 patients with hemiplegia at varying intervals over a 1-year period after a stroke, using a novel method for gait pattern assessment based on principal component analysis. Conventional measures such as gait speed and stride length were also evaluated. Testing started as soon as patients became ambulatory after stroke. Results: Of the 16 patients assessed, 7 showed at least a 50% increase in self-selected gait speed from the first to the last test. The results of the pattern analysis closely mirrored self-selected gait speed at higher speeds, but relative rankings derived from gait speed and the pattern analysis did not match for 6 of the 16 patients. Kinematic pattern analysis suggested that different mechanisms were used to generate changes in gait speed at different speed levels. Conclusion: There is a sizable fraction of the stroke population for whom kinematic gait pattern analysis can provide information that is different from that provided by speed, stride length, and cadence. The kinematic analysis can potentially provide information about the mechanisms of pathological gait.
Frontiers in Neurology, 2021
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.
Medical Science Monitor, 2020
Background: For future development of machine learning tools for gait impairment assessment after stroke, simple observational whole-body clinical scales are required. Current observational scales regard either only leg movement or discrete overall parameters, neglecting dysfunctions in the trunk and arms. The purpose of this study was to introduce a new multiple-cue observational scale, called the stroke mobility score (SMS). Material/Methods: In a group of 131 patients, we developed a 1-page manual involving 6 subscores by Delphi method using the video-based SMS: trunk posture, leg movement of the most affected side, arm movement of the most affected side, walking speed, gait fluency and stability/risk of falling. Six medical raters then validated the SMS on a sample of 60 additional stroke patients. Conventional scales (NIHSS, Timed-Up-And-Go-Test, 10-Meter-Walk-Test, Berg Balance Scale, FIM-Item L, Barthel Index) were also applied. Results: (1) High consistency and excellent inter-rater reliability of the SMS were verified (Cronbach's alpha >0.9). (2) The SMS subscores are non-redundant and reveal much more nuanced whole-body dysfunction details than conventional scores, although evident correlations as e.g. between 10-Meter-Walk-Test and subscore "gait speed" are verified. (3) The analysis of cross-correlations between SMS subscores unveils new functional interrelationships for stroke profiling. Conclusions: The SMS proves to be an easy-to-use, tele-applicable, robust, consistent, reliable, and nuanced functional scale of gait impairments after stroke. Due to its sensitivity to whole-body motion criteria, it is ideally suited for machine learning algorithms and for development of new therapy strategies based on instrumented gait analysis.
Repeatability and variation of quantitative gait data in subgroups of patients with stroke
Gait & Posture, 2008
We aimed to determine the repeatability and variation of quantitative gait data in patients with stroke and to compare the subgroups in terms of gait variability. Time-distance and kinematic characteristics of gait were evaluated in 90 inpatients (30 women) with hemiparesis (mean AE S.D. age 57.7 AE 12.5 years and time since stroke 5.99 AE 6.46 months). Repeatability was adequate to excellent in all stroke subgroups (ICC range 0.48-0.98). Walking velocity was the most repeatable gait parameter after stroke. Variation in step length was significantly higher in women than in men (CV 16% versus 9%, p < 0.05). Slow walkers (walking velocity <0.34 m/s) had a higher variation than fast walkers in step length (CV 12.5% versus 7.5%, p < 0.01), single support time (CV 11.9% versus 6.3%, p < 0.05), peak hip extensions in stance (CV 11.5% versus 3.7%, p < 0.01) and knee flexion in swing (CV 11.8% versus 6.5%, p < 0.05). In our stroke patients, their age, time since injury, lesion characteristics, impaired proprioception or level of motor recovery had no effect on gait variability.
Analysis of gait pattern deviations among chronic post stroke patients
2020
Objective: To determine the abnormalities of different parameters of gait and to determine the association of spasticity with them in chronic post stroke population. Methodology: It was a cross sectional descriptive study conducted over a duration of 3 months in 2017 at Pakistan Institute of Medical Sciences, Islamabad and Railway General Hospital, Rawalpindi and included 104 chronic stroke patients using non probability purposive sampling technique. Patients presenting with both ischemic and hemorrhagic stroke, both genders having age of 25 year to 75 years, patients who had suffered from stroke in past 6 months at time of data collection were included in the study. Those patients who had weakness due to any tumor, Gullian Barre Syndrome, peripheral nerve injury, multiple sclerosis or traumatic brain injury were excluded from the study. Dynamic gait index, Modified Ashworth scale, 10 meter walk test and temporospatial characteristics like, step length, stride length, step width and...
Multivariate Examination of Data From Gait Analysis of Persons With Stroke
Physical Therapy, 1998
Background and Purpose. Gait analyses yield redundant information that often is difficult to interpret. The purpose of this study was to show how principal-component analysis can provide insight into gait data obtained from persons with stroke. Subiects. Twenty male and 11 female adults who were ambulatory were studied (mean age=60.5 years, SD= 11.8, range=24-79; mean time since stroke= 11.4 months, SD=15.4, range=2.0-88.0). Methods. Spatial data were used in a 4segment link-segment model to calculate the kinematic and kinetic variables of gait. Principal components were constructed on the averages for 40 variables. Results. The first principal component was related to speed and accounted for 40.8% of the variance. The second principal component was related to differences between the 2 limbs (symmetry) and accounted for 12.8% of the variance. The third principal component was related to adoption of a postural flexion bias and accounted for 10.2% of the variance. The fourth principal component, which was not interpretable, accounted for 6.8% of the variance. Conclusion and Discussion. The principal-component analysis allowed clustering of related variables and simplified the complex picture presented by the large number of variables resulting from gait analysis. Examination of variables closely related to each principal component yielded insight into the nature of the strategies used in walking and their interrelationships. The method has potential for insight into similarities and differences in gait performances arising from different pathologies and for comparing the progress of individuals with similar pathologies. [Olney SJ, Griffin MP, McBride ID. Multivariate examination of data from gait analysis of persons with stroke. Phys Ther. 1998;78:814-828.1